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Pitkänen, Johanna, Juha Koikkalainen, Tuomas Nieminen, Ivan Marinkovic, Sami Curtze, Gerli Sibolt, Hanna Jokinen, et al. "Evaluating severity of white matter lesions from computed tomography images with convolutional neural network." Neuroradiology 62, no. 10 (April 13, 2020): 1257–63. http://dx.doi.org/10.1007/s00234-020-02410-2.
Повний текст джерелаАнотація:
Abstract Purpose Severity of white matter lesion (WML) is typically evaluated on magnetic resonance images (MRI), yet the more accessible, faster, and less expensive method is computed tomography (CT). Our objective was to study whether WML can be automatically segmented from CT images using a convolutional neural network (CNN). The second aim was to compare CT segmentation with MRI segmentation. Methods The brain images from the Helsinki University Hospital clinical image archive were systematically screened to make CT-MRI image pairs. Selection criteria for the study were that both CT and MRI images were acquired within 6 weeks. In total, 147 image pairs were included. We used CNN to segment WML from CT images. Training and testing of CNN for CT was performed using 10-fold cross-validation, and the segmentation results were compared with the corresponding segmentations from MRI. Results A Pearson correlation of 0.94 was obtained between the automatic WML volumes of MRI and CT segmentations. The average Dice similarity index validating the overlap between CT and FLAIR segmentations was 0.68 for the Fazekas 3 group. Conclusion CNN-based segmentation of CT images may provide a means to evaluate the severity of WML and establish a link between CT WML patterns and the current standard MRI-based visual rating scale.
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Yazdi, Mahsa Badiee, Mohammad Mahdi Khalilzadeh, and Mohsen Foroughipour. "MRI SEGMENTATION BY FUZZY CLUSTERING METHOD BASED ON PRIOR KNOWLEDGE." Biomedical Engineering: Applications, Basis and Communications 28, no. 04 (August 2016): 1650025. http://dx.doi.org/10.4015/s1016237216500253.
Повний текст джерелаАнотація:
Image segmentation is often required as a fundamental stage in medical image processing, particularly during the clinical analysis of magnetic resonance (MR) brain images. Fuzzy c-means (FCM) clustering algorithm is one of the best known and widely used segmentation methods, but this algorithm has some problem for segmenting simulated MRI images to high number of clusters with different noise levels and real images because of spatial complexities. Anatomical segmentation usually requires information derived from the manual segmentations done by experts, prior knowledge can be useful to modify image segmentation methods. In this paper, we propose some methods to modify FCM algorithm using expert manual segmentation as prior knowledge. We developed combination of FCM algorithm and prior knowledge in three ways, in order to improve segmentation of brain MR images with high noise level and spatial complexity. In real images, we had a considerable improvement in similarity index of three classes (white matter, gray matter, CSF) and in simulated images with different noise levels evaluation criteria of white matter and gray matter has improved.
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Wang, Guodong, Jie Xu, Qian Dong, and Zhenkuan Pan. "Active Contour Model Coupling with Higher Order Diffusion for Medical Image Segmentation." International Journal of Biomedical Imaging 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/237648.
Повний текст джерелаАнотація:
Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active contour model can converge to the edge of the image even with the intensity inhomogeneities. Because of the introduction of Laplace information, the difference scheme becomes more difficult. To enhance the efficiency of the segmentation, the fast Split Bregman algorithm is designed for the segmentation implementation. The performance of our method is demonstrated through numerical experiments of some medical image segmentations with intensity inhomogeneities.
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Weishaupt, L. L., T. Vuong, A. Thibodeau-Antonacci, A. Garant, K. S. Singh, C. Miller, A. Martin, and S. Enger. "A121 QUANTIFYING INTER-OBSERVER VARIABILITY IN THE SEGMENTATION OF RECTAL TUMORS IN ENDOSCOPY IMAGES AND ITS EFFECTS ON DEEP LEARNING." Journal of the Canadian Association of Gastroenterology 5, Supplement_1 (February 21, 2022): 140–42. http://dx.doi.org/10.1093/jcag/gwab049.120.
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Abstract Background Tumor delineation in endoscopy images is a crucial part of clinical diagnoses and treatment planning for rectal cancer patients. However, it is challenging to detect and adequately determine the size of tumors in these images, especially for inexperienced clinicians. This motivates the need for a standardized, automated segmentation method. While deep learning has proven to be a powerful tool for medical image segmentation, it requires a large quantity of high-quality annotated training data. Since the annotation of endoscopy images is prone to high inter-observer variability, creating a robust unbiased deep learning model for this task is challenging. Aims To quantify the inter-observer variability in the manual segmentation of tumors in endoscopy images of rectal cancer patients and investigate an automated approach using deep learning. Methods Three gastrointestinal physicians and radiation oncologists (G1, G2, and G3) segmented 2833 endoscopy images into tumor and non-tumor regions. The whole image classifications and the pixelwise classifications into tumor and non-tumor were compared to quantify the inter-observer variability. Each manual annotator is from a different institution. Three different deep learning architectures (FCN32, U-Net, and SegNet) were trained on the binary contours created by G2. This naive approach investigates the effectiveness of neglecting any information about the uncertainty associated with the task of tumor delineation. Finally, segmentations from G2 and the deep learning models’ predictions were compared against ground truth labels from G1 and G3, and accuracy, sensitivity, specificity, precision, and F1 scores were computed for images where both segmentations contained tumors. Results The deep-learning segmentation took less than 1 second, while manual segmentation took approximately 10 seconds per image. There was significant inter-observer variability for the whole-image classifications made by the manual annotators (Figure 1A). The segmentation scores achieved by the deep learning models (SegNet F1:0.80±0.08) were comparable to the inter-observer variability for the pixel-wise image classification (Figure 1B). Conclusions The large inter-observer variability observed in this study indicates a need for an automated segmentation tool for tumors in endoscopy images of rectal cancer patients. While deep learning models trained on a single observer’s labels can segment tumors with an accuracy similar to the inter-observer variability, these models do not accurately reflect the intrinsic uncertainty associated with tumor delineation. In our ongoing studies, we investigate training a model with all observers’ contours to reflect the uncertainty associated with the tumor segmentations. Funding Agencies CIHRNSERC
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Conti, Luis Américo, and Murilo Baptista. "SYNTHETIC APERTURE SONAR IMAGES SEGMENTATION USING DYNAMICAL MODELING ANALYSIS." Revista Brasileira de Geofísica 31, no. 3 (September 1, 2013): 455. http://dx.doi.org/10.22564/rbgf.v31i3.315.
Повний текст джерелаАнотація:
ABSTRACT. Symbolic Models applied to Synthetic Aperture Sonar images are proposed in order to assess the validity and reliability of use of such models and evaluate how effective they can be in terms of image classification and segmentation. We developed an approach for the description of sonar images where the pixels distribution can be transformed into points in the symbolic space in a similar way as symbolic space can encode a trajectory of a dynamical system. One of the main characteristic of approach is that points in the symbolic space are mapped respecting dynamical rules and, as a consequence, it can possible to calculate quantities thatcharacterize the dynamical system, such as Fractal Dimension (D), Shannon Entropy (H) and the amount of information of the image. It also showed potential to classify image sub-patterns based on the textural characteristics of the seabed. The proposed method reached a reasonable degree of success with results compatible with the classical techniques described in literature.Keywords: Synthetic Aperture Sonar, image processing, dynamical models, fractal, seabed segmentation. RESUMO. Este estudo apresenta uma proposta de metodologia para segmentação e classificação de imagens de sonar de Abertura Sintética a partir de modelos de Dinâmica Simbólica. Foram desenvolvidas, em um primeiro momento, técnicas de descrição de registros de sonar, com base na transformação da distribuição dos pixels da imagem em pontos em um espaço simbólico, codificado a partir de uma função de interação, de modo que as imagens podem ser interpretadas como sistemas dinâmicos em que trajetórias do sistema podem ser estabelecidas. Uma das características marcantes deste método é que, ao descrever uma imagem como um sistema dinâmico, é possível calcular grandezas como dimensão fractal (D) e entropia de Shannon (H) além da quantidade de informação inerente a imagem. Foi possível classificar, posteriormente, características texturais das imagens com base nas propriedades dinâmicas do espaço simbólico, o que permitiu a segmentação automática de padrões de “backscatter” indicando variações da geologia/geomorfologia do substrato marinho. O método proposto atingiu um razoável grau de sucesso em relação à acurácia de segmentação, com sucesso compatível com métodos alternativos descritos em literatura.Palavras-chave: sonar de abertura sintética, processamento de imagens, modelos dinâmicos, fractal, segmentação.
