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Hassan, Raghad Mahdie, and Luma Salal Hassan. "Assessments Image Segmentation Using Genetic Algorithm." Al-Furat Journal of Innovations in Electronics and Computer Engineering 3, no. 2 (July 18, 2024): 352–63. http://dx.doi.org/10.46649/fjiece.v3.2.23a.2.6.2024.
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Image segmentation is a crucial technique for processing images. It is a challenging task to process images, and the quality of the segmentation process affects the following assignments, which include classification, object recognition, feature extraction, and object detection.It's a significant phase of a system for computer vision. Image segmentation is the basic problem in many applications for image processing. Over time, image segmentation has gotten more challenging due to its extensive use in numerous applications. It is the procedure of segmenting the image into various areas by using a specific technique. There are many different ways for image segmentation.A new information parameter with a threshold basis for segmenting images using the genetic algorithm. Due to its ability to calculate the ideal number of segmentation regions, we employed the Genetic Algorithm. In this work, a novel approach built upona genetic algorithm is used to solve the image segmentation problem by utilizing the thresholding concept.The suggested method uses a genetic algorithm to identify the evolutionary best segmented image based on a threshold that is based on new information. We presented the results of our experiments using the suggested method on various grayscale images in the last section. By using parameters used to evaluate image segmentation quality (PSNR,MSE,SC), we notice the results are good.
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Stevens, Michiel, Afroditi Nanou, Leon W. M. M. Terstappen, Christiane Driemel, Nikolas H. Stoecklein, and Frank A. W. Coumans. "StarDist Image Segmentation Improves Circulating Tumor Cell Detection." Cancers 14, no. 12 (June 13, 2022): 2916. http://dx.doi.org/10.3390/cancers14122916.
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After a CellSearch-processed circulating tumor cell (CTC) sample is imaged, a segmentation algorithm selects nucleic acid positive (DAPI+), cytokeratin-phycoerythrin expressing (CK-PE+) events for further review by an operator. Failures in this segmentation can result in missed CTCs. The CellSearch segmentation algorithm was not designed to handle samples with high cell density, such as diagnostic leukapheresis (DLA) samples. Here, we evaluate deep-learning-based segmentation method StarDist as an alternative to the CellSearch segmentation. CellSearch image archives from 533 whole blood samples and 601 DLA samples were segmented using CellSearch and StarDist and inspected visually. In 442 blood samples from cancer patients, StarDist segmented 99.95% of CTC segmented by CellSearch, produced good outlines for 98.3% of these CTC, and segmented 10% more CTC than CellSearch. Visual inspection of the segmentations of DLA images showed that StarDist continues to perform well when the cell density is very high, whereas CellSearch failed and generated extremely large segmentations (up to 52% of the sample surface). Moreover, in a detailed examination of seven DLA samples, StarDist segmented 20% more CTC than CellSearch. Segmentation is a critical first step for CTC enumeration in dense samples and StarDist segmentation convincingly outperformed CellSearch segmentation.
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Rahman, Fathur, Nuzul Hikmah, and Misdiyanto Misdiyanto. "Analysis Influence Segmentation Image on Classification Image X-raylungs with Method Convolutional Neural." Journal of Informatics Development 2, no. 1 (October 30, 2023): 23–29. http://dx.doi.org/10.30741/jid.v2i1.1159.
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The impact of image segmentation on the classification of lung X-ray images using Convolutional Neural Networks (CNNs) has been scrutinized in this study. The dataset used in this research comprises 150 lung X-ray images, distributed as 78 for training, 30 for validation, and 42 for testing. Initially, image data undergoes preprocessing to enhance image quality, employing adaptive histogram equalization to augment contrast and enhance image details. The evaluation of segmentation's influence is based on a comparison between image classification with and without the segmentation process. Segmentation involves the delineation of lung regions through techniques like thresholding, accompanied by various morphological operations such as hole filling, area opening, and labeling. The image classification process employs a CNN featuring 5 convolution layers, the Adam optimizer, and a training period of 30 epochs. The results of this study indicate that the X-ray image dataset achieved a classification accuracy of 59.52% in network testing without segmentation. In contrast, when segmentation was applied to the X-ray image dataset, the accuracy significantly improved to 73.81%. This underscores the segmentation process's ability to enhance network performance, as it simplifies the classification of segmented image patterns.
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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.
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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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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.
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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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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.
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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.
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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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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.
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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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Ding, Ruiyao. "Segmentation analysis of UAV images based on Unet deep learning algorithm." Applied and Computational Engineering 54, no. 1 (March 29, 2024): 248–53. http://dx.doi.org/10.54254/2755-2721/54/20241644.
