Статті в журналах: "SEGMENTATION SYSTEM"

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Liu, Qiming, Qifan Lu, Yezi Chai, Zhengyu Tao, Qizhen Wu, Meng Jiang, and Jun Pu. "Radiomics-Based Quality Control System for Automatic Cardiac Segmentation: A Feasibility Study." Bioengineering 10, no. 7 (July 1, 2023): 791. http://dx.doi.org/10.3390/bioengineering10070791.

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Purpose: In the past decade, there has been a rapid increase in the development of automatic cardiac segmentation methods. However, the automatic quality control (QC) of these segmentation methods has received less attention. This study aims to address this gap by developing an automatic pipeline that incorporates DL-based cardiac segmentation and radiomics-based quality control. Methods: In the DL-based localization and segmentation part, the entire heart was first located and cropped. Then, the cropped images were further utilized for the segmentation of the right ventricle cavity (RVC), myocardium (MYO), and left ventricle cavity (LVC). As for the radiomics-based QC part, a training radiomics dataset was created with segmentation tasks of various quality. This dataset was used for feature extraction, selection, and QC model development. The model performance was then evaluated using both internal and external testing datasets. Results: In the internal testing dataset, the segmentation model demonstrated a great performance with a dice similarity coefficient (DSC) of 0.954 for whole heart segmentations. Images were then appropriately cropped to 160 × 160 pixels. The models also performed well for cardiac substructure segmentations. The DSC values were 0.863, 0.872, and 0.940 for RVC, MYO, and LVC for 2D masks and 0.928, 0.886, and 0.962 for RVC, MYO, and LVC for 3D masks with an attention-UNet. After feature selection with the radiomics dataset, we developed a series of models to predict the automatic segmentation quality and its DSC value for the RVC, MYO, and LVC structures. The mean absolute values for our best prediction models were 0.060, 0.032, and 0.021 for 2D segmentations and 0.027, 0.017, and 0.011 for 3D segmentations, respectively. Additionally, the radiomics-based classification models demonstrated a high negative detection rate of >0.85 in all 2D groups. In the external dataset, models showed similar results. Conclusions: We developed a pipeline including cardiac substructure segmentation and QC at both the slice (2D) and subject (3D) levels. Our results demonstrate that the radiomics method possesses great potential for the automatic QC of cardiac segmentation.

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Meenakshi BK, Meenakshi BK, and Prasad M. R. Prasad M R. "Survey on Segmentation to Iris Recognition System." International Journal of Scientific Research 3, no. 4 (June 1, 2012): 514–15. http://dx.doi.org/10.15373/22778179/apr2014/184.

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Gurari, Danna, Mehrnoosh Sameki, and Margrit Betke. "Investigating the Influence of Data Familiarity to Improve the Design of a Crowdsourcing Image Annotation System." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 4 (September 21, 2016): 59–68. http://dx.doi.org/10.1609/hcomp.v4i1.13294.

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Crowdsourced demarcations of object boundaries in images (segmentations) are important for many vision-based applications. A commonly reported challenge is that a large percentage of crowd results are discarded due to concerns about quality. We conducted three studies to examine (1) how does the quality of crowdsourced segmentations differ for familiar everyday images versus unfamiliar biomedical images?, (2) how does making familiar images less recognizable (rotating images upside down) influence crowd work with respect to the quality of results, segmentation time, and segmentation detail?, and (3) how does crowd workers’ judgments of the ambiguity of the segmentation task, collected by voting, differ for familiar everyday images and unfamiliar biomedical images? We analyzed a total of 2,525 segmentations collected from 121 crowd workers and 1,850 votes from 55 crowd workers. Our results illustrate the potential benefit of explicitly accounting for human familiarity with the data when designing computer interfaces for human interaction.

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Mendoza Garay, Juan Ignacio. "Segmentation boundaries in accelerometer data of arm motion induced by music: Online computation and perceptual assessment." Human Technology 18, no. 3 (December 28, 2022): 250–66. http://dx.doi.org/10.14254/1795-6889.2022.18-3.4.

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Segmentation is a cognitive process involved in the understanding of information perceived through the senses. Likewise, the automatic segmentation of data captured by sensors may be used for the identification of patterns. This study is concerned with the segmentation of dancing motion captured by accelerometry and its possible applications, such as pattern learning and recognition, or gestural control of devices. To that effect, an automatic segmentation system was formulated and tested. Two participants were asked to ‘dance with one arm’ while their motion was measured by an accelerometer. The performances were recorded on video, and manually segmented by six annotators later. The annotations were used to optimize the automatic segmentation system, maximizing a novel similarity score between computed and annotated segmentations. The computed segmentations with highest similarity to each annotation were then manually assessed by the annotators, resulting in Precision between 0.71 and 0.89, and Recall between 0.82 to 1.

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Sunoqrot, Mohammed R. S., Kirsten M. Selnæs, Elise Sandsmark, Gabriel A. Nketiah, Olmo Zavala-Romero, Radka Stoyanova, Tone F. Bathen, and Mattijs Elschot. "A Quality Control System for Automated Prostate Segmentation on T2-Weighted MRI." Diagnostics 10, no. 9 (September 18, 2020): 714. http://dx.doi.org/10.3390/diagnostics10090714.

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Computer-aided detection and diagnosis (CAD) systems have the potential to improve robustness and efficiency compared to traditional radiological reading of magnetic resonance imaging (MRI). Fully automated segmentation of the prostate is a crucial step of CAD for prostate cancer, but visual inspection is still required to detect poorly segmented cases. The aim of this work was therefore to establish a fully automated quality control (QC) system for prostate segmentation based on T2-weighted MRI. Four different deep learning-based segmentation methods were used to segment the prostate for 585 patients. First order, shape and textural radiomics features were extracted from the segmented prostate masks. A reference quality score (QS) was calculated for each automated segmentation in comparison to a manual segmentation. A least absolute shrinkage and selection operator (LASSO) was trained and optimized on a randomly assigned training dataset (N = 1756, 439 cases from each segmentation method) to build a generalizable linear regression model based on the radiomics features that best estimated the reference QS. Subsequently, the model was used to estimate the QSs for an independent testing dataset (N = 584, 146 cases from each segmentation method). The mean ± standard deviation absolute error between the estimated and reference QSs was 5.47 ± 6.33 on a scale from 0 to 100. In addition, we found a strong correlation between the estimated and reference QSs (rho = 0.70). In conclusion, we developed an automated QC system that may be helpful for evaluating the quality of automated prostate segmentations.