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Yao, Hongtai, Xianpei Wang, Le Zhao, Meng Tian, Zini Jian, Li Gong, and Bowen Li. "An Object-Based Markov Random Field with Partition-Global Alternately Updated for Semantic Segmentation of High Spatial Resolution Remote Sensing Image." Remote Sensing 14, no. 1 (December 29, 2021): 127. http://dx.doi.org/10.3390/rs14010127.
Повний текст джерелаАнотація:
The Markov random field (MRF) method is widely used in remote sensing image semantic segmentation because of its excellent spatial (relationship description) ability. However, there are some targets that are relatively small and sparsely distributed in the entire image, which makes it easy to misclassify these pixels into different classes. To solve this problem, this paper proposes an object-based Markov random field method with partition-global alternately updated (OMRF-PGAU). First, four partition images are constructed based on the original image, they overlap with each other and can be reconstructed into the original image; the number of categories and region granularity for these partition images are set. Then, the MRF model is built on the partition images and the original image, their segmentations are alternately updated. The update path adopts a circular path, and the correlation assumption is adopted to establish the connection between the label fields of partition images and the original image. Finally, the relationship between each label field is constantly updated, and the final segmentation result is output after the segmentation has converged. Experiments on texture images and different remote sensing image datasets show that the proposed OMRF-PGAU algorithm has a better segmentation performance than other selected state-of-the-art MRF-based methods.
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Lubis, Kesuma Anggraini, Muhammad Rusdi, and Sugianto Sugianto. "Proses Segmentasi Citra Satelit Untuk Pemetaan Tutupan Lahan." Jurnal Ilmiah Mahasiswa Pertanian 6, no. 4 (November 1, 2021): 691–98. http://dx.doi.org/10.17969/jimfp.v6i4.18414.
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Abstrak. Salah satu permasalahan penting dalam bidang pengolahan citra dan pengenalan pola adalah segmentasi citra ke dalam area homogen. Ekstraksi ciri dan segmentasi citra merupakan langkah awal dalam analisis citra. Tujuan utama segmentasi adalah membagi citra ke dalam bagian-bagian yang mempunyai korelasi kuat dengan objek dalam citra. Pada proses segmentasi dapat dilakukan dengan berbagai pendekatan algoritma, salah satu algoritma yang banyak digunakan pada penelitian-penelitian sebelumnya adalah algoritma multiresolusi segmentasi. Berdasarkan konsep segmentasi, untuk mendapatkan hasil segmentasi dengan menggunakan algoritma multiresolusi segmentasi tergantung dari lima parameter yaitu parameter skala, bentuk, warna, kehalusan dan kekompakan. Penelitian ini bertujuan untuk mengkaji proses metode segmentasi citra satelit untuk pemetaan tutupan lahan dengan menggunakan algoritma multiresolution segmentation.Satellite Image Segmentation Process for Land Cover MappingAbstract. One of the important problems in image processing and pattern recognition is image segmentation into homogeneous areas. Feature extraction and image segmentation are the first steps in image analysis. The main purpose of segmentation is to divide the image into parts that have a strong correlation with the objects in the image. The segmentation process can be done with various algorithm approaches, one of the algorithms that is widely used in previous studies is the multi-resolution segmentation algorithm. Based on the concept of segmentation, to obtain segmentation results using a multi-resolution segmentation algorithm depends on five parameters, namely the parameters of scale, shape, color, smoothness and compactness. This study aims to examine the process of satellite image segmentation method for land cover mapping using a multiresolution segmentation algorithm.
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Li, Yuan, Fu Cang Jia, Xiao Dong Zhang, Cheng Huang, and Huo Ling Luo. "Local Patch Similarity Ranked Voxelwise STAPLE on Magnetic Resonance Image Hippocampus Segmentation." Applied Mechanics and Materials 333-335 (July 2013): 1065–70. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1065.
Повний текст джерелаАнотація:
The segmentation and labeling of sub-cortical structures of interest are important tasks for the assessment of morphometric features in quantitative magnetic resonance (MR) image analysis. Recently, multi-atlas segmentation methods with statistical fusion strategy have demonstrated high accuracy in hippocampus segmentation. While, most of the segmentations rarely consider spatially variant model and reserve all segmentations. In this study, we propose a novel local patch-based and ranking strategy for voxelwise atlas selection to extend the original Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. The local ranking strategy is based on the metric of normalized cross correlation (NCC). Unlike its predecessors, this method estimates the fusion of each voxel patch-by-patch and makes use of gray image features as a prior. Validation results on 33 pairs of hippocampus MR images show good performance on the segmentation of hippocampus.
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Cruz-Aceves, I., J. G. Avina-Cervantes, J. M. Lopez-Hernandez, M. G. Garcia-Hernandez, M. Torres-Cisneros, H. J. Estrada-Garcia, and A. Hernandez-Aguirre. "Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/419018.
Повний текст джерелаАнотація:
This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.
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Beasley, Ryan A. "Semiautonomous Medical Image Segmentation Using Seeded Cellular Automaton Plus Edge Detector." ISRN Signal Processing 2012 (May 17, 2012): 1–9. http://dx.doi.org/10.5402/2012/914232.
Повний текст джерелаАнотація:
Segmentations of medical images are required in a number of medical applications such as quantitative analyses and patient-specific orthotics, yet accurate segmentation without significant user attention remains a challenge. This work presents a novel segmentation algorithm combining the region-growing Seeded Cellular Automata with a boundary term based on an edge-detected image. Both single processor and parallel processor implementations are developed and the algorithm is shown to be suitable for quick segmentations (2.2 s for voxel brain MRI) and interactive supervision (2–220 Hz). Furthermore, a method is described for generating appropriate edge-detected images without requiring additional user attention. Experiments demonstrate higher segmentation accuracy for the proposed algorithm compared with both Graphcut and Seeded Cellular Automata, particularly when provided minimal user attention.
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Mohd Ghani, Noor Ain Syazwani, and Abdul Kadir Jumaat. "Selective Segmentation Model for Vector-Valued Images." Journal of Information and Communication Technology 21, No.2 (April 7, 2022): 149–73. http://dx.doi.org/10.32890/jict2022.21.2.1.
Повний текст джерелаАнотація:
One of the most important steps in image processing and computer vision for image analysis is segmentation, which can be classified into global and selective segmentations. Global segmentation models can segment whole objects in an image. Unfortunately, these models are unable to segment a specific object that is required for extraction. To overcome this limitation, the selective segmentation model, which is capable of extracting a particular object or region in an image, must be prioritised. Recent selective segmentation models have shown to be effective in segmenting greyscale images. Nevertheless, if the input is vector-valued or identified as a colour image, the models simply ignore the colour information by converting that image into a greyscale format. Colour plays an important role in the interpretation of object boundaries within an image as it helps to provide a more detailed explanation of the scene’s objects. Therefore, in this research, a model for selective segmentation of vector-valued images is proposed by combining concepts from existing models. The finite difference method was used to solve the resulting Euler-Lagrange (EL) partial differential equation of the proposed model. The accuracy of the proposed model’s segmentation output was then assessed using visual observation as well as by using two similarity indices, namely the Jaccard (JSC) and Dice (DSC) similarity coefficients. Experimental results demonstrated that the proposed model is capable of successfully segmenting a specific object in vector-valued images. Future research on this area can be further extended in three-dimensional modelling.
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Xiang, Ming, Zhen Dong Cui, and Yuan Hong Wu. "A Fingerprint Image Segmentation Method Based on Fractal Dimension." Advanced Materials Research 461 (February 2012): 299–301. http://dx.doi.org/10.4028/www.scientific.net/amr.461.299.
Повний текст джерелаАнотація:
Fractal analysis is becoming more and more popular in image segmentation community, in which the box-counting based fractal dimension estimations are most commonly used. In this paper, a novel fractal estimation algorithm is proposed. Both the proposed algorithm and the box-counting based methods have been applied to the segmentation of texture images. The comparison results demonstrate that the fractal estimation can differentiate texture images more effectively and provide more robust segmentations
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Sutaji, Deni, Chastine Fatichah, and Dini Adni Navastara. "Segmentasi Pembuluh Darah Retina Pada Citra Fundus Menggunakan Gradient Based Adaptive Thresholding Dan Region Growing." Register: Jurnal Ilmiah Teknologi Sistem Informasi 2, no. 2 (July 1, 2016): 105. http://dx.doi.org/10.26594/r.v2i2.553.