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The continuous development of UAV technology provides us with more and higher quality data, in which the application of UAV image segmentation technology can help us better understand and process these data. Traditional image segmentation methods can no longer meet the needs of UAV image segmentation, so researchers have begun to explore the application of deep learning methods in UAV image segmentation.U-Net, as a classical deep learning model, is also widely used in UAV image segmentation.U-Net is characterized by two parts: encoder and decoder, which are used to extract the image features, and decoder is used to map the features back to the original image size. features to map back to the original image size. UAV image segmentation technology can be applied in agriculture, urban planning, environmental monitoring and other fields. In the field of agriculture, UAV image segmentation can help farmers better manage and monitor farmland to improve crop yield and quality. In the field of urban planning, UAV image segmentation can help urban planners better understand the development status of the city and provide a more scientific basis for urban planning. In the field of environmental monitoring, UAV image segmentation can help us better monitor the changes in the natural environment and provide more effective means for environmental protection. By setting the ratio of training set, validation set and test set as 6:2:2 and performing 100 rounds of training, the U-Net model shows good results in UAV image segmentation. The loss of the model gradually stabilizes at 0.2979 and the accuracy gradually converges to 90.34%. The test results show that the prediction results are very close to the true mask, indicating that the U-Net model can segment UAV images well. The application of UAV image segmentation technology can help us better understand and process the data acquired by UAV and provide more information and basis. In the future, with the continuous development of UAV technology, UAV image segmentation technology will also be more widely used. Through the application of UAV image segmentation technology, we will better understand and protect our environment, manage our farmland more efficiently, and plan our cities more scientifically.
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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.
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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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Tatyankin, Vitaly M., and Irina S. Dyubko. "Image segmentation." Yugra State University Bulletin 11, no. 2 (June 15, 2015): 99–101. http://dx.doi.org/10.17816/byusu201511299-101.
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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.
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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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Bharti, Tanwar* Rakesh Kumar Girdhar Gopal. "IMPACT OF IMAGE SEGMENTATION APPROACHES ON NOISY AND BLURRED IMAGES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 6 (June 5, 2016): 354–61. https://doi.org/10.5281/zenodo.54854.
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In today’s world, many applications are used for image processing. Segmentation is one of the main steps used for image processing. Segmentation is used to identify objects in an image. It divides an image into multiple segmentations. There are hundreds of techniques present that are used to segment an image. Clustering is one of the techniques. K nearest neighbor and K-mean techniques are two clustering techniques of segmentation. The main principle of clustering technique is to make cluster of pixels on the basis of distance between pixels and centroids. This paper gives details about KNN and K-mean techniques and their efficiency. In past, these images are applied a lot on smooth images. In this paper, the efforts have been done to analyze the impact of these techniques on various non-efficient images. The functions used to make the images degraded are noise and blur in the image. It has been observed that depending on various parameters both the algorithms performs in similar manner except some differences which are highlighted in this paper.
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Jain, Raunak, Faith Lee, Nianhe Luo, Harpreet Hyare, and Anand S. Pandit. "A Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation." NeuroSci 5, no. 3 (August 2, 2024): 265–75. http://dx.doi.org/10.3390/neurosci5030021.
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The purpose of the article is to provide a practical guide for manual and semi-automated image segmentation of common neurosurgical cranial lesions, namely meningioma, glioblastoma multiforme (GBM) and subarachnoid haemorrhage (SAH), for neurosurgical trainees and researchers. Materials and Methods: The medical images used were sourced from the Medical Image Computing and Computer Assisted Interventions Society (MICCAI) Multimodal Brain Tumour Segmentation Challenge (BRATS) image database and from the local Picture Archival and Communication System (PACS) record with consent. Image pre-processing was carried out using MRIcron software (v1.0.20190902). ITK-SNAP (v3.8.0) was used in this guideline due to its availability and powerful built-in segmentation tools, although others (Seg3D, Freesurfer and 3D Slicer) are available. Quality control was achieved by employing expert segmenters to review. Results: A pipeline was developed to demonstrate the pre-processing and manual and semi-automated segmentation of patient images for each cranial lesion, accompanied by image guidance and video recordings. Three sample segmentations were generated to illustrate potential challenges. Advice and solutions were provided within both text and video. Conclusions: Semi-automated segmentation methods enhance efficiency, increase reproducibility, and are suitable to be incorporated into future clinical practise. However, manual segmentation remains a highly effective technique in specific circumstances and provides initial training sets for the development of more advanced semi- and fully automated segmentation algorithms.
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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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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.
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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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Cardone, Barbara, Ferdinando Di Martino, and Vittorio Miraglia. "A Novel Fuzzy-Based Remote Sensing Image Segmentation Method." Sensors 23, no. 24 (December 5, 2023): 9641. http://dx.doi.org/10.3390/s23249641.