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Joseph, E., A. M. Aibinu, B. A. Sadiq, H. Bello Salau, and M. J. E. Salami. "Scorpion image segmentation system." IOP Conference Series: Materials Science and Engineering 53 (December 20, 2013): 012055. http://dx.doi.org/10.1088/1757-899x/53/1/012055.

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Mauricaite, Radvile, Ella Mi, Jiarong Chen, Andrew Ho, Lillie Pakzad-Shahabi, and Matthew Williams. "Fully automated deep learning system for detecting sarcopenia on brain MRI in glioblastoma." Neuro-Oncology 23, Supplement_4 (October 1, 2021): iv13. http://dx.doi.org/10.1093/neuonc/noab195.031.

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Abstract Aims Glioblastoma multiforme (GBM) is an aggressive brain malignancy. Performance status is an important prognostic factor but is subjectively evaluated, resulting in inaccuracy. Objective markers of frailty/physical condition, such as measures of skeletal muscle mass can be evaluated on cross-sectional imaging and is associated with cancer survival. In GBM, temporalis muscle has been identified as a skeletal muscle mass surrogate and a prognostic factor. However, current manual muscle quantification is time consuming, limiting clinical adoption. We previously developed a deep learning system for automated temporalis muscle quantification, with high accuracy (Dice coefficient 0.912), and showed muscle cross-sectional area is independently significantly associated with survival in GBM (HR 0.380). However, it required manual selection of the temporalis muscle-containing MRI slice. Thus, in this work we aimed to develop a fully automatic deep-learning system, using the eyeball as an anatomic landmark for automatic slice selection, to quantify temporalis and validate on independent datasets. Method 3D brain MRI scans were obtained from four datasets: our in-house glioblastoma patient dataset, TCGA-GBM, IVY-GAP and REMBRANDT. Manual eyeball and temporalis segmentations were performed on 2D MRI images by two experienced readers. Two neural networks (2D U-Nets) were trained, one to automatically segment the eyeball and the other to segment the temporalis muscle on 2D MRI images using Dice loss function. The cross sectional area of eyeball segmentations were quantified and thresholded, to select the superior orbital MRI slice from each scan. This slice underwent temporalis segmentation, whose cross sectional area was then quantified. Accuracy of automatically predicted eyeball and temporalis segmentations were compared to manual ground truth segmentations on metrics of Dice coefficient, precision, recall and Hausdorff distance. Accuracy of MRI slice selection (by the eyeball segmentation model) for temporalis segmentation was determined by comparing automatically selected slices to slices selected manually by a trained neuro-oncologist. Results 398 images from 185 patients and 366 images from 145 patients were used for the eyeball and temporalis segmentation models, respectively. 61 independent TCGA-GBM scans formed a validation cohort to assess the performance of the full pipeline. The model achieved high accuracy in eyeball segmentation, with test set Dice coefficient of 0.9029 ± 0.0894, precision of 0.8842 ± 0.0992, recall of 0.9297 ± 0.6020 and Hausdorff distance of 2.8847 ± 0.6020. High segmentation accuracy was also achieved by the temporalis segmentation model, with Dice coefficient of 0.8968 ± 0.0375, precision of 0.8877 ± 0.0679, recall of 0.9118 ± 0.0505 and Hausdorff distance of 1.8232 ± 0.3263 in the test set. 96.1% of automatically selected slices for temporalis segmentation were within 2 slices of the manually selected slice. Conclusion Temporalis muscle cross-sectional area can be rapidly and accurately assessed from 3D MRI brain scans using a deep learning-based system in a fully automated pipeline. Combined with our and others’ previous results that demonstrate the prognostic significance of temporalis cross-sectional area and muscle width, our findings suggest a role for deep learning in muscle mass and sarcopenia screening in GBM, with the potential to add significant value to routine imaging. Possible clinical applications include risk profiling, treatment stratification and informing interventions for muscle preservation. Further work will be to validate the prognostic value of temporalis muscle cross sectional area measurements generated by our fully automatic deep learning system in the multiple in-house and external datasets.

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Wang, Zhengfei, Lai Wang, Fu'an Xiao, Qingsong Chen, Liming Lu, and Jiaming Hong. "A Traditional Chinese Medicine Traceability System Based on Lightweight Blockchain." Journal of Medical Internet Research 23, no. 6 (June 21, 2021): e25946. http://dx.doi.org/10.2196/25946.

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Background Recently, the problem of traditional Chinese medicine (TCM) safety has attracted attention worldwide. To prevent the spread of counterfeit drugs, it is necessary to establish a drug traceability system. A traditional drug traceability system can record the whole circulation process of drugs, from planting, production, processing, and warehousing to use by hospitals and patients. Once counterfeit drugs are found, they can be traced back to the source. However, traditional drug traceability systems have some drawbacks, such as failure to prevent tampering and facilitation of sensitive disclosure. Blockchain (including Bitcoin and Ethernet Square) is an effective technology to address the problems of traditional drug traceability systems. However, some risks impact the reliability of blockchain, such as information explosion, sensitive information leakage, and poor scalability. Objective To avoid the risks associated with the application of blockchain, we propose a lightweight block chain framework. Methods In this framework, both horizontal and vertical segmentations are performed when designing the blocks, and effective strategies are provided for both segmentations. For horizontal segmentation operations, the header and body of the blockchain are separated and stored in the blockchain, and the body is stored in the InterPlanetary File System. For vertical segmentation operations, the blockchain is cut off according to time or size. For the addition of new blocks, miners only need to copy the latest part of the blockchain and append the tail and vertical segmentation of the block through the consensus mechanism. Results Our framework could greatly reduce the size of the blockchain and improve the verification efficiency. Conclusions Experimental results have shown that the efficiency improves compared with ethernet when a new block is added to the blockchain and a search is conducted.