Повний текст джерелаАнотація:
AbstrakSegmentasi pembuluh darah pada citra fundus retina menjadi hal yang substansial dalam dunia kedokteran, karena dapat digunakan untuk mendeteksi penyakit, seperti: diabetic retinopathy, hypertension, dan cardiovascular. Dokter membutuhkan waktu sekitar dua jam untuk mendeteksi pembuluh darah retina, sehingga diperlukan metode yang dapat membantu screening agar lebih cepat.Penelitian sebelumnya mampu melakukan segmentasi pembuluh darah yang sensitif terhadap variasi ukuran lebar pembuluh darah namun masih terjadi over-segmentasi pada area patologi. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan metode segmentasi pembuluh darah pada citra fundus retina yang dapat mengurangi over-segmentasi pada area patologi menggunakan Gradient Based Adaptive Thresholding dan Region Growing.Metode yang diusulkan terdiri dari 3 tahap, yaitu segmentasi pembuluh darah utama, deteksi area patologi dan segmentasi pembuluh darah tipis. Tahap segmentasi pembuluh darah utama menggunakan high-pass filtering dan tophat reconstruction pada kanal hijau citra yang sudah diperbaiki kontrasnya sehingga lebih jelas perbedaan antara pembuluh darah dan background. Tahap deteksi area patologi menggunakan metode Gradient Based Adaptive Thresholding. Tahap segmentasi pembuluh darah tipis menggunakan Region Growing berdasarkan informasi label pembuluh darah utama dan label area patologi. Hasil segmentasi pembuluh darah utama dan pembuluh darah tipis kemudian digabungkan sehingga menjadi keluaran sistem berupa citra biner pembuluh darah. Berdasarkan hasil uji coba, metode ini mampu melakukan segmentasi pembuluh darah retina dengan baik pada citra fundus DRIVE, yaitu dengan akurasi rata-rata 95.25% dan nilai Area Under Curve (AUC) pada kurva Relative Operating Characteristic (ROC) sebesar 74.28%. Kata Kunci: citra fundus retina, gradient based adaptive thresholding, patologi, pembuluh darah retina, region growing, segmentasi. AbstractSegmentation of blood vessels in the retina fundus image becomes substantial in the medical, because it can be used to detect diseases, such as diabetic retinopathy, hypertension, and cardiovascular. Doctor takes about two hours to detect the blood vessels of the retina, so screening methods are needed to make it faster. The previous methods are able to segment the blood vessels that are sensitive to variations in the size of the width of blood vessels, but there is over-segmentation in the area of pathology. Therefore, this study aims to develop a segmentation method of blood vessels in retinal fundus images which can reduce over-segmentation in the area of pathology using Gradient Based Adaptive Thresholding and Region Growing. The proposed method consists of three stages, namely the segmentation of the main blood vessels, detection area of pathology and segmentation thin blood vessels. Main blood vessels segmentation using high-pass filtering and tophat reconstruction on the green channel which adjusted of contras image that results the clearly between object and background. Detection area of pathology using Gradient Based Adaptive thresholding method. Thin blood vessels segmentation using Region Growing based on the information main blood vessel segmentation and detection of pathology area. Output of the main blood vessel segmentation and thin blood vessels are then combined to reconstruct an image of the blood vessels as output system.This method is able to segment the blood vessels in retinal fundus images DRIVE with an accuracy of 95.25% and the value of Area Under Curve (AUC) in the relative operating characteristic curve (ROC) of 74.28%.Keywords: Blood vessel, fundus retina image, gradient based adaptive thresholding, pathology, region growing, segmentation.
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Liu, Hong, Haijun Wei, Lidui Wei, Jingming Li, and Zhiyuan Yang. "The Segmentation of Wear Particles Images UsingJ-Segmentation Algorithm." Advances in Tribology 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4931502.
Повний текст джерелаАнотація:
This study aims to use a JSEG algorithm to segment the wear particle’s image. Wear particles provide detailed information about the wear processes taking place between mechanical components. Autosegmentation of their images is key to intelligent classification system. This study examined whether this algorithm can be used in particles’ image segmentation. Different scales have been tested. Compared with traditional thresholding along with edge detector, the JSEG algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with little computing complexity. A conclusion can be drawn that the JSEG method is suited for imaged wear particle segmentation and can be put into practical use in wear particle’s identification system.
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Li, Jianzhang, Sven Nebelung, Björn Rath, Markus Tingart, and Jörg Eschweiler. "A novel combined level set model for automatic MR image segmentation." Current Directions in Biomedical Engineering 6, no. 3 (September 1, 2020): 20–23. http://dx.doi.org/10.1515/cdbme-2020-3006.
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AbstractMedical image processing comes along with object segmentation, which is one of the most important tasks in that field. Nevertheless, noise and intensity inhomogeneity in magnetic resonance images challenge the segmentation procedure. The level set method has been widely used in object detection. The flexible integration of energy terms affords the level set method to deal with variable difficulties. In this paper, we introduce a novel combined level set model that mainly cooperates with an edge detector and a local region intensity descriptor. The noise and intensity inhomogeneities are eliminated by the local region intensity descriptor. The edge detector helps the level set model to locate the object boundaries more precisely. The proposed model was validated on synthesized images and magnetic resonance images of in vivo wrist bones. Comparing with the ground truth, the proposed method reached a Dice similarity coefficient of > 0.99 on all image tests, while the compared segmentation approaches failed the segmentations. The presented combined level set model can be used for the object segmentation in magnetic resonance images.
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Kollem, Sreedhar, Katta Rama Linga Reddy, and Duggirala Srinivasa Rao. "A Review of Image Denoising and Segmentation Methods Based on Medical Images." International Journal of Machine Learning and Computing 9, no. 3 (June 2019): 288–95. http://dx.doi.org/10.18178/ijmlc.2019.9.3.800.
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de Lotbiniere-Bassett, M., E. Schonfeld, T. Jansen, D. Anthony, and A. Veeravagu. "P.169 Reduced radiation CT imaging for augmented reality spinal surgery applications." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 48, s3 (November 2021): S68. http://dx.doi.org/10.1017/cjn.2021.445.
Повний текст джерелаАнотація:
Background: There is growing evidence for the use of augmented reality (AR) in pedicle screw placement in spinal surgery to increase surgical accuracy, improve clinical outcomes and reduce the radiation exposure required for intraoperative navigation. Auto-segmentation is the cornerstone of AR applications because it correlates patient-specific anatomy to structures segmented from preoperative computed tomography (pCT) images. These AR techniques allow for a reduction in the radiation dose required to acquire CT images while maintaining accurate segmentation. Methods: In this study, we methodically increase the noise that is introduced into CT images to determine the image quality threshold that is required for auto-segmentation on pCT. We then enhance the images with denoising algorithms to evaluate the effect on the segmentation. Results: The pCT radiation dose is decreased to below the current lowest clinical threshold and the resulting images still produce segmentations that are appropriate for input into AR applications. The application of denoising algorithms to the images resulted in increased artifacts and decreased bone density. Conclusions: The CT image quality that is required for successful AR auto-segmentation is lower than that which is currently employed in spine surgery. Future research is required to identify the specific, clinically relevant radiation dose thresholds.
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Yang, Yong, Shuying Huang, and Nini Rao. "An Automatic Hybrid Method for Retinal Blood Vessel Extraction." International Journal of Applied Mathematics and Computer Science 18, no. 3 (September 1, 2008): 399–407. http://dx.doi.org/10.2478/v10006-008-0036-5.
Повний текст джерелаАнотація:
An Automatic Hybrid Method for Retinal Blood Vessel ExtractionThe extraction of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. This paper presents a novel hybrid automatic approach for the extraction of retinal image vessels. The method consists in the application of mathematical morphology and a fuzzy clustering algorithm followed by a purification procedure. In mathematical morphology, the retinal image is smoothed and strengthened so that the blood vessels are enhanced and the background information is suppressed. The fuzzy clustering algorithm is then employed to the previous enhanced image for segmentation. After the fuzzy segmentation, a purification procedure is used to reduce the weak edges and noise, and the final results of the blood vessels are consequently achieved. The performance of the proposed method is compared with some existing segmentation methods and hand-labeled segmentations. The approach has been tested on a series of retinal images, and experimental results show that our technique is promising and effective.
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Orlando, Nathan, Igor Gyacskov, Derek J. Gillies, Fumin Guo, Cesare Romagnoli, David D’Souza, Derek W. Cool, Douglas A. Hoover, and Aaron Fenster. "Effect of dataset size, image quality, and image type on deep learning-based automatic prostate segmentation in 3D ultrasound." Physics in Medicine & Biology 67, no. 7 (March 29, 2022): 074002. http://dx.doi.org/10.1088/1361-6560/ac5a93.