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Image segmentation is a well-known image processing task that consists of partitioning an image into homogeneous areas. It is applied to remotely sensed imagery for many problems such as land use classification and landscape changes. Recently, several hybrid remote sensing image segmentation techniques have been proposed that include metaheuristic approaches in order to increase the segmentation accuracy; however, the critical point of these approaches is the high computational complexity, which affects time and memory consumption. In order to overcome this criticality, we propose a fuzzy-based image segmentation framework implemented in a GIS-based platform for remotely sensed images; furthermore, the proposed model allows us to evaluate the reliability of the segmentation. The Fast Generalized Fuzzy c-means algorithm is implemented to segment images in order to detect local spatial relations between pixels and the Triple Center Relation validity index is used to find the optimal number of clusters. The framework elaborates the composite index to be analyzed starting by multiband remotely sensed images. For each cluster, a segmented image is obtained in which the pixel value represents, transformed into gray levels, the graph belonging to the cluster. A final thematic map is built in which the pixels are classified based on the assignment to the cluster to which they belong with the highest membership degree. In addition, the reliability of the classification is estimated by associating each class with the average of the membership degrees of the pixels assigned to it. The method was tested in the study area consisting of the south-western districts of the city of Naples (Italy) for the segmentation of composite indices maps determined by multiband remote sensing images. The segmentation results are consistent with the segmentations of the study area by morphological and urban characteristics, carried out by domain experts. The high computational speed of the proposed image segmentation method allows it to be applied to massive high-resolution remote sensing images.
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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.
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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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Bai, Shurui, Zhuo Deng, Jingyan Yang, Zheng Gong, Weihao Gao, Lei Shao, Fang Li, Wenbin Wei, and Lan Ma. "FTSNet: Fundus Tumor Segmentation Network on Multiple Scales Guided by Classification Results and Prompts." Bioengineering 11, no. 9 (September 22, 2024): 950. http://dx.doi.org/10.3390/bioengineering11090950.
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The segmentation of fundus tumors is critical for ophthalmic diagnosis and treatment, yet it presents unique challenges due to the variability in lesion size and shape. Our study introduces Fundus Tumor Segmentation Network (FTSNet), a novel segmentation network designed to address these challenges by leveraging classification results and prompt learning. Our key innovation is the multiscale feature extractor and the dynamic prompt head. Multiscale feature extractors are proficient in eliciting a spectrum of feature information from the original image across disparate scales. This proficiency is fundamental for deciphering the subtle details and patterns embedded in the image at multiple levels of granularity. Meanwhile, a dynamic prompt head is engineered to engender bespoke segmentation heads for each image, customizing the segmentation process to align with the distinctive attributes of the image under consideration. We also present the Fundus Tumor Segmentation (FTS) dataset, comprising 254 pairs of fundus images with tumor lesions and reference segmentations. Experiments demonstrate FTSNet’s superior performance over existing methods, achieving a mean Intersection over Union (mIoU) of 0.8254 and mean Dice (mDice) of 0.9042. The results highlight the potential of our approach in advancing the accuracy and efficiency of fundus tumor segmentation.
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Gaikwad, Akshay V., and Suyash Awate. "Deep Monte-Carlo EM for Semantic Segmentation using Weakly-and-Semi-Supervised Learning Using Very Few Expert Segmentations." Machine Learning for Biomedical Imaging 2, June 2024 (June 14, 2024): 717–60. http://dx.doi.org/10.59275/j.melba.2024-2fgd.
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Typical methods for semantic image segmentation rely on large training sets comprising per-pixel semantic segmentations. In medical-imaging applications, obtaining a large number of expert segmentations can be difficult because of the underlying demands on the experts’ time and the budget. However, in many such applications, it is much easier to obtain image-level information indicating the class labels of the objects of interest present in the image. We propose a novel deep-neural-network (DNN) framework for the semantic segmentation of images relying on weakly-and-semi-supervised learning from a training set comprising (i) very few images having per-pixel semantic segmentations and (ii) all images having class labels for the objects of interest present within. To enable weakly-and-semi-supervised learning, our framework proposes to couple the tasks of semantic segmentation and image classification, by incorporating a semantic-segmenter DNN followed by a translator DNN with end-to-end learning. We propose variational learning relying on Monte-Carlo expectation maximization, infering a posterior distribution on the hidden variable that models the segmenter-DNN’s latent space. We propose a Metropolis-Hastings sampler for the posterior distribution, along with sample reparametrizations to enable end-to-end backpropagation. Results on three publicly available real-world microscopy datasets show the benefits of our framework over existing methods, along with empirical insights into the workings of various approaches.
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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.
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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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Arora, Jyoti, and Meena Tushir. "Intuitionistic Level Set Segmentation for Medical Image Segmentation." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 1039–46. http://dx.doi.org/10.2174/2213275912666190218150045.