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Allioui, Hanane, Mohamed Sadgal, and Aziz El Fazziki. "An Improved Image Segmentation System." Journal of communications software and systems 16, no. 2 (April 27, 2020): 143–55. http://dx.doi.org/10.24138/jcomss.v16i2.830.

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In this paper, we present a solution-based cooperation approach for strengthening the image segmentation.This paper proposes a cooperative method relying on Multi-Agent System. The main contribution of this work is to highlight the importance of cooperation between the contour and region growing based on Multi-Agent System (MAS). Consequently, agents’ interactions form the main part of the whole process for image segmentation. Similar works were proposed to evaluate the effectiveness of the proposed solution. The main difference is that our Multi-Agent System can perform the segmentation process ensuring efficiency. Our results show that the performance indices in the system were higher. Furthermore, the integration of the cooperation paradigm allows to speed up the segmentation process. Besides, the tests reveal the robustness of our method by proving competitive results. Our proposal achieved an accuracy of 93,51%± 0,8, a sensitivity of 93,53%± 5,08 and a specificity rate of 92,64%± 4,01.

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Baujard, Olivier. "KISS: a multiagent segmentation system." Optical Engineering 32, no. 6 (1993): 1235. http://dx.doi.org/10.1117/12.134190.

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Loizou, Christos P., and Marios Pantziaris. "An integrated system for the complete segmentation of the common carotid artery bifurcation in ultrasound images." Journal of Biomedical Engineering and Informatics 1, no. 1 (July 27, 2015): 11. http://dx.doi.org/10.5430/jbei.v1n1p11.

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The complete segmentation of the common carotid artery (CCA) bifurcation in ultrasound images is important for the evaluation of atherosclerosis disease and the quantification of the risk of stroke. The current research work further evaluates and validates a semi-automated (SA) snake’s based segmentation system suitable for the complete segmentation of the CCA bifurcation in two-dimensional (2D) ultrasound images. The proposed system semi-automatically estimates the intima-media thickness (IMT), the atherosclerotic carotid plaque borders and dimensions, the internal carotid artery (ICA) origin’s stenosis, the carotid diameter (D), as well as other geometric measurements of the atherosclerotic carotid plaque. The system was evaluated on 300 2D longitudinal ultrasound images of the CCA bifurcation with manual (M) segmentations available from a neurovascular expert. No statistical significant differences between all M and SA IMT, plaque and D segmentation measurements were found. In a future study, texture features extracted from the intima-media complex (IMC) may be used to separate subjects in high and low risk groups, which may develop a stroke. However, a larger scale study is required for evaluating the system before its application in the real clinical practice.

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Carmo, Diedre, Israel Campiotti, Lívia Rodrigues, Irene Fantini, Gustavo Pinheiro, Daniel Moraes, Rodrigo Nogueira, Leticia Rittner, and Roberto Lotufo. "Rapidly deploying a COVID-19 decision support system in one of the largest Brazilian hospitals." Health Informatics Journal 27, no. 3 (July 2021): 146045822110330. http://dx.doi.org/10.1177/14604582211033017.

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The COVID-19 pandemic generated research interest in automated models to perform classification and segmentation from medical imaging of COVID-19 patients, However, applications in real-world scenarios are still needed. We describe the development and deployment of COVID-19 decision support and segmentation system. A partnership with a Brazilian radiologist consortium, gave us access to 1000s of labeled computed tomography (CT) and X-ray images from São Paulo Hospitals. The system used EfficientNet and EfficientDet networks, state-of-the-art convolutional neural networks for natural images classification and segmentation, in a real-time scalable scenario in communication with a Picture Archiving and Communication System (PACS). Additionally, the system could reject non-related images, using header analysis and classifiers. We achieved CT and X-ray classification accuracies of 0.94 and 0.98, respectively, and Dice coefficient for lung and covid findings segmentations of 0.98 and 0.73, respectively. The median response time was 7 s for X-ray and 4 min for CT.

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Muhadi, Nur, Ahmad Abdullah, Siti Bejo, Muhammad Mahadi, and Ana Mijic. "Image Segmentation Methods for Flood Monitoring System." Water 12, no. 6 (June 26, 2020): 1825. http://dx.doi.org/10.3390/w12061825.

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Flood disasters are considered annual disasters in Malaysia due to their consistent occurrence. They are among the most dangerous disasters in the country. Lack of data during flood events is the main constraint to improving flood monitoring systems. With the rapid development of information technology, flood monitoring systems using a computer vision approach have gained attention over the last decade. Computer vision requires an image segmentation technique to understand the content of the image and to facilitate analysis. Various segmentation algorithms have been developed to improve results. This paper presents a comparative study of image segmentation techniques used in extracting water information from digital images. The segmentation methods were evaluated visually and statistically. To evaluate the segmentation methods statistically, the dice similarity coefficient and the Jaccard index were calculated to measure the similarity between the segmentation results and the ground truth images. Based on the experimental results, the hybrid technique obtained the highest values among the three methods, yielding an average of 97.70% for the dice score and 95.51% for the Jaccard index. Therefore, we concluded that the hybrid technique is a promising segmentation method compared to the others in extracting water features from digital images.

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Zhao, Xin Bo, Xiao Chun Zou, and Zhong Ma. "An Efficient Sport Video Segmentation System." Key Engineering Materials 467-469 (February 2011): 2042–47. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.2042.

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Digital sport video segmentation is an active area of research. And such an efficient sport video segmentation system is necessary. The system employs the feature-based motion estimator to estimate the video motion model parameters. Thus, we can use the result to compose the panorama. Project frames with motion parameters, the corresponding frames were connected and then stitched into a panoramic image according to infer the frames on a 2D manifold. For accurate alignment, we iterate between the motion estimator and topology determination to optimized parameters. Accuracy panorama composition is implemented to redress the error accumulation and achieve the pixel combination. After that, for the purpose of removing the foreground objects, we use the panorama estimation process. At last, we determine the sportsman segmentation masks that are the final output of the segmentation system. The test results showed that the proposed system is efficient.

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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.