Повний текст джерелаАнотація:
Abstract Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled 2D images followed by 3D reconstruction, trained on a large, clinically diverse dataset with variable image quality. As large clinical datasets are rare, widespread adoption of automatic segmentation could be facilitated with efficient 2D-based approaches and the development of an image quality grading method. The complete training dataset of 6761 2D images, resliced from 206 3D TRUS volumes acquired using end-fire and side-fire acquisition methods, was split to train two separate networks using either end-fire or side-fire images. Split datasets were reduced to 1000, 500, 250, and 100 2D images. For deep learning prediction, modified U-Net and U-Net++ architectures were implemented and compared using an unseen test dataset of 40 3D TRUS volumes. A 3D TRUS image quality grading scale with three factors (acquisition quality, artifact severity, and boundary visibility) was developed to assess the impact on segmentation performance. For the complete training dataset, U-Net and U-Net++ networks demonstrated equivalent performance, but when trained using split end-fire/side-fire datasets, U-Net++ significantly outperformed the U-Net. Compared to the complete training datasets, U-Net++ trained using reduced-size end-fire and side-fire datasets demonstrated equivalent performance down to 500 training images. For this dataset, image quality had no impact on segmentation performance for end-fire images but did have a significant effect for side-fire images, with boundary visibility having the largest impact. Our algorithm provided fast (<1.5 s) and accurate 3D segmentations across clinically diverse images, demonstrating generalizability and efficiency when employed on smaller datasets, supporting the potential for widespread use, even when data is scarce. The development of an image quality grading scale provides a quantitative tool for assessing segmentation performance.
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Brownhill, Daniel, Yachin Chen, Barbara A. K. Kreilkamp, Christophe de Bezenac, Christine Denby, Martyn Bracewell, Shubhabrata Biswas, Kumar Das, Anthony G. Marson, and Simon S. Keller. "Automated subcortical volume estimation from 2D MRI in epilepsy and implications for clinical trials." Neuroradiology 64, no. 5 (October 18, 2021): 935–47. http://dx.doi.org/10.1007/s00234-021-02811-x.
Повний текст джерелаАнотація:
Abstract Purpose Most techniques used for automatic segmentation of subcortical brain regions are developed for three-dimensional (3D) MR images. MRIs obtained in non-specialist hospitals may be non-isotropic and two-dimensional (2D). Automatic segmentation of 2D images may be challenging and represents a lost opportunity to perform quantitative image analysis. We determine the performance of a modified subcortical segmentation technique applied to 2D images in patients with idiopathic generalised epilepsy (IGE). Methods Volume estimates were derived from 2D (0.4 × 0.4 × 3 mm) and 3D (1 × 1x1mm) T1-weighted acquisitions in 31 patients with IGE and 39 healthy controls. 2D image segmentation was performed using a modified FSL FIRST (FMRIB Integrated Registration and Segmentation Tool) pipeline requiring additional image reorientation, cropping, interpolation and brain extraction prior to conventional FIRST segmentation. Consistency between segmentations was assessed using Dice coefficients and volumes across both approaches were compared between patients and controls. The influence of slice thickness on consistency was further assessed using 2D images with slice thickness increased to 6 mm. Results All average Dice coefficients showed excellent agreement between 2 and 3D images across subcortical structures (0.86–0.96). Most 2D volumes were consistently slightly lower compared to 3D volumes. 2D images with increased slice thickness showed lower agreement with 3D images with lower Dice coefficients (0.55–0.83). Significant volume reduction of the left and right thalamus and putamen was observed in patients relative to controls across 2D and 3D images. Conclusion Automated subcortical volume estimation of 2D images with a resolution of 0.4 × 0.4x3mm using a modified FIRST pipeline is consistent with volumes derived from 3D images, although this consistency decreases with an increased slice thickness. Thalamic and putamen atrophy has previously been reported in patients with IGE. Automated subcortical volume estimation from 2D images is feasible and most reliable at using in-plane acquisitions greater than 1 mm x 1 mm and provides an opportunity to perform quantitative image analysis studies in clinical trials.
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Shah, Nilima, Dhanesh Patel, and Pasi Fränti. "Fast Mumford-Shah Two-Phase Image Segmentation Using Proximal Splitting Scheme." Journal of Applied Mathematics 2021 (April 13, 2021): 1–13. http://dx.doi.org/10.1155/2021/6618505.
Повний текст джерелаАнотація:
The Mumford-Shah model is extensively used in image segmentation. Its energy functional causes the content of the segments to remain homogeneous and the segment boundaries to become short. However, the problem is that optimization of the functional can be very slow. To attack this problem, we propose a reduced two-phase Mumford-Shah model to segment images having one prominent object. First, initial segmentation is obtained by the k-means clustering technique, further minimizing the Mumford-Shah functional by the Douglas-Rachford algorithm. Evaluation of segmentations with various error metrics shows that 70 percent of the segmentations keep the error values below 50%. Compared to the level set method to solve the Chan-Vese model, our algorithm is significantly faster. At the same time, it gives almost the same or better segmentation results. When compared to the recent k-means variant, it also gives much better segmentation with convex boundaries. The proposed algorithm balances well between time and quality of the segmentation. A crucial step in the design of machine vision systems is the extraction of discriminant features from the images, which is based on low-level segmentation which can be obtained by our approach.
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Wan, Guo Chun, Meng Meng Li, He Xu, Wen Hao Kang, Jin Wen Rui, and Mei Song Tong. "XFinger-Net: Pixel-Wise Segmentation Method for Partially Defective Fingerprint Based on Attention Gates and U-Net." Sensors 20, no. 16 (August 10, 2020): 4473. http://dx.doi.org/10.3390/s20164473.
Повний текст джерелаАнотація:
Partially defective fingerprint image (PDFI) with poor performance poses challenges to the automated fingerprint identification system (AFIS). To improve the quality and the performance rate of PDFI, it is essential to use accurate segmentation. Currently, most fingerprint image segmentations use methods with ridge orientation, ridge frequency, coherence, variance, local gradient, etc. This paper proposes a method of XFinger-Net for segmenting PDFIs. Based on U-Net, XFinger-Net inherits its characteristics. The attention gate with fewer parameters is used to replace the cascaded network, which can suppress uncorrelated regions of PDFIs. Moreover, the XFinger-Net implements a pixel-level segmentation and takes non-blocking fingerprint images as an input to preserve the global characteristics of PDFIs. The XFinger-Net can achieve a very good segmentation effect as demonstrated in the self-made fingerprint segmentation test.
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Liu, Hongbing, Xiaoyu Diao, and Huaping Guo. "Quantitative analysis for image segmentation by granular computing clustering from the view of set." Journal of Algorithms & Computational Technology 13 (January 2019): 174830181983305. http://dx.doi.org/10.1177/1748301819833050.
Повний текст джерелаАнотація:
As partition method of set, granular computing clustering is applied to image segmentation evaluated by global consistency error, variation of Information, and Rand index from the view of set. Firstly, quantitative assessment of clustering is evaluated from the view of set. Secondly, granular computing clustering algorithms are induced by the distance formulas, the granules with different shapes are defined as the forms of vectors by different distance norms, especially, the atomic granule is induced by a point of space, the union operator realizes the transformation between two granule spaces and is used to form granular computing clustering algorithms. Thirdly, the image segmentations by granular computing clustering are evaluated from the view of set, such as global consistency error, variation of Information, and Rand index. Segmentations of the color images selected from BSD300 are used to show the superiority and feasibility for image segmentation by granular computing clustering compared with Kmeans and fuzzy c-means by experiments.
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Lee, Aaron Y., Cecilia S. Lee, Pearse A. Keane, and Adnan Tufail. "Use of Mechanical Turk as a MapReduce Framework for Macular OCT Segmentation." Journal of Ophthalmology 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/6571547.
Повний текст джерелаАнотація:
Purpose. To evaluate the feasibility of using Mechanical Turk as a massively parallel platform to perform manual segmentations of macular spectral domain optical coherence tomography (SD-OCT) images using a MapReduce framework.Methods.A macular SD-OCT volume of 61 slice images was map-distributed to Amazon Mechanical Turk. Each Human Intelligence Task was set to$0.01 and required the user to draw five lines to outline the sublayers of the retinal OCT image after being shown example images. Each image was submitted twice for segmentation, and interrater reliability was calculated. The interface was created using custom HTML5 and JavaScript code, and data analysis was performed using R. An automated pipeline was developed to handle the map and reduce steps of the framework.Results.More than 93,500 data points were collected using this framework for the 61 images submitted. Pearson’s correlation of interrater reliability was 0.995 (p<0.0001) and coefficient of determination was 0.991. The cost of segmenting the macular volume was$1.21. A total of 22 individual Mechanical Turk users provided segmentations, each completing an average of 5.5 HITs. Each HIT was completed in an average of 4.43 minutes.Conclusions.Amazon Mechanical Turk provides a cost-effective, scalable, high-availability infrastructure for manual segmentation of OCT images.