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Introduction: Image segmentation is one of the basic practices that involve dividing an image into mutually exclusive partitions. Learning how to partition an image into different segments is considered as one of the most critical and crucial step in the area of medical image analysis. Objective: The primary objective of the work is to design an integrated approach for automating the process of level set segmentation for medical image segmentation. This method will help to overcome the problem of manual initialization of parameters. Methods: In the proposed method, input image is simplified by the process of intuitionistic fuzzification of an image. Further segmentation is done by intuitionistic based clustering technique incorporated with local spatial information (S-IFCM). The controlling parameters of level set method are automated by S-IFCM, for defining anatomical boundaries. Results: Experimental results were carried out on MRI and CT-scan images of brain and liver. The results are compared with existing Fuzzy Level set segmentation; Spatial Fuzzy Level set segmentation using MSE, PSNR and Segmentation Accuracy. Qualitatively results achieved after proposed segmentation technique shows more clear definition of boundaries. The attain PSNR and MSE value of propose algorithm proves the robustness of algorithm. Segmentation accuracy is calculated for the segmentation results of the T-1 weighted axial slice of MRI image with 0.909 value. Conclusion: The proposed method shows good accuracy for the segmentation of medical images. This method is a good substitute for the segmentation of different clinical images with different modalities and proves to give better result than fuzzy technique.
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Ikokou, Guy Blanchard, and Kate Miranda Malale. "Unsupervised Image Segmentation Parameters Evaluation for Urban Land Use/Land Cover Applications." Geomatics 4, no. 2 (May 12, 2024): 149–72. http://dx.doi.org/10.3390/geomatics4020009.
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Image segmentation plays an important role in object-based classification. An optimal image segmentation should result in objects being internally homogeneous and, at the same time, distinct from one another. Strategies that assess the quality of image segmentation through intra- and inter-segment homogeneity metrics cannot always predict possible under- and over-segmentations of the image. Although the segmentation scale parameter determines the size of the image segments, it cannot synchronously guarantee that the produced image segments are internally homogeneous and spatially distinct from their neighbors. The majority of image segmentation assessment methods largely rely on a spatial autocorrelation measure that makes the global objective function fluctuate irregularly, resulting in the image variance increasing drastically toward the end of the segmentation. This paper relied on a series of image segmentations to test a more stable image variance measure based on the standard deviation model as well as a more robust hybrid spatial autocorrelation measure based on the current Moran’s index and the spatial autocorrelation coefficient models. The results show that there is a positive and inversely proportional correlation between the inter-segment heterogeneity and the intra-segment homogeneity since the global heterogeneity measure increases with a decrease in the image variance measure. It was also found that medium-scale parameters produced better quality image segments when used with small color weights, while large-scale parameters produced good quality segments when used with large color factor weights. Moreover, with optimal segmentation parameters, the image autocorrelation measure stabilizes and follows a near horizontal fluctuation while the image variance drops to values very close to zero, preventing the heterogeneity function from fluctuating irregularly towards the end of the image segmentation process.
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Xu, Yuhang. "Application of Image Segmentation Algorithms in Computer Vision." Frontiers in Computing and Intelligent Systems 7, no. 3 (April 10, 2024): 17–20. http://dx.doi.org/10.54097/gq1s6737.
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In the field of computer vision (CV), image segmentation technology, as a fundamental part, has a crucial impact on the accuracy of subsequent image processing tasks. Image segmentation is not only a crucial transitional step from image processing to image analysis, but also a hot and difficult research topic in the field of CV. Although significant progress has been made in the research of image segmentation algorithms, existing segmentation algorithms may still face challenges in certain specific scenarios due to the complexity and diversity of images, making it difficult to achieve ideal segmentation results. In recent years, the rapid development of deep learning (DL) technology has brought new breakthroughs to the field of image segmentation. DL models, especially Convolutional Neural Networks (CNNs), can capture semantic information of images more accurately by automatically learning feature representations in images, thereby achieving more precise image segmentation. This article delves into the research and application of image segmentation algorithms in CV, with a focus on the application of DL in the field of image segmentation. With the continuous development of advanced technologies such as DL, it is believed that image segmentation technology will play a greater role in more fields in the future.
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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.
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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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Zhou, Zhi Heng, and Hui Qiang Zhong. "Image Segmentation Based on Poisson Equation." Applied Mechanics and Materials 284-287 (January 2013): 3131–34. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3131.
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Image segmentation is an important part of the image processing. Currently, image segmentation methods are mainly the threshold-based segmentation method, the region-based segmentation method, the edge-based segmentation method and the Snake model based on energy function etc. This paper presents a novel image segmentation method based on the Poisson equation. The goal of the segmentation method is to divide the image into two homogeneous parts, the boundary portion and the non-boundary portion, which have similar gray values in homogeneous part. The key of the method is to build a Poisson equation with Dirichlet boundary condition. It sets a gradient threshold as the Dirichlet boundary condition of the Poisson equation, and gets a binary image by retaining the image boundary and smoothing the non-image boundary. Then simple binary segmentation will be able to get the image boundary. The experimental results show that this segmentation method can get accurate image boundaries for non-noise images and the weak noise images.