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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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Jaware, Tushar H., K. B. Khanchandani, and Anita Zurani. "An Accurate Automated Local Similarity Factor-Based Neural Tree Approach toward Tissue Segmentation of Newborn Brain MRI." American Journal of Perinatology 36, no. 11 (December 15, 2018): 1157–70. http://dx.doi.org/10.1055/s-0038-1675375.

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Background Segmentation of brain MR images of neonates is a primary step for assessment of brain evolvement. Advanced segmentation techniques used for adult brain MRI are not companionable for neonates, due to extensive dissimilarities in tissue properties and head structure. Existing segmentation methods for neonates utilizes brain atlases or requires manual elucidation, which results into improper and atlas dependent segmentation. Objective The primary objective of this work is to develop fully automatic, atlas free, and robust system to segment and classify brain tissues of newborn infants from magnetic resonance images. Study Design In this study, we propose a fully automatic, atlas-free pipeline based Neural Tree approach for segmentation of newborn brain MRI which utilizes resourceful local resemblance factor such as concerning, connectivity, structure, and relative tissue location. Physical collaboration and uses of an atlas are not required in proposed method and at the same time skirting atlas-associated bias which results in improved segmentation. Proposed technique segments and classify brain tissues both at global and tissue level. Results We examined our results through visual assessment by neonatologists and quantitative comparisons that show first-rate concurrence with proficient manual segmentations. The implementation results of the proposed technique provided a good overall accuracy of 91.82% for the segmentation of brain tissues as compared with other methods. Conclusion The pipelined-based neural tree approach along with local similarity factor segments and classify brain tissues. The proposed automated system have higher dice similarity coefficient as well as computational speed.

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Win, Htwe Pa Pa, Phyo Thu Thu Khine, and Khin Nwe Ni Tun. "Character Segmentation Scheme for OCR System." International Journal of Computer Vision and Image Processing 1, no. 4 (October 2011): 50–58. http://dx.doi.org/10.4018/ijcvip.2011100104.

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Automatic machine-printed Optical Characters or texts Recognizers (OCR) are highly desirable for a multitude of modern IT applications, including Digital Library software. However, the state of the art OCR systems cannot do for Myanmar scripts as the language poses many challenges for document understanding. Therefore, the authors design an Optical Character Recognition System for Myanmar Printed Document (OCRMPD), with several proposed techniques that can automatically recognize Myanmar printed text from document images. In order to get more accurate system, the authors propose the method for isolation of the character image by using not only the projection methods but also structural analysis for wrongly segmented characters. To reveal the effectiveness of the segmentation technique, the authors follow a new hybrid feature extraction method and choose the SVM classifier for recognition of the character image. The proposed algorithms have been tested on a variety of Myanmar printed documents and the results of the experiments indicate that the methods can increase the segmentation accuracy as well as recognition rates.

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Fujimoto, K., M. Musashi, and T. Yoshinaga. "Discrete-time dynamic image segmentation system." Electronics Letters 44, no. 12 (2008): 727. http://dx.doi.org/10.1049/el:20080546.

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ALGINAHI, Y., D. FEKRI, and M. A. SID-AHMED. "A NEURAL-BASED PAGE SEGMENTATION SYSTEM." Journal of Circuits, Systems and Computers 14, no. 01 (February 2005): 109–22. http://dx.doi.org/10.1142/s0218126605002192.

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Page segmentation is necessary for optical character recognition and very useful in document image manipulation. This paper describes two classification methods, a modified linear adaptive method and a proposed neural network system that classifies an image into text, halftone image (photos, dark images, etc.), and graphics (graphs, tables, flowcharts, etc.). The blocks were segmented using the Run Length Smearing Algorithm. The smearing process was done automatically by fixing the threshold values for smearing. Features are extracted from the segmented blocks for classification into text, graphics, and halftone images. The second method uses a multi-layer perceptron neural network for classification. Two parameters, a shape factor, f1, and an angle from the rectangular block segments, were fed into the neural network system giving us three classes: text, halftone images, and graphics. Experiments on 30 mixed-content document images show that the method works well on a wide variety of layouts in document images.

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Giustolisi, O., and L. Ridolfi. "Modularity Index for Hydraulic System Segmentation." Procedia Engineering 89 (2014): 1152–59. http://dx.doi.org/10.1016/j.proeng.2014.11.240.

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Subasic, M., S. Loncaric, and J. Birchbauer. "Expert system segmentation of face images." Expert Systems with Applications 36, no. 3 (April 2009): 4497–507. http://dx.doi.org/10.1016/j.eswa.2008.05.010.

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Lareyre, Fabien, Cédric Adam, Marion Carrier, and Juliette Raffort. "Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning." Journal of Clinical Medicine 10, no. 15 (July 29, 2021): 3347. http://dx.doi.org/10.3390/jcm10153347.

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Background: Computed tomography angiography (CTA) is one of the most commonly used imaging technique for the management of vascular diseases. Here, we aimed to develop a hybrid method combining a feature-based expert system with a supervised deep learning (DL) algorithm to enable a fully automatic segmentation of the abdominal vascular tree. Methods: We proposed an algorithm based on the hybridization of a data-driven convolutional neural network and a knowledge-based model dedicated to vascular system segmentation. By using two distinct datasets of CTA from patients to evaluate independence to training dataset, the accuracy of the hybrid method for lumen and thrombus segmentation was evaluated compared to the feature-based expert system alone and to the ground truth provided by a human expert. Results: The hybrid approach demonstrated a better accuracy for lumen segmentation compared to the expert system alone (volume similarity: 0.8128 vs. 0.7912, p = 0.0006 and Dice similarity coefficient: 0.8266 vs. 0.7942, p < 0.0001). The accuracy for thrombus segmentation was also enhanced using the hybrid approach (volume similarity: 0.9404 vs. 0.9185, p = 0.0027 and Dice similarity coefficient: 0.8918 vs. 0.8654, p < 0.0001). Conclusions: By enabling a robust and fully automatic segmentation, the method could be used to develop real-time decision support to help in the management of vascular diseases.

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Shankar, K., Dr S. Srinivasan, and Dr T. S. Sivakumaran K. Madhavi Priya. "Discovering Anomalies Based on Saliency Detection and Segmentation in Surveillance System." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 227–31. http://dx.doi.org/10.31142/ijtsrd5871.