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Yahya, Rafaa I., Siti Mariyam Shamsuddin, Salah I. Yahya, Bisan Alsalibi, and Ghada K. Al-Khafaji. "Membrane Computing for Real Medical Image Segmentation." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 6, no. 2 (December 10, 2018): 27. http://dx.doi.org/10.14500/aro.10442.
Повний текст джерелаАнотація:
In this paper, membrane-based computing image segmentation, both region-based and edge-based, is proposed for medical images that involve two types of neighborhood relations between pixels. These neighborhood relations—namely, 4-adjacency and 8-adjacency of a membrane computing approach—construct a family of tissue-like P systems for segmenting actual 2D medical images in a constant number of steps; the two types of adjacency were compared using different hardware platforms. The process involves the generation of membrane-based segmentation rules for 2D medical images. The rules are written in the P-Lingua format and appended to the input image for visualization. The findings show that the neighborhood relations between pixels of 8-adjacency give better results compared with the 4-adjacency neighborhood relations, because the 8-adjacency considers the eight pixels around the center pixel, which reduces the required communication rules to obtain the final segmentation results. The experimental results proved that the proposed approach has superior results in terms of the number of computational steps and processing time. To the best of our knowledge, this is the first time an evaluation procedure is conducted to evaluate the efficiency of real image segmentations using membrane computing.
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Hakim, Lukman, Siti Mutrofin, and Evy Kamilah Ratnasari. "Segmentasi Citra menggunakan Support Vector Machine (SVM) dan Ellipsoid Region Search Strategy (ERSS) Arimoto Entropy berdasarkan Ciri Warna dan Tekstur." Register: Jurnal Ilmiah Teknologi Sistem Informasi 2, no. 1 (January 1, 2016): 11. http://dx.doi.org/10.26594/r.v2i1.440.
Повний текст джерелаАнотація:
Abstrak
Segmentasi citra merupakan suatu metode penting dalam pengolahan citra digital yang bertujuan membagi citra menjadi beberapa region yang homogen berdasarkan kriteria kemiripan tertentu. Salah satu syarat utama yang harus dimiliki suatu metode segmentasi citra yaitu menghasilkan citra boundary yang optimal.Untuk memenuhi syarat tersebut suatu metode segmentasi membutuhkan suatu klasifikasi piksel citra yang dapat memisahkan piksel secara linier dan non-linear. Pada penelitian ini, penulis mengusulkan metode segmentasi citra menggunakan SVM dan entropi Arimoto berbasis ERSS sehingga tahan terhadap derau dan mempunyai kompleksitas yang rendah untuk menghasilkan citra boundary yang optimal. Pertama, ekstraksi ciri warna dengan local homogeneity dan ciri tekstur dengan menggunakan Gray Level Co-occurrence Matrix (GLCM) yang menghasilkan beberapa fitur. Kedua, pelabelan dengan Arimoto berbasis ERSS yang digunakan sebagai kelas dalam klasifikasi. Ketiga, hasil ekstraksi fitur dan training kemudian diklasifikasi berdasarkan label dengan SVM yang telah di-training. Dari percobaan yang dilakukan menunjukkan hasil segmentasi kurang optimal dengan akurasi 69 %. Reduksi fitur perlu dilakukan untuk menghasilkan citra yang tersegmentasi dengan baik.
Kata kunci: segmentasi citra, support vector machine, ERSS Arimoto Entropy, ekstraksi ciri.
Abstract
Image segmentation is an important tool in image processing that divides an image into homogeneous regions based on certain similarity criteria, which ideally should be meaning-full for a certain purpose. Optimal boundary is one of the main criteria that an image segmentation method should has. A classification method that can partitions pixel linearly or non-linearly is needed by an image segmentation method. We propose a color image segmentation using Support Vector Machine (SVM) classification and ERSS Arimoto entropy thresholding to get optimal boundary of segmented image that noise-free and low complexity. Firstly, the pixel-level color feature and texture feature of the image, which is used as input to SVM model (classifier), are extracted via the local homogeneity and Gray Level Co-Occurrence Matrix (GLCM). Then, determine class of classifier using Arimoto based ERSS thresholding. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation result less satisfied segmented image with 69 % accuracy. Feature reduction is needed to get an effective image segmentation.
Key word: image segmentation, support vector machine, ERSS Arimoto Entropy, feature extraction.
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Kumaseh, Max R., Luther Latumakulita, and Nelson Nainggolan. "SEGMENTASI CITRA DIGITAL IKAN MENGGUNAKAN METODE THRESHOLDING." JURNAL ILMIAH SAINS 13, no. 1 (May 17, 2013): 74. http://dx.doi.org/10.35799/jis.13.1.2013.2057.
Повний текст джерелаАнотація:
SEGMENTASI CITRA DIGITAL IKAN MENGGUNAKAN METODE THRESHOLDINGABSTRAK Untuk mengenal jenis-jenis ikan berdasarkan ciri-cirinya, telah dibuat suatu sistem untuk memisahkan objek mata ikan menggunakan metode thresholding. Prosesnya dimulai dengan menginput citra digital ikan, selanjutnya dikonversi ke citra grayscale. Kemudian dilakukan proses segmentasi terhadap citra grayscale. Selanjutnya, dipilih hasil segmentasi dan ditandai dengan proses deteksi tepi menggunakan operator Canny yang dipertajam dengan proses dilasi. Proses terakhir adalah membuat plot contour terhadap hasil proses dilasi dan citra grayscale. Hasil segmentasi berhasil memisahkan objek mata ikan dengan menggunakan metode thresholding local. Keseluruhan proses ini dilakukan dengan menggunakan MATLAB R2012a. Kata kunci : Mata Ikan, Segmentasi Citra, Thresholding DIGITAL FISH IMAGE SEGMENTATION BY THRESHOLDING METHOD ABSTRACT A system of fish eyelets seperation has been conducted to identify types of fish acording to their characteristics, by using thresholding method. The process start by inserting digital fish image then convert it to grayscale image. Next step is to process segmentation the grayscale image. Choosed the segmentation result then marked it by edge detection process using Canny operation which has been sharpened by dilation process. The last step is to make contour plot to dilation result and grayscale image. The result of the segmentation shows that the fish eyelets can be separated using local thresholding method. The whole process is conducted by using MATLAB R2012a. Keywords : Fish Eyelets, Segmentation Image, Thresholding
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Tetard, Martin, Ross Marchant, Giuseppe Cortese, Yves Gally, Thibault de Garidel-Thoron, and Luc Beaufort. "Technical note: A new automated radiolarian image acquisition, stacking, processing, segmentation and identification workflow." Climate of the Past 16, no. 6 (December 2, 2020): 2415–29. http://dx.doi.org/10.5194/cp-16-2415-2020.
Повний текст джерелаАнотація:
Abstract. Identification of microfossils is usually done by expert taxonomists and requires time and a significant amount of systematic knowledge developed over many years. These studies require manual identification of numerous specimens in many samples under a microscope, which is very tedious and time-consuming. Furthermore, identification may differ between operators, biasing reproducibility. Recent technological advances in image acquisition, processing and recognition now enable automated procedures for this process, from microscope image acquisition to taxonomic identification. A new workflow has been developed for automated radiolarian image acquisition, stacking, processing, segmentation and identification. The protocol includes a newly proposed methodology for preparing radiolarian microscopic slides. We mount eight samples per slide, using a recently developed 3D-printed decanter that enables the random and uniform settling of particles and minimizes the loss of material. Once ready, slides are automatically imaged using a transmitted light microscope. About 4000 specimens per slide (500 per sample) are captured in digital images that include stacking techniques to improve their focus and sharpness. Automated image processing and segmentation is then performed using a custom plug-in developed for the ImageJ software. Each individual radiolarian image is automatically classified by a convolutional neural network (CNN) trained on a Neogene to Quaternary radiolarian database (currently 21 746 images, corresponding to 132 classes) using the ParticleTrieur software. The trained CNN has an overall accuracy of about 90 %. The whole procedure, including the image acquisition, stacking, processing, segmentation and recognition, is entirely automated via a LabVIEW interface, and it takes approximately 1 h per sample. Census data count and classified radiolarian images are then automatically exported and saved. This new workflow paves the way for the analysis of long-term, radiolarian-based palaeoclimatic records from siliceous-remnant-bearing samples.
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Cruz-Aceves, I., J. G. Avina-Cervantes, J. M. Lopez-Hernandez, H. Rostro-Gonzalez, C. H. Garcia-Capulin, M. Torres-Cisneros, and R. Guzman-Cabrera. "Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation." Computational and Mathematical Methods in Medicine 2013 (2013): 1–14. http://dx.doi.org/10.1155/2013/190304.
Повний текст джерелаАнотація:
This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation.