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Channappa A. "Comparative analysis of image segmentation algorithms for pattern recognition." World Journal of Advanced Research and Reviews 5, no. 3 (March 30, 2020): 193–99. https://doi.org/10.30574/wjarr.2020.5.3.0048.
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Image Segmentation is one of the hopeful and emerging fields in image processing. The examined algorithms are compared according to the quality of its operation at distorted images with respect to ground-truth images. It has applications in various fields like medical applications, astronomical, traffic controlling, Fingerprint recognition, digital forensics, self-driving motor cars, locating objects in satellite images etc. It is the process of splitting an image into sub regions with respect to one or more characteristics. Image segmentation is the basic step to analyse images and extract data from them. In the image segmentation process, the anatomical organization or the region of interest needs to be defined and extracted out so that it can be viewed independently. In this comparative study we venture the significant place of segmentation of images in pulling out information for decision making. Hence characterization, visualization of region of interest in any image, delineation plays an important role in image segmentation. Using the different algorithms the current methodologies of image segmentation is reviewed so that user interaction is possible for images. Image segmentation results have an effect on image analysis and it following higher order tasks. Image analysis includes object description and representation, feature measurement. Image segmentation based on Region Based, Edge Detection, Thresholding, Clustering, Fuzzy Logic and Neural Network are discussed and compared.
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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.
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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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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.
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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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Priego, Torres Blanca María, and Richard J. Duro. "An approach for the customized high-dimensional segmentation of remote sensing hyperspectral images." Sensors 19, no. 13 (June 29, 2019): 2887. https://doi.org/10.3390/s19132887.
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The work presents a methodology for the customized segmentation of remote sensing hyperspectral images using a multigradient cellular automaton (MGCA) approach coupled with an evolutionary algorithm (ECAS-II). The study addresses three main challenges in hyperspectral image segmentation: the need for segmentations tailored to user requirements, the scarcity of adequately labeled reference images, and the loss of information that occurs when high-dimensional images are projected into lower-dimensional spaces before segmentation. The proposed methodology allows for the segmentation of multidimensional images without reducing their dimensionality, preserving the richness of spectral information. This approach is validated through experiments with synthetic and real images, demonstrating a significant improvement in segmentation accuracy compared to traditional methods. The proposed approach not only reduces dependency on large labeled datasets but also facilitates the adaptation of the segmentation process to specific user needs, using a reduced set of low-dimensional training images to generate high-dimensional segmenters. This personalized approach has potential applications in remote sensing, environmental monitoring, and natural resource management, where precise and context-specific segmentations are required.
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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.
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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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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.
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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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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.
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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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MEZARIS, VASILEIOS, IOANNIS KOMPATSIARIS, and MICHAEL G. STRINTZIS. "STILL IMAGE SEGMENTATION TOOLS FOR OBJECT-BASED MULTIMEDIA APPLICATIONS." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 04 (June 2004): 701–25. http://dx.doi.org/10.1142/s0218001404003393.
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In this paper, a color image segmentation algorithm and an approach to large-format image segmentation are presented, both focused on breaking down images to semantic objects for object-based multimedia applications. The proposed color image segmentation algorithm performs the segmentation in the combined intensity–texture–position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. A preprocessing stage of conditional image filtering and a modified K-Means-with-connectivity-constraint pixel classification algorithm are used to allow for seamless integration of the different pixel features. Unsupervised operation of the segmentation algorithm is enabled by means of an initial clustering procedure. The large-format image segmentation scheme employs the aforementioned segmentation algorithm, providing an elegant framework for the fast segmentation of relatively large images. In this framework, the segmentation algorithm is applied to reduced versions of the original images, in order to speed-up the completion of the segmentation, resulting in a coarse-grained segmentation mask. The final fine-grained segmentation mask is produced with partial reclassification of the pixels of the original image to the already formed regions, using a Bayes classifier. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.
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Gao, Song, Chengcui Zhang, and Wei-Bang Chen. "Color Image Segmentation." International Journal of Multimedia Data Engineering and Management 3, no. 3 (July 2012): 66–82. http://dx.doi.org/10.4018/jmdem.2012070104.