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Kaur, Gurpreet, and Sonika Jindal. "A REVIEW ON IMAGE SEGMENTATION USING GPU." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 10 (July 21, 2016): 7160–63. http://dx.doi.org/10.24297/ijct.v15i10.4502.

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Image Segmentations play a heavy role in areas such as computer vision and image processing due to its broad usage and immense applications. Because of the large importance of image segmentation a number of algorithms have been proposed and different approaches have been adopted. Segmentation divides an image into distinct regions containing each pixel with similar attributes. The objective of apportioning is to simplify and/or alter the representation of an image into something that is more meaningful and more comfortable to break down. This paper discusses the various techniques implemented for image segmentation and discusses the various Computations that can be performed on the graphics processing unit (GPU) by means of the CUDA architecture in order to achieve fast performance and increase the utilization of available system resources.

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Han, Dan, and Zhi Han Yu. "The Critical Technology Development Status of Machine Translation." Advanced Materials Research 791-793 (September 2013): 1622–25. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1622.

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In this article, we mainly introduce some basic concepts about machine translation. Machine translation means translating a natural language text to another by software. It can be divided into two categories: rule-based and corpus-based. IBM's statistical machine translation, Microsoft's multi-language machine translation project, AT & T's voice translation system and CMUs PANGLOSS system are three typical machine translation systems. Due to sentences are constructed by words continuously in Chinese. Chinese word segmentation is very essential. Three methods of Chinese word segmentation: segmentation methods based on string matching, segmentation method based on the understanding and segmentation method based on the statistics.

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Lai, Runping. "Research on Interactive Visual Communication Design System Based on Dynamic Image." Journal of Physics: Conference Series 2146, no. 1 (January 1, 2022): 012029. http://dx.doi.org/10.1088/1742-6596/2146/1/012029.

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Abstract The SVR image marine-continental segmentation algorithm on account of ameliorated CV model can segment the marine-continental image efficiently, and compare the image results with the original model, so as to continuously iterate the effectiveness of image segmentation. On account of this, this paper first analyses the concept and main methods of SAR image marine-continental segmentation algorithm, then studies the SAR image marine-continental segmentation algorithm on account of ameliorated CV model, and finally gives the process and effect analysis of SAR image marine-continental segmentation on account of ameliorated CV model.

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Sridhar, Bokka. "Investigations of Medical Image Segmentation Methods with Inclusion Mathematical Morphological Operations." Traitement du Signal 38, no. 5 (October 31, 2021): 1531–40. http://dx.doi.org/10.18280/ts.380530.

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Medical image segmentation research is becoming efficient by using mathematical morphological (MM) operators. There are different methods in image segmentation such as supervised and unsupervised segmentations. The MM operators are much effective, in developing a computer aided diagnosis (CAD) system. Medical image such as mammograms, generally they are of low contrast, such that radiologists face difficulties in observing the results. Due to this, diagnosis fails to generate high rate false positives and false negatives. In the proposed work improvement of quality of image segmentation with inclusion of morphological operations with other methods such as watershed transform, fuzzy logic based techniques, curvelets and MRF to detect the masses and calcifications in mammograms. Classification of masses and evaluation of segmentation process are done with artificial neural network and other performance metrics. These methods lead to increase in the accuracy, specificity and sensitivity of mammography and reduce unnecessary biopsies.

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Vania, Malinda, Dawit Mureja, and Deukhee Lee. "Automatic spine segmentation from CT images using Convolutional Neural Network via redundant generation of class labels." Journal of Computational Design and Engineering 6, no. 2 (February 13, 2019): 224–32. http://dx.doi.org/10.1016/j.jcde.2018.05.002.

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Abstract There has been a significant increase from 2010 to 2016 in the number of people suffering from spine problems. The automatic image segmentation of the spine obtained from a computed tomography (CT) image is important for diagnosing spine conditions and for performing surgery with computer-assisted surgery systems. The spine has a complex anatomy that consists of 33 vertebrae, 23 intervertebral disks, the spinal cord, and connecting ribs. As a result, the spinal surgeon is faced with the challenge of needing a robust algorithm to segment and create a model of the spine. In this study, we developed a fully automatic segmentation method to segment the spine from CT images, and we compared our segmentation results with reference segmentations obtained by well-known methods. We use a hybrid method. This method combines the convolutional neural network (CNN) and fully convolutional network (FCN), and utilizes class redundancy as a soft constraint to greatly improve the segmentation results. The proposed method was found to significantly enhance the accuracy of the segmentation results and the system processing time. Our comparison was based on 12 measurements: the Dice coefficient (94%), Jaccard index (93%), volumetric similarity (96%), sensitivity (97%), specificity (99%), precision (over segmentation 8.3 and under segmentation 2.6), accuracy (99%), Matthews correlation coefficient (0.93), mean surface distance (0.16 mm), Hausdorff distance (7.4 mm), and global consistency error (0.02). We experimented with CT images from 32 patients, and the experimental results demonstrated the efficiency of the proposed method. Highlights A method to enhance the accuracy of spine segmentation from CT data was proposed. The proposed method uses Convolutional Neural Network via redundant generation of class labels. Experiments show the segmentation accuracy has been enhanced.

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Horn, David, and Irit Opher. "Temporal Segmentation in a Neural Dynamic System." Neural Computation 8, no. 2 (February 15, 1996): 373–89. http://dx.doi.org/10.1162/neco.1996.8.2.373.

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Oscillatory attractor neural networks can perform temporal segmentation, i.e., separate the joint inputs they receive, through the formation of staggered oscillations. This property, which may be basic to many perceptual functions, is investigated here in the context of a symmetric dynamic system. The fully segmented mode is one type of limit cycle that this system can develop. It can be sustained for only a limited number n of oscillators. This limitation to a small number of segments is a basic phenomenon in such systems. Within our model we can explain it in terms of the limited range of narrow subharmonic solutions of the single nonlinear oscillator. Moreover, this point of view allows us to understand the dominance of three leading amplitudes in solutions of partial segmentation, which are obtained for high n. The latter are also abundant when we replace the common input with a graded one, allowing for different inputs to different oscillators. Switching to an input with fluctuating components, we obtain segmentation dominance for small systems and quite irregular waveforms for large systems.