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Cruz-Aceves, I., J. G. Avina-Cervantes, J. M. Lopez-Hernandez, M. G. Garcia-Hernandez, and M. A. Ibarra-Manzano. "Unsupervised Cardiac Image Segmentation via Multiswarm Active Contours with a Shape Prior." Computational and Mathematical Methods in Medicine 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/909625.
Повний текст джерелаАнотація:
This paper presents a new unsupervised image segmentation method based on particle swarm optimization and scaled active contours with shape prior. The proposed method uses particle swarm optimization over a polar coordinate system to perform the segmentation task, increasing the searching capability on medical images with respect to different interactive segmentation techniques. This method is used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, where the shape prior is acquired by cardiologists, and it is utilized as the initial active contour. Moreover, to assess the performance of the cardiac medical image segmentations obtained by the proposed method and by the interactive techniques regarding the regions delineated by experts, a set of validation metrics has been adopted. The experimental results are promising and suggest that the proposed method is capable of segmenting human heart and ventricular areas accurately, which can significantly help cardiologists in clinical decision support.
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Ma, Yushu, Yingzhe Gao, Zhaolin Li, Ang Li, Yi Wang, Jian Liu, Yao Yu, Wenbo Shi, and Zhenhe Ma. "Automated retinal layer segmentation on optical coherence tomography image by combination of structure interpolation and lateral mean filtering." Journal of Innovative Optical Health Sciences 14, no. 01 (January 2021): 2140011. http://dx.doi.org/10.1142/s1793545821400113.
Повний текст джерелаАнотація:
Segmentation of layers in retinal images obtained by optical coherence tomography (OCT) has become an important clinical tool to diagnose ophthalmic diseases. However, due to the susceptibility to speckle noise and shadow of blood vessels etc., the layer segmentation technology based on a single image still fail to reach a satisfactory level. We propose a combination method of structure interpolation and lateral mean filtering (SI-LMF) to improve the signal-to-noise ratio based on one retinal image. Before performing one-dimensional lateral mean filtering to remove noise, structure interpolation was operated to eliminate thickness fluctuations. Then, we used boundary growth method to identify boundaries. Compared with existing segmentations, the method proposed in this paper requires less data and avoids the influence of microsaccade. The automatic segmentation method was verified on the spectral domain OCT volume images obtained from four normal objects, which successfully identified the boundaries of 10 physiological layers, consistent with the results based on the manual determination.
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Warfield, Simon K., Kelly H. Zou, and William M. Wells. "Validation of image segmentation by estimating rater bias and variance." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1874 (April 11, 2008): 2361–75. http://dx.doi.org/10.1098/rsta.2008.0040.
Повний текст джерелаАнотація:
The accuracy and precision of segmentations of medical images has been difficult to quantify in the absence of a ‘ground truth’ or reference standard segmentation for clinical data. Although physical or digital phantoms can help by providing a reference standard, they do not allow the reproduction of the full range of imaging and anatomical characteristics observed in clinical data. An alternative assessment approach is to compare with segmentations generated by domain experts. Segmentations may be generated by raters who are trained experts or by automated image analysis algorithms. Typically, these segmentations differ due to intra-rater and inter-rater variability. The most appropriate way to compare such segmentations has been unclear. We present here a new algorithm to enable the estimation of performance characteristics, and a true labelling, from observations of segmentations of imaging data where segmentation labels may be ordered or continuous measures. This approach may be used with, among others, surface, distance transform or level-set representations of segmentations, and can be used to assess whether or not a rater consistently overestimates or underestimates the position of a boundary.
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Li, Qingyun, Zhibin Yu, Yubo Wang, and Haiyong Zheng. "TumorGAN: A Multi-Modal Data Augmentation Framework for Brain Tumor Segmentation." Sensors 20, no. 15 (July 28, 2020): 4203. http://dx.doi.org/10.3390/s20154203.
Повний текст джерелаАнотація:
The high human labor demand involved in collecting paired medical imaging data severely impedes the application of deep learning methods to medical image processing tasks such as tumor segmentation. The situation is further worsened when collecting multi-modal image pairs. However, this issue can be resolved through the help of generative adversarial networks, which can be used to generate realistic images. In this work, we propose a novel framework, named TumorGAN, to generate image segmentation pairs based on unpaired adversarial training. To improve the quality of the generated images, we introduce a regional perceptual loss to enhance the performance of the discriminator. We also develop a regional L1 loss to constrain the color of the imaged brain tissue. Finally, we verify the performance of TumorGAN on a public brain tumor data set, BraTS 2017. The experimental results demonstrate that the synthetic data pairs generated by our proposed method can practically improve tumor segmentation performance when applied to segmentation network training.
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Wählby, Carolina, Joakim Lindblad, Mikael Vondrus, Ewert Bengtsson, and Lennart Björkesten. "Algorithms for Cytoplasm Segmentation of Fluorescence Labelled Cells." Analytical Cellular Pathology 24, no. 2-3 (2002): 101–11. http://dx.doi.org/10.1155/2002/821782.
Повний текст джерелаАнотація:
Automatic cell segmentation has various applications in cytometry, and while the nucleus is often very distinct and easy to identify, the cytoplasm provides a lot more challenge. A new combination of image analysis algorithms for segmentation of cells imaged by fluorescence microscopy is presented. The algorithm consists of an image pre‐processing step, a general segmentation and merging step followed by a segmentation quality measurement. The quality measurement consists of a statistical analysis of a number of shape descriptive features. Objects that have features that differ to that of correctly segmented single cells can be further processed by a splitting step. By statistical analysis we therefore get a feedback system for separation of clustered cells. After the segmentation is completed, the quality of the final segmentation is evaluated. By training the algorithm on a representative set of training images, the algorithm is made fully automatic for subsequent images created under similar conditions. Automatic cytoplasm segmentation was tested on CHO‐cells stained with calcein. The fully automatic method showed between 89% and 97% correct segmentation as compared to manual segmentation.
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Jo, Hang-Chan, Hyeonwoo Jeong, Junhyuk Lee, Kyung-Sun Na, and Dae-Yu Kim. "Quantification of Blood Flow Velocity in the Human Conjunctival Microvessels Using Deep Learning-Based Stabilization Algorithm." Sensors 21, no. 9 (May 6, 2021): 3224. http://dx.doi.org/10.3390/s21093224.
Повний текст джерелаАнотація:
The quantification of blood flow velocity in the human conjunctiva is clinically essential for assessing microvascular hemodynamics. Since the conjunctival microvessel is imaged in several seconds, eye motion during image acquisition causes motion artifacts limiting the accuracy of image segmentation performance and measurement of the blood flow velocity. In this paper, we introduce a novel customized optical imaging system for human conjunctiva with deep learning-based segmentation and motion correction. The image segmentation process is performed by the Attention-UNet structure to achieve high-performance segmentation results in conjunctiva images with motion blur. Motion correction processes with two steps—registration and template matching—are used to correct for large displacements and fine movements. The image displacement values decrease to 4–7 μm during registration (first step) and less than 1 μm during template matching (second step). With the corrected images, the blood flow velocity is calculated for selected vessels considering temporal signal variances and vessel lengths. These methods for resolving motion artifacts contribute insights into studies quantifying the hemodynamics of the conjunctiva, as well as other tissues.
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Akcay, Ozgun, Emin Avsar, Melis Inalpulat, Levent Genc, and Ahmet Cam. "Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery." ISPRS International Journal of Geo-Information 7, no. 11 (October 31, 2018): 424. http://dx.doi.org/10.3390/ijgi7110424.
Повний текст джерелаАнотація:
Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained.
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Sun, Qixuan, Nianhua Fang, Zhuo Liu, Liang Zhao, Youpeng Wen, and Hongxiang Lin. "HybridCTrm: Bridging CNN and Transformer for Multimodal Brain Image Segmentation." Journal of Healthcare Engineering 2021 (October 1, 2021): 1–10. http://dx.doi.org/10.1155/2021/7467261.
Повний текст джерелаАнотація:
Multimodal medical image segmentation is always a critical problem in medical image segmentation. Traditional deep learning methods utilize fully CNNs for encoding given images, thus leading to deficiency of long-range dependencies and bad generalization performance. Recently, a sequence of Transformer-based methodologies emerges in the field of image processing, which brings great generalization and performance in various tasks. On the other hand, traditional CNNs have their own advantages, such as rapid convergence and local representations. Therefore, we analyze a hybrid multimodal segmentation method based on Transformers and CNNs and propose a novel architecture, HybridCTrm network. We conduct experiments using HybridCTrm on two benchmark datasets and compare with HyperDenseNet, a network based on fully CNNs. Results show that our HybridCTrm outperforms HyperDenseNet on most of the evaluation metrics. Furthermore, we analyze the influence of the depth of Transformer on the performance. Besides, we visualize the results and carefully explore how our hybrid methods improve on segmentations.