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An intuitive way of color image segmentation is through clustering in which each pixel in an image is treated as a data point in the feature space. A feature space is effective if it can provide high distinguishability among objects in images. Typically, in the preprocessing phase, various modalities or feature spaces are considered, such as color, texture, intensity, and spatial information. Feature selection or reduction can also be understood as transforming the original feature space into a more distinguishable space (or subspaces) for distinguishing different content in an image. Most clustering-based image segmentation algorithms work in the full feature space while considering the tradeoff between efficiency and effectiveness. The authors’ observation indicates that often time objects in images can be simply detected by applying clustering algorithms in subspaces. In this paper, they propose an image segmentation framework, named Hill-Climbing based Projective Clustering (HCPC), which utilizes EPCH (an efficient projective clustering technique by histogram construction) as the core framework and Hill-Climbing K-means (HC) for dense region detection, and thereby being able to distinguish image contents within subspaces of a given feature space. Moreover, a new feature space, named HSVrVgVb, is also explored which is derived from Hue, Saturation, and Value (HSV) color space. The scalability of the proposed algorithm is linear to the dimensionality of the feature space, and our segmentation results outperform that of HC and other projective clustering-based algorithms.
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Muniappan, Ramaraj, Thiruvenkadam Thangavel, Govindaraj Manivasagam, Dhendapani Sabareeswaran, Nainan Thangarasu, Chembath Jothish, and Bhaarathi Ilango. "Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (June 1, 2024): 2803. http://dx.doi.org/10.11591/ijece.v14i3.pp2803-2813.
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In the empire of image processing and computer vision, the demand for advanced segmentation techniques has intensified with the growing complexity of visual data. This study focuses on the innovative paradigm of fuzzy mountain-based image segmentation, a method that harnesses the power of fuzzy logic and topographical inspiration to achieve nuanced and adaptable delineation of image regions. This research primarily concentrates on determining the age of tigers, a critical and challenging task in the current scenario. The primary objectives include the development of a comprehensive framework for FMBIS and an in-depth investigation into its adaptability to different image characteristics. This research work incorporates those domains of image processing and data mining to predict the age of the tiger using different kinds of color images. Fuzzy mountain-based pixel segmentation arises from the need to capture the subtle gradients and uncertainties present in images, offering a novel approach to achieving high-fidelity segmentations in diverse and complex scenarios. The proposed methods enable image enhancement and filtering and are then assessed during process time, retrieval time, to give a more accurate and reduced error rate for producing higher results for real-time tiger image database.
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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.
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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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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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MOSTOVYI, V., and S. HORIASHCHENKO. "SEGMENTATION OF MEDICAL IMAGES." Herald of Khmelnytskyi National University. Technical sciences 289, no. 5 (November 26, 2020): 51–56. https://doi.org/10.31891/2307-5732-2020-289-5-51-56.
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Segmentation is an integral part of the digital image processing process. It is the division or division of the image into some parts that meet the specified characteristics and characterize these areas and the image as a whole. At the segmentation stage, issues are solved that complement the standard tasks of image processing, namely coding, restoration, quality improvement. The segmentation process is considered an integral part of the tasks of image recognition, classification and identification. That is why segmentation has found its wide application in such areas as microbiology, medicine, astronomy, military equipment and other areas of human life. Such research also helps psychologists and physiologists to study such processes as the perception of forms, learning and recognition of objects by living organisms and the human brain, and so on. Segmentation is widely used in the automation of microscopic examinations of various medical objects, which include the processing of images of cells of organisms and their components and hemocytological drugs. This process is an integral part of recognition and classification in medical diagnostics. Recently, work has begun on the complete automation of the process of segmentation of images of biological objects in order to increase the reliability of the diagnosis of various diseases. The information obtained as a result of segmentation is also used to identify the effects of various adverse factors and helps to predict the course of leukemia, lymphosarcoma, anemia and other diseases of the human body. The article considers and analyzes the possibility of applying segmentation methods based on signs of connectivity for other types of images, conducted an analytical review and classification of known segmentation methods, based on which the requirements for developing structural models for segmentation of microscopic medical images, substantiated the relevance of the feature connections on segmentation problems and its mathematical models are built.
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Li, Bing, Shaoyong Wu, Siqin Zhang, Xia Liu, and Guangqing Li. "Fast Segmentation of Vertebrae CT Image Based on the SNIC Algorithm." Tomography 8, no. 1 (January 3, 2022): 59–76. http://dx.doi.org/10.3390/tomography8010006.
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Automatic image segmentation plays an important role in the fields of medical image processing so that these fields constantly put forward higher requirements for the accuracy and speed of segmentation. In order to improve the speed and performance of the segmentation algorithm of medical images, we propose a medical image segmentation algorithm based on simple non-iterative clustering (SNIC). Firstly, obtain the feature map of the image by extracting the texture information of it with feature extraction algorithm; Secondly, reduce the image to a quarter of the original image size by downscaling; Then, the SNIC super-pixel algorithm with texture information and adaptive parameters which used to segment the downscaling image to obtain the superpixel mark map; Finally, restore the superpixel labeled image to the original size through the idea of the nearest neighbor algorithm. Experimental results show that the algorithm uses an improved superpixel segmentation method on downscaling images, which can increase the segmentation speed when segmenting medical images, while ensuring excellent segmentation accuracy.