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Ma, Tian, Xinlei Zhou, Jiayi Yang, Boyang Meng, Jiali Qian, Jiehui Zhang, and Gang Ge. "Dental Lesion Segmentation Using an Improved ICNet Network with Attention." Micromachines 13, no. 11 (November 7, 2022): 1920. http://dx.doi.org/10.3390/mi13111920.

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Precise segmentation of tooth lesions is critical to creation of an intelligent tooth lesion detection system. As a solution to the problem that tooth lesions are similar to normal tooth tissues and difficult to segment, an improved segmentation method of the image cascade network (ICNet) network is proposed to segment various lesion types, such as calculus, gingivitis, and tartar. First, the ICNet network model is used to achieve real-time segmentation of lesions. Second, the Convolutional Block Attention Module (CBAM) is integrated into the ICNet network structure, and large-size convolutions in the spatial attention module are replaced with layered dilated convolutions to enhance the relevant features while suppressing useless features and solve the problem of inaccurate lesion segmentations. Finally, part of the convolution in the network model is replaced with an asymmetric convolution to reduce the calculations added by the attention module. Experimental results show that compared with Fully Convolutional Networks (FCN), U-Net, SegNet, and other segmentation algorithms, our method has a significant improvement in the segmentation effect, and the image processing frequency is higher, which satisfies the real-time requirements of tooth lesion segmentation accuracy.

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Rossi, Farli. "APPLICATION OF A SEMI-AUTOMATED TECHNIQUE IN LUNG LESION SEGMENTATION." Jurnal Teknoinfo 15, no. 1 (January 15, 2021): 56. http://dx.doi.org/10.33365/jti.v15i1.945.

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Segmentation is one of the most important steps in automated medical diagnosis applications, which affects the accuracy of the overall system. In this study, we apply a semi-automated technique that combines an active contour and low-level processing techniques in lung lesion segmentation by extracting lung lesions from thoracic Positron Emission Tomography (PET)/Computed Tomography (CT) images. The lesions were first segmented in Positron Emission Tomography (PET) images which have been converted previously to Standardised Uptake Values (SUVs). The segmented PET images then serve as an initial contour for subsequent active contour segmentation of corresponding CT images. To measure accuracy, the Jaccard Index (JI) was used. Jaccard Index (JI) was calculated by comparing the segmented lesion to alternative segmentations obtained from the QIN lung CT segmentation challenge, which is possible by registering the whole body PET/CT images to the corresponding thoracic CT images. The results showed that the semi-automated technique (combination techniques between an active contour and low-level processing) in lung lesion segmentation has moderate accuracy with an average JI value of 0.76±0.12.

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Klimont, Michał, Mateusz Flieger, Jacek Rzeszutek, Joanna Stachera, Aleksandra Zakrzewska, and Katarzyna Jończyk-Potoczna. "Automated Ventricular System Segmentation in Paediatric Patients Treated for Hydrocephalus Using Deep Learning Methods." BioMed Research International 2019 (July 7, 2019): 1–9. http://dx.doi.org/10.1155/2019/3059170.

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Hydrocephalus is a common neurological condition that can have traumatic ramifications and can be lethal without treatment. Nowadays, during therapy radiologists have to spend a vast amount of time assessing the volume of cerebrospinal fluid (CSF) by manual segmentation on Computed Tomography (CT) images. Further, some of the segmentations are prone to radiologist bias and high intraobserver variability. To improve this, researchers are exploring methods to automate the process, which would enable faster and more unbiased results. In this study, we propose the application of U-Net convolutional neural network in order to automatically segment CT brain scans for location of CSF. U-Net is a neural network that has proven to be successful for various interdisciplinary segmentation tasks. We optimised training using state of the art methods, including “1cycle” learning rate policy, transfer learning, generalized dice loss function, mixed float precision, self-attention, and data augmentation. Even though the study was performed using a limited amount of data (80 CT images), our experiment has shown near human-level performance. We managed to achieve a 0.917 mean dice score with 0.0352 standard deviation on cross validation across the training data and a 0.9506 mean dice score on a separate test set. To our knowledge, these results are better than any known method for CSF segmentation in hydrocephalic patients, and thus, it is promising for potential practical applications.

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Bouget, David, Roelant S. Eijgelaar, André Pedersen, Ivar Kommers, Hilko Ardon, Frederik Barkhof, Lorenzo Bello, et al. "Glioblastoma Surgery Imaging–Reporting and Data System: Validation and Performance of the Automated Segmentation Task." Cancers 13, no. 18 (September 17, 2021): 4674. http://dx.doi.org/10.3390/cancers13184674.

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For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime.

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Kubicek, Jan, Alice Varysova, Martin Cerny, Kristyna Hancarova, David Oczka, Martin Augustynek, Marek Penhaker, Ondrej Prokop, and Radomir Scurek. "Performance and Robustness of Regional Image Segmentation Driven by Selected Evolutionary and Genetic Algorithms: Study on MR Articular Cartilage Images." Sensors 22, no. 17 (August 23, 2022): 6335. http://dx.doi.org/10.3390/s22176335.

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The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of these methods is prone to the image noise and artefacts. In this context, regional segmentation strategies, driven by either genetic algorithms or selected evolutionary computing strategies, have the potential to overcome these traditional methods such as Otsu thresholding or K-means in the context of their performance. These optimization strategies consecutively generate a pyramid of a possible set of histogram thresholds, of which the quality is evaluated by using the fitness function based on Kapur’s entropy maximization to find the most optimal combination of thresholds for articular cartilage segmentation. On the other hand, such optimization strategies are often computationally demanding, which is a limitation of using such methods for a stack of MR images. In this study, we publish a comprehensive analysis of the optimization methods based on fuzzy soft segmentation, driven by artificial bee colony (ABC), particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO), and a genetic algorithm for an optimal thresholding selection against the routine segmentations Otsu and K-means for analysis and the features extraction of articular cartilage from MR images. This study objectively analyzes the performance of the segmentation strategies upon variable noise with dynamic intensities to report a segmentation’s robustness in various image conditions for a various number of segmentation classes (4, 7, and 10), cartilage features (area, perimeter, and skeleton) extraction preciseness against the routine segmentation strategies, and lastly the computing time, which represents an important factor of segmentation performance. We use the same settings on individual optimization strategies: 100 iterations and 50 population. This study suggests that the combination of fuzzy thresholding with an ABC algorithm gives the best performance in the comparison with other methods as from the view of the segmentation influence of additive dynamic noise influence, also for cartilage features extraction. On the other hand, using genetic algorithms for cartilage segmentation in some cases does not give a good performance. In most cases, the analyzed optimization strategies significantly overcome the routine segmentation methods except for the computing time, which is normally lower for the routine algorithms. We also publish statistical tests of significance, showing differences in the performance of individual optimization strategies against Otsu and K-means method. Lastly, as a part of this study, we publish a software environment, integrating all the methods from this study.