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Campbell, N. W., B. T. Thomas, and T. Troscianko. "Automatic Segmentation and Classification of Outdoor Images Using Neural Networks." International Journal of Neural Systems 08, no. 01 (February 1997): 137–44. http://dx.doi.org/10.1142/s0129065797000161.
Повний текст джерелаАнотація:
The paper describes how neural networks may be used to segment and label objects in images. A self-organising feature map is used for the segmentation phase, and we quantify the quality of the segmentations produced as well as the contribution made by colour and texture features. A multi-layer perceptron is trained to label the regions produced by the segmentation process. It is shown that 91.1% of the image area is correctly classified into one of eleven categories which include cars, houses, fences, roads, vegetation and sky.
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Cahuina, Edward Cayllahua, Jean Cousty, Yukiko Kenmochi, Arnaldo de Albuquerque Araújo, Guillermo Cámara-Chávez, and Silvio Jamil F. Guimarães. "Efficient Algorithms for Hierarchical Graph-Based Segmentation Relying on the Felzenszwalb–Huttenlocher Dissimilarity." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 11 (October 2019): 1940008. http://dx.doi.org/10.1142/s0218001419400081.
Повний текст джерелаАнотація:
Hierarchical image segmentation provides a region-oriented scale-space, i.e. a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. However, most image segmentation algorithms, among which a graph-based image segmentation method relying on a region merging criterion was proposed by Felzenszwalb–Huttenlocher in 2004, do not lead to a hierarchy. In order to cope with a demand for hierarchical segmentation, Guimarães et al. proposed in 2012 a method for hierarchizing the popular Felzenszwalb–Huttenlocher method, without providing an algorithm to compute the proposed hierarchy. This paper is devoted to providing a series of algorithms to compute the result of this hierarchical graph-based image segmentation method efficiently, based mainly on two ideas: optimal dissimilarity measuring and incremental update of the hierarchical structure. Experiments show that, for an image of size 321 × 481 pixels, the most efficient algorithm produces the result in half a second whereas the most naive one requires more than 4 h.
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Chacón, Gerardo, Johel E. Rodríguez, Valmore Bermúdez, Miguel Vera, Juan Diego Hernández, Sandra Vargas, Aldo Pardo, Carlos Lameda, Delia Madriz, and Antonio J. Bravo. "Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer." F1000Research 7 (July 17, 2018): 1098. http://dx.doi.org/10.12688/f1000research.14491.1.
Повний текст джерелаАнотація:
Background: The multi–slice computerized tomography (MSCT) is a medical imaging modality that has been used to determine the size and location of the stomach cancer. Additionally, MSCT is considered the best modality for the staging of gastric cancer. One way to assess the type 2 cancer of stomach is by detecting the pathological structure with an image segmentation approach. The tumor segmentation of MSCT gastric cancer images enables the diagnosis of the disease condition, for a given patient, without using an invasive method as surgical intervention. Methods: This approach consists of three stages. The initial stage, an image enhancement, consists of a method for correcting non homogeneities present in the background of MSCT images. Then, a segmentation stage using a clustering method allows to obtain the adenocarcinoma morphology. In the third stage, the pathology region is reconstructed and then visualized with a three–dimensional (3–D) computer graphics procedure based on marching cubes algorithm. In order to validate the segmentations, the Dice score is used as a metric function useful for comparing the segmentations obtained using the proposed method with respect to ground truth volumes traced by a clinician. Results: A total of 8 datasets available for patients diagnosed, from the cancer data collection of the project, Cancer Genome Atlas Stomach Adenocarcinoma (TCGASTAD) is considered in this research. The volume of the type 2 stomach tumor is estimated from the 3–D shape computationally segmented from the each dataset. These 3–D shapes are computationally reconstructed and then used to assess the morphopathology macroscopic features of this cancer. Conclusions: The segmentations obtained are useful for assessing qualitatively and quantitatively the stomach type 2 cancer. In addition, this type of segmentation allows the development of computational models that allow the planning of virtual surgical processes related to type 2 cancer.
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Chacón, Gerardo, Johel E. Rodríguez, Valmore Bermúdez, Miguel Vera, Juan Diego Hernández, Sandra Vargas, Aldo Pardo, Carlos Lameda, Delia Madriz, and Antonio J. Bravo. "Computational assessment of stomach tumor volume from multi-slice computerized tomography images in presence of type 2 cancer." F1000Research 7 (October 9, 2018): 1098. http://dx.doi.org/10.12688/f1000research.14491.2.
Повний текст джерелаАнотація:
Background: The multi–slice computerized tomography (MSCT) is a medical imaging modality that has been used to determine the size and location of the stomach cancer. Additionally, MSCT is considered the best modality for the staging of gastric cancer. One way to assess the type 2 cancer of stomach is by detecting the pathological structure with an image segmentation approach. The tumor segmentation of MSCT gastric cancer images enables the diagnosis of the disease condition, for a given patient, without using an invasive method as surgical intervention. Methods: This approach consists of three stages. The initial stage, an image enhancement, consists of a method for correcting non homogeneities present in the background of MSCT images. Then, a segmentation stage using a clustering method allows to obtain the adenocarcinoma morphology. In the third stage, the pathology region is reconstructed and then visualized with a three–dimensional (3–D) computer graphics procedure based on marching cubes algorithm. In order to validate the segmentations, the Dice score is used as a metric function useful for comparing the segmentations obtained using the proposed method with respect to ground truth volumes traced by a clinician. Results: A total of 8 datasets available for patients diagnosed, from the cancer data collection of the project, Cancer Genome Atlas Stomach Adenocarcinoma (TCGASTAD) is considered in this research. The volume of the type 2 stomach tumor is estimated from the 3–D shape computationally segmented from the each dataset. These 3–D shapes are computationally reconstructed and then used to assess the morphopathology macroscopic features of this cancer. Conclusions: The segmentations obtained are useful for assessing qualitatively and quantitatively the stomach type 2 cancer. In addition, this type of segmentation allows the development of computational models that allow the planning of virtual surgical processes related to type 2 cancer.
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Ma, Xiqi, Pengyu Zhang, Xiaofei Man, and Leming Ou. "A New Belt Ore Image Segmentation Method Based on the Convolutional Neural Network and the Image-Processing Technology." Minerals 10, no. 12 (December 11, 2020): 1115. http://dx.doi.org/10.3390/min10121115.
Повний текст джерелаАнотація:
In the field of mineral processing, an accurate image segmentation method is crucial for measuring the size distribution of run-of-mine ore on the conveyor belts in real time0The image-based measurement is considered to be real time, on-line, inexpensive, and non-intrusive. In this paper, a new belt ore image segmentation method was proposed based on a convolutional neural network and image processing technology. It consisted of a classification model and two segmentation algorithms. A total of 2880 images were collected as an original dataset from the process control system (PCS). The test images were processed using the proposed method, the PCS system, the coarse image segmentation (CIS) algorithm, and the fine image segmentation (FIS) algorithm, respectively. The segmentation results of each algorithm were compared with those of the manual segmentation. All empty belt images in the test images were accurately identified by our method. The maximum error between the segmentation results of our method and the results of manual segmentation is 5.61%. The proposed method can accurately identify the empty belt images and segment the coarse material images and mixed material images with high accuracy. Notably, it can be used as a brand new algorithm for belt ore image processing.
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Fajarianto, Gama Wisnu, Ahmad Hifdhul Abror, and Nur Hayatin. "Adaptif Range-Constrained Otsu Untuk Pemilihan Threshold Secara Otomatis Pada Histogram Citra Dengan Variansi Kelas Yang Tidak Seimbang." Register: Jurnal Ilmiah Teknologi Sistem Informasi 2, no. 1 (January 1, 2016): 6. http://dx.doi.org/10.26594/r.v2i1.439.
Повний текст джерелаАнотація:
Abstrak
Image Thresholding merupakan proses segmentasi untuk pemisahkan foreground dan background pada citra dengan cara membagi histogram citra menjadi dua kelas. Beberapa metode thresholding seperti Otsu dan Range-constrained Otsu menggunakan nilai variansi dari histogram untuk mendapatkan titik threshold, namun ketika menangani citra yang memiliki nilai variansi kelas foreground dan background tidak seimbang titik threshold yang dihasilkan kurang tepat. Paper ini mengusulkan metode Adaptif Range-constrained Otsu untuk mengatasi permasalahan variansi kelas yang tidak seimbang dengan cara
mencari kelas yang memiliki nilai variansi lebih besar, untuk mendapatkan titik threshold yang lebih tepat. Pengujian menggunakan 22 NDT image dengan evaluasi misclassification error rate dan metode perankingan menunjukkan metode ini menghasilkan rerata ME 0.1153. Sedangkan Otsu sebesar 0.1746. Nilai rerata ranking 3.55, selisih 0.05 dibanding Kittler III. Hasil ini menunjukkan metode yang diusulkan kompetitif, terutama untuk segmentasi citra yang memiliki variansi kelas tidak sama.