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R, Asharani, and Naveen Kumar R. "Review on Brain Tumor Image Segmentation in Time-Frequency Domain." Journal of Image Processing and Artificial Intelligence 8, no. 3 (September 20, 2022): 1–6. http://dx.doi.org/10.46610/joipai.2022.v08i03.001.
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The progressive image segmentation is one of the necessary stages in image acquisition and recognition for an effective identification of brain tumor in advanced medical equipment’s, any image segmentation algorithms working effectively in distinguishing impaired and malignant information from tomographic images through various classification techniques. There is an ambiguity in segmentation for effective regeneration of disseminated information during investigation and extraction of features like shape, volume, and motions of organs from medical images is essential. Current research in medical imaging is aimed at developing automated image recognition and diagnostic systems, which require efficient image segmentation and quantification tools. This paper made an effort to realize the Time-frequency method of image segmentation and reviewing the findings of existing Medical segmentation techniques for medical images.
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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.
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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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Zhou, Yang, Lijuan Zhu, and Dong Ma. "Research on Traditional Image Segmentation Method Based on Oil Drilling Pipe Defects." Journal of Physics: Conference Series 2639, no. 1 (November 1, 2023): 012057. http://dx.doi.org/10.1088/1742-6596/2639/1/012057.
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Abstract This study explores the feasibility and efficacy of conventional image seg-mentation technology in diagnosing failures in oil drilling pipe images. Simultaneously, it envisions an intelligent approach to diagnose defects in oil drilling pipes. The present paper examines and scrutinizes traditional image segmentation methods in light of the characteristics of oil drilling pipe defect images. It devises experiments tailored for these defect images and employs various traditional image segmentation methods to facilitate comparison and evaluation. The experimental findings illustrate that the traditional image segmentation methods possess a discernible impact on detecting defects in oil drilling pipes, with the image segmentation effect based on the Canny operator method of edge detection proving to be the most effective. The experiments are specifically devised for defective images of oil drilling pipes, utilizing diverse traditional image segmentation methods for comparison and evaluation. The experimental results demonstrate that the traditional image segmentation methods exhibit a certain degree of efficacy in detecting defects in oil drilling pipes, with the image segmentation effect based on the Canny operator method of edge detection being the most optimal.
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Khudov, Hennadii, Oleksandr Makoveichuk, Irina Khizhnyak, Sergey Glukhov, Nazar Shamrai, Serhii Rudnichenko, Maksym Husak, and Rostyslav Khudov. "The Choice of Quality Indicator for the Image Segmentation Evaluation." International Journal of Emerging Technology and Advanced Engineering 12, no. 10 (October 1, 2022): 95–103. http://dx.doi.org/10.46338/ijetae1022_11.
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In this paper, the main attention is focused on the stage of image segmentation and on the choice of a quality indicator for the evaluation of image segmentation. In general form the segmentation problem of color and tone images is formulated in. The main approaches that are used in methods and techniques of image segmentation are highlighted. The need to evaluate the results of the work of methods and techniques of image segmentation to assess their performance has been established. The options for the image segmentation quality assessing of both at the objective (quantitative) level and at the subjective (qualitative) level are considered. The main features of qualitative segmentation are highlighted. Two main groups of indicators for evaluating the quality of segmentation were analyzed: indicators based on comparison with the reference result of image segmentation and the indicators for which benchmark segmentation is not required. Their main disadvantages when used in evaluating the results of image segmentation are described. The information quality indicator – Kullback-Leibner divergence for the image segmentation quality assessing of the results of image segmentation is considered. The result of image segmentation by the Canny edge detector is presented. The estimation of the quality of the segmented image was calculated using the information Kullback-Leibner divergence for pairs of images of different scales. The paper shows show a graph of the dependence of the value of the Kullback-Leibler divergence on the value of the scale factor of the original image. Keywords— Canny edge detector, image segmentation, objective criteria, Kullback-Leibler divergence, quality indicator.
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Tan, Jianquan, Wenrui Zhou, Ling Lin, and Huxidan Jumahong. "A Review of Semantic Medical Image Segmentation Based on Different Paradigms." International Journal on Semantic Web and Information Systems 20, no. 1 (June 6, 2024): 1–25. http://dx.doi.org/10.4018/ijswis.345246.
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In recent years, with the widespread application of medical images, the rapid and accurate identification of these regions of interest in a large number of medical images has received widespread attention. This article provides a review of medical image segmentation methods based on deep learning. Firstly, an overview of medical image segmentation methods was provided in the relevant knowledge, segmentation types, segmentation processes, and image processing applications. Secondly, the applications of supervised, semi supervised, and unsupervised methods in medical image segmentation were discussed, and their advantages, disadvantages, and applicable scenarios were revealed through the application of a large number of specific segmentation examples in practical scenarios. Finally, the commonly used medical image segmentation datasets and evaluation indicators were introduced, and the current medical image segmentation methods were summarized and prospected. This review provides a comprehensive and in-depth understanding for researchers in the field of medical image segmentation, and provides valuable references for the design and implementation of future related work.