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35

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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36

Liu, Chang. "Research on Words Segmentation Technology in Chinese Full Text Retrieval System." Applied Mechanics and Materials 411-414 (September 2013): 313–16. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.313.

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In order to improve the speed of Chinese full-text retrieval in the premise of ensuring Chinese ambiguity inclusion and length limitation, this paper introduces the application methods of Chinese full-text retrieval system and the current application situation of Chinese word segmentation technology. Based on the existed word segmentation algorithms, this paper proposed an improved Chinese word segmentation algorithm. In the proposed method, the procedure of indexing is to construct the map between the relative words in the context and the dictionary. This paper improves the diction to realize better mapping with relative words, so as to realize Chinese words segmentation. The experiments demonstrate that the proposed Chinese full-text words segmentation algorithm is more effective than the existing methods.

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37

Cui, ChunHui, Qian Zhang, and KingNgi Ngan. "Multi-view Video Based Object Segmentation - A Tutorial." ECTI Transactions on Electrical Engineering, Electronics, and Communications 7, no. 2 (July 10, 2008): 1–16. http://dx.doi.org/10.37936/ecti-eec.200972.171842.

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Video based object segmentation (VBOS) is an important step in many computer vision and multimedia tasks such as video editing and compositing. In recent years, multi-view VBOS systems have become more and more popular because the stereo clues from multiview data can be efficiently incorporated to improve the segmentation results and eliminate the required initial user input. In this paper, we give a review on recent development of multi-view VBOS systems and the related techniques including data acquisition, camera calibration, depth reconstruction, object segmentation and tracking. Furthermore, we introduce our multiple objects segmentation system from multiview video sequence to illustrate the practical implementation of multi-view VBOS system for 3D video rendering applications.

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38

Wu, Xiao Yu, Lei Yang, Shao Bin Li, and Pin Xu. "An Interactive Video Foreground Segmentation System Based on Modeling and Dynamic Graph Cut Algorithm." Advanced Materials Research 532-533 (June 2012): 1770–74. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1770.

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This paper proposes an interactive video foreground segmentation method based on modeling and graph cut algorithm. User interactions are required at initial frame or key frame of video sequence at first. Secondly we make use of user interactions information to develop background/foreground model and get foreground segmentation result of the current frame in term of graph cut algorithm. And automatic updated methods are proposed to obtain foreground segmentation results automatically on the later sequence of video without user interaction. The developed system of interactive video foreground segmentation has performances with extracting object and editing segmentation results. Experimental results on kinds of video demonstrated that our interactive segmentation system is efficient.

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Deng, Hanbing, Tongyu Xu, Yuncheng Zhou, and Teng Miao. "Depth Density Achieves a Better Result for Semantic Segmentation with the Kinect System." Sensors 20, no. 3 (February 3, 2020): 812. http://dx.doi.org/10.3390/s20030812.

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Image segmentation is one of the most important methods for animal phenome research. Since the advent of deep learning, many researchers have looked at multilayer convolutional neural networks to solve the problems of image segmentation. A network simplifies the task of image segmentation with automatic feature extraction. Many networks struggle to output accurate details when dealing with pixel-level segmentation. In this paper, we propose a new concept: Depth density. Based on a depth image, produced by a Kinect system, we design a new function to calculate the depth density value of each pixel and bring this value back to the result of semantic segmentation for improving the accuracy. In the experiment, we choose Simmental cattle as the target of image segmentation and fully convolutional networks (FCN) as the verification networks. We proved that depth density can improve four metrics of semantic segmentation (pixel accuracy, mean accuracy, mean intersection over union, and frequency weight intersection over union) by 2.9%, 0.3%, 11.4%, and 5.02%, respectively. The result shows that depth information produced by Kinect can improve the accuracy of the semantic segmentation of FCN. This provides a new way of analyzing the phenotype information of animals.

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MARTIN, GALE L., MOSFEQ RASHID, and JAMES A. PITTMAN. "INTEGRATED SEGMENTATION AND RECOGNITION THROUGH EXHAUSTIVE SCANS OR LEARNED SACCADIC JUMPS." International Journal of Pattern Recognition and Artificial Intelligence 07, no. 04 (August 1993): 831–47. http://dx.doi.org/10.1142/s021800149300042x.

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This paper advances two approaches to integrating handwritten character segmentation and recognition within one system, where the underlying function is learned by a backpropagation neural network. Integrated segmentation and recognition is necessary when characters overlap or touch, or when an individual character is broken up. The first approach exhaustively scans a field of characters, effectively creating a possible segmentation at each scan point. A neural net is trained to both identify when its input window is centered over a character, and if it is, to classify the character. This approach is similar to most recently advanced approaches to integrating segmentation and recognition, and has the common flaw of generating too many possible segmentations to be truly efficient. The second approach overcomes this weakness without reducing accuracy by training a neural network to mimic the ballistic and corrective saccades (eye movements) of human vision. A single neural net learns to jump from character to character, making corrective jumps when necessary, and to classify the centered character when properly fixated. The significant aspect of this system is that the neural net learns to both control what is in its input window as well as to recognize what is in the window. High accuracy results are reported for a standard database of handprinted digits for both approaches.