Kata kunci: segmentasi, thresholding, histogram, Otsu, Range-constrained.
Abstract
Image thresholding is segmentation process for separating foreground and background of an image by dividing image histogram into two classes. Several thresholding methods like Otsu and Rangeconstrained Otsu using the variance value of the histogram to get the threshold point, but when handling images that have unbalance class variance of the foreground and background produce less accurate threshold point. This paper proposes a method Adaptive Range-constrained Otsu to solve unbalance class variance problem by finding a class that has greater variance value to obtain more accurate threshold point. NDT testing using 22 images with misclassification error rate evaluation and ranking methods shows that this method results ME average of 0.1153, while Otsu method results 0.1746. The
rankings mean value is 3.55, which has the difference of 0.05 when compared with Kittler III. These results show that the proposed method is competitive, especially for image segmentation with different class variance.
Key word: segmentasi, thresholding, histogram, Otsu, Range-constrained.
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Tripathi, Rakesh, and Neelesh Gupta. "A Review on Segmentation Techniques in Large-Scale Remote Sensing Images." SMART MOVES JOURNAL IJOSCIENCE 4, no. 4 (April 20, 2018): 7. http://dx.doi.org/10.24113/ijoscience.v4i4.143.
Повний текст джерелаАнотація:
Information extraction is a very challenging task because remote sensing images are very complicated and can be influenced by many factors. The information we can derive from a remote sensing image mostly depends on the image segmentation results. Image segmentation is an important processing step in most image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation. Labeling different parts of the image has been a challenging aspect of image processing. Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. Various algorithms for automating the segmentation process have been proposed, tested and evaluated to find the most ideal algorithm to be used for different types of images. In this paper a review of basic image segmentation techniques of satellite images is presented.
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Yazdani, S., R. Yusof, A. Karimian, A. H. Riazi, and M. Bennamoun. "A Unified Framework for Brain Segmentation in MR Images." Computational and Mathematical Methods in Medicine 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/829893.
Повний текст джерелаАнотація:
Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets.
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Anbarasan, Kalaivani, and S. Chitrakala. "Clustering-Based Color Image Segmentation Using Local Maxima." International Journal of Intelligent Information Technologies 14, no. 1 (January 2018): 28–47. http://dx.doi.org/10.4018/ijiit.2018010103.
Повний текст джерелаАнотація:
Color image segmentation has contributed significantly to image analysis and retrieval of relevant images. Color image segmentation helps the end user subdivide user input images into unique homogenous regions of similar pixels, based on pixel property. The success of image analysis is largely owing to the reliability of segmentation. The automatic segmentation of a color image into accurate regions without over-segmentation is a tedious task. Our paper focuses on segmenting color images automatically into multiple regions accurately, based on the local maxima of the GLCM texture property, with pixels spatially clustered into identical regions. A novel Clustering-based Image Segmentation using Local Maxima (CBIS-LM) method is presented. Our proposed approach generates reliable, accurate and non-overlapping multiple regions for the given user input image. The segmented regions can be automatically annotated with distinct labels which, in turn, help retrieve relevant images based on image semantics.
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Wang, Caiqiong, Lei Zhao, Wangfei Zhang, Xiyun Mu, and Shitao Li. "Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning." PeerJ 10 (January 19, 2022): e12805. http://dx.doi.org/10.7717/peerj.12805.
Повний текст джерелаАнотація:
Abstract Polarimetric SAR (PolSAR) image segmentation is a key step in its interpretation. For the targets with time series changes, the single-temporal PolSAR image segmentation algorithm is difficult to provide correct segmentation results for its target recognition, time series analysis and other applications. For this, a new algorithm for multi-temporal PolSAR image segmentation is proposed in this paper. Firstly, the over-segmentation of single-temporal PolSAR images is carried out by the mean-shift algorithm, and the over-segmentation results of single-temporal PolSAR are combined to get the over-segmentation results of multi-temporal PolSAR images. Secondly, the edge detectors are constructed to extract the edge information of single-temporal PolSAR images and fuse them to get the edge fusion results of multi-temporal PolSAR images. Then, the similarity measurement matrix is constructed based on the over-segmentation results and edge fusion results of multi-temporal PolSAR images. Finally, the normalized cut criterion is used to complete the segmentation of multi-temporal PolSAR images. The performance of the proposed algorithm is verified based on three temporal PolSAR images of Radarsat-2, and compared with the segmentation algorithm of single-temporal PolSAR image. Experimental results revealed the following findings: (1) The proposed algorithm effectively realizes the segmentation of multi-temporal PolSAR images, and achieves ideal segmentation results. Moreover, the segmentation details are excellent, and the region consistency is good. The objects which can’t be distinguished by the single-temporal PolSAR image segmentation algorithm can be segmented. (2) The segmentation accuracy of the proposed multi-temporal algorithm is up to 86.5%, which is significantly higher than that of the single-temporal PolSAR image segmentation algorithm. In general, the segmentation result of proposed algorithm is closer to the optimal segmentation. The optimal segmentation of farmland parcel objects to meet the needs of agricultural production is realized. This lays a good foundation for the further interpretation of multi-temporal PolSAR image.
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Chandra De, Utpal, Madhabananda Das, Debashis Mishra, and Debashis Mishra. "Threshold based brain tumor image segmentation." International Journal of Engineering & Technology 7, no. 3 (August 22, 2018): 1801. http://dx.doi.org/10.14419/ijet.v7i3.12425.
Повний текст джерелаАнотація:
Image processing is most vital area of research and application in field of medical-imaging. Especially it is a major component in medical science. Starting from radiology to ultrasound (sonography), MRI, etc. in lots of area image is the only source of diagnosis process. Now-a-days, different types of devices are being introduced to capture the internal body parts in medical science to carry the diagnosis process correctly. However, due to various reasons, the captured images need to be tuned digitally to gain the more information. These processes involve noise reduction, segmentations, thresholding etc. . Image segmentation is a process to segment the target area of image to identify the area more prominently. There are different process are evolved to perform the segmentation process, one of which is Image thresholding. Moreover there are different tools are also introduce to perform this step of image thresholding. The recent introduced tool PSO is being used here to segment the MRI scans to identify the brain lesions using image thresholding technique.
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Liu, Ming, Shichao Chen, Fugang Lu, Mengdao Xing, and Jingbiao Wei. "Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion." Sensors 21, no. 5 (February 26, 2021): 1643. http://dx.doi.org/10.3390/s21051643.
Повний текст джерелаАнотація:
For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.
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Schmidt-Richberg, A., J. Fiehler, T. Illies, D. Möller, H. Handels, D. Säring, and N. D. Forkert. "Fuzzy-based Vascular Structure Enhancement in Time-of-Flight MRA Images for Improved Segmentation." Methods of Information in Medicine 50, no. 01 (2011): 74–83. http://dx.doi.org/10.3414/me10-02-0003.
Повний текст джерелаАнотація:
Summary Objectives: Cerebral vascular malformations might lead to strokes due to occurrence of ruptures. The rupture risk is highly related to the individual vascular anatomy. The 3D Time-of-Flight (TOF) MRA technique is a commonly used non-invasive imaging technique for exploration of the vascular anatomy. Several clinical applications require exact cerebrovascular segmentations from this image sequence. For this purpose, intensity-based segmentation approaches are widely used. Since small low-contrast vessels are often not detected, vesselness filter-based segmentation schemes have been proposed, which contrari-wise have problems detecting malformed vessels. In this paper, a fuzzy logic-based method for fusion of intensity and vesselness information is presented, allowing an improved segmentation of malformed and small vessels at preservation of advantages of both approaches. Methods: After preprocessing of a TOF dataset, the corresponding vesselness image is computed. The role of the fuzzy logic is to voxel-wisely fuse the intensity information from the TOF dataset with the corresponding vesselness information based on an analytically designed rule base. The resulting fuzzy parame ter image can then be used for improved cerebrovascular segmentation. Results: Six datasets, manually segmented by medical experts, were used for evaluation. Based on TOF, vesselness and fused fuzzy parameter images, the vessels of each patient were segmented using optimal thresholds computed by maximizing the agreement to manual segmentations using the Tanimoto coefficient. The results showed an overall improvement of 0.054 (fuzzy vs. TOF) and 0.079 (fuzzy vs. vesselness). Furthermore, the evaluation has shown that the method proposed yields better results than statistical Bayes classification. Conclusion: The proposed method can automatically fuse the benefits of intensity and vesselness information and can improve the results of following cerebrovascular segmentations.