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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.
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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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Tian, Yan, Chong Wu Ruan, and Chen Hong Sui. "Study on the Optimal Image Resolution for Image Segmentation." Applied Mechanics and Materials 665 (October 2014): 724–32. http://dx.doi.org/10.4028/www.scientific.net/amm.665.724.
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Resolution is one of the basic and key indexes on assessing the quality of remote sensing image. However, it can not be concluded that the higher the image resolution, the better the segmentation result, since high resolution image contains not only more details of interested object, but also more redundant information of background which causes much difficulty on image segmentation and target recognition. To determine an optimal image resolution for image segmentation, an image pyramid with resolution continuously changing is built by down sampling and super-resolution techniques at first, and then an index called degree of image segmentation is presented based on the image histogram. Degree of image segmentation is a hybrid index which is designed based on integrating the area and symmetry of the valley of the image histogram. At last the optimal image resolution is determined by seeking the maximum value of degree of image segmentation from the images with different resolutions contained in the image pyramid. The experimental results illustrate that degree of image segmentation is directly related with the result of segmentation, and the degree of image segmentation presented in this paper is a good index to describe how well an image can be segmented in the viewpoint of quantitative and qualitative assessing.
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Hao, Shuang, Yuhuan Cui, and Jie Wang. "Segmentation Scale Effect Analysis in the Object-Oriented Method of High-Spatial-Resolution Image Classification." Sensors 21, no. 23 (November 28, 2021): 7935. http://dx.doi.org/10.3390/s21237935.
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High-spatial-resolution images play an important role in land cover classification, and object-based image analysis (OBIA) presents a good method of processing high-spatial-resolution images. Segmentation, as the most important premise of OBIA, significantly affects the image classification and target recognition results. However, scale selection for image segmentation is difficult and complicated for OBIA. The main challenge in image segmentation is the selection of the optimal segmentation parameters and an algorithm that can effectively extract the image information. This paper presents an approach that can effectively select an optimal segmentation scale based on land object average areas. First, 20 different segmentation scales were used for image segmentation. Next, the classification and regression tree model (CART) was used for image classification based on 20 different segmentation results, where four types of features were calculated and used, including image spectral bands value, texture value, vegetation indices, and spatial feature indices, respectively. WorldView-3 images were used as the experimental data to verify the validity of the proposed method for the selection of the optimal segmentation scale parameter. In order to decide the effect of the segmentation scale on the object area level, the average areas of different land objects were estimated based on the classification results. Experiments based on the multi-scale segmentation scale testify to the validity of the land object’s average area-based method for the selection of optimal segmentation scale parameters. The study results indicated that segmentation scales are strongly correlated with an object’s average area, and thus, the optimal segmentation scale of every land object can be obtained. In this regard, we conclude that the area-based segmentation scale selection method is suitable to determine optimal segmentation parameters for different land objects. We hope the segmentation scale selection method used in this study can be further extended and used for different image segmentation algorithms.
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Song, Yinglei, Benjamin Adobah, Junfeng Qu, and Chunmei Liu. "Segmentation of Ordinary Images and Medical Images with an Adaptive Hidden Markov Model and Viterbi Algorithm." Current Signal Transduction Therapy 15, no. 2 (December 1, 2020): 109–23. http://dx.doi.org/10.2174/1574362413666181109113834.
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Background: Image segmentation is an important problem in both image processing and computer vision. Given an image, the goal of image segmentation is to label each pixel in the image such that the pixels with the same label collectively represent an object. Materials and Methods: Due to the inherent complexity and noise that may exist in images, developing an algorithm that can generate excellent segmentation results for an arbitrary image is still a challenging problem. In this paper, a new adaptive Hidden Markov Model is developed to describe the spatial and semantic relationships among pixels in an image. Based on this statistical model, image segmentation can be efficiently performed with an adaptive Viterbi algorithm in linear time. Results: The algorithm is unsupervised and does not require being used along with any other approach in image segmentation. Testing results on synthetic and real images show that this algorithm is able to achieve excellent segmentation results in both ordinary images and medical images. Conclusion: An implementation of this algorithm in MATLAB is freely available upon request.
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Sharma, Dr Kamlesh, and Nidhi Garg. "An Extensive Review on Image Segmentation Techniques." Indian Journal of Image Processing and Recognition 1, no. 2 (June 10, 2021): 1–5. http://dx.doi.org/10.35940/ijipr.b1002.061221.
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Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.