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Amrehn, Mario, Stefan Steidl, Reinier Kortekaas, Maddalena Strumia, Markus Weingarten, Markus Kowarschik, and Andreas Maier. "A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems." International Journal of Biomedical Imaging 2019 (September 5, 2019): 1–21. http://dx.doi.org/10.1155/2019/1464592.

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For complex segmentation tasks, the achievable accuracy of fully automated systems is inherently limited. Specifically, when a precise segmentation result is desired for a small amount of given data sets, semi-automatic methods exhibit a clear benefit for the user. The optimization of human computer interaction (HCI) is an essential part of interactive image segmentation. Nevertheless, publications introducing novel interactive segmentation systems (ISS) often lack an objective comparison of HCI aspects. It is demonstrated that even when the underlying segmentation algorithm is the same throughout interactive prototypes, their user experience may vary substantially. As a result, users prefer simple interfaces as well as a considerable degree of freedom to control each iterative step of the segmentation. In this article, an objective method for the comparison of ISS is proposed, based on extensive user studies. A summative qualitative content analysis is conducted via abstraction of visual and verbal feedback given by the participants. A direct assessment of the segmentation system is executed by the users via the system usability scale (SUS) and AttrakDiff-2 questionnaires. Furthermore, an approximation of the findings regarding usability aspects in those studies is introduced, conducted solely from the system-measurable user actions during their usage of interactive segmentation prototypes. The prediction of all questionnaire results has an average relative error of 8.9%, which is close to the expected precision of the questionnaire results themselves. This automated evaluation scheme may significantly reduce the resources necessary to investigate each variation of a prototype’s user interface (UI) features and segmentation methodologies.

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Mohd Saad, Norhashimah, Muhamad Faizal Yaakub, Abdul Rahim Abdullah, Nor Shahirah Mohd Noor, Nur Azmina Zainal, and Wira Hidayat Mohd Saad. "Automated brain tumor segmentation and classification for MRI analysis system." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (September 1, 2019): 1337. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1337-1344.

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<span>This paper proposed a new analysis technique of brain tumor segmentation and classification for Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI). 25 FLAIR MRI images were collected from online database of Multimodal Brain Tumor Segmentation Challenge 2015 (BRaTS’15). The analysis comprised four stages which are preprocessing, segmentation, feature extraction and classification. Fuzzy C-Means (FCM) was proposed for brain tumor segmentation. Mean, median, mode, standard deviation, area and perimeter were calculated and utilized as the features to be fed into a rule-based classifier. The segmentation performances were assessed based on Jaccard, Dice, False Positive and False Negative Rates (FPR and FNR). The results indicate that FCM offered high similarity indices which were 0.74 and 0.83 for Jaccard and Dice indices, respectively. The technique can possibly provide high accuracy and has the potential to detect and classify brain tumor from FLAIR MRI database.</span>

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43

Rymarczyk, Tomasz. "ANALYSIS MEDICAL AND STEREOSCOPIC IMAGES BY E-MEDICUS SYSTEM." Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska 8, no. 2 (May 30, 2018): 54–57. http://dx.doi.org/10.5604/01.3001.0012.0707.

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In this work, there were implemented methods to analyze and segmentation medical images by using different kind of algorithms. The solution shows the architecture of the system collecting and analyzing data. There was tried to develop an algorithm for level set method applied to piecewise constant image segmentation. These algorithms are needed to identify arbitrary number of phases for the segmentation problem. With the use of modern algorithms, it can obtain a quicker diagnosis and automatically marking areas of the interest region in medical images.

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M, Merlin Asha, G. Naveen Balaji S. Mythili A. Karthikeyan, and N. Thillaiarasu. "An Efficient Brain Tumor Detection Algorithm based on Segmentation for MRI System." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (February 28, 2018): 1353–58. http://dx.doi.org/10.31142/ijtsrd9667.

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45

Chae, Hyun-Uk, Taeho Kim, and Kang-Hyun Jo. "2A1-D10 Segmentation and Correspondence of Human Body from Multiple Camera System." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2009 (2009): _2A1—D10_1—_2A1—D10_4. http://dx.doi.org/10.1299/jsmermd.2009._2a1-d10_1.

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46

Mendi, Engin, Songul Cecen, Emre Ermisoglu, and Coskun Bayrak. "Automated neurosurgical video segmentation and retrieval system." Journal of Biomedical Science and Engineering 03, no. 06 (2010): 618–24. http://dx.doi.org/10.4236/jbise.2010.36084.

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47

Chan, Yung-Kuan, Der-Chen Huang, Kuo-Ching Liu, Rong-Tai Chen, and Xiaoyi Jiang. "An Automatic Indirect Immunofluorescence Cell Segmentation System." Mathematical Problems in Engineering 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/501206.

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Indirect immunofluorescence (IIF) with HEp-2 cells has been used for the detection of antinuclear autoantibodies (ANA) in systemic autoimmune diseases. The ANA testing allows us to scan a broad range of autoantibody entities and to describe them by distinct fluorescence patterns. Automatic inspection for fluorescence patterns in an IIF image can assist physicians, without relevant experience, in making correct diagnosis. How to segment the cells from an IIF image is essential in developing an automatic inspection system for ANA testing. This paper focuses on the cell detection and segmentation; an efficient method is proposed for automatically detecting the cells with fluorescence pattern in an IIF image. Cell culture is a process in which cells grow under control. Cell counting technology plays an important role in measuring the cell density in a culture tank. Moreover, assessing medium suitability, determining population doubling times, and monitoring cell growth in cultures all require a means of quantifying cell population. The proposed method also can be used to count the cells from an image taken under a fluorescence microscope.

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48

Izquierdo, E., and M. Ghanbari. "Key components for an advanced segmentation system." IEEE Transactions on Multimedia 4, no. 1 (March 2002): 97–113. http://dx.doi.org/10.1109/6046.985558.

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49

Sari, T., and M. Sellami. "Cursive Arabic Script Segmentation and Recognition System." International Journal of Computers and Applications 27, no. 3 (January 2005): 161–68. http://dx.doi.org/10.1080/1206212x.2005.11441771.

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50

Wang, Weixing. "Colony image acquisition system and segmentation algorithms." Optical Engineering 50, no. 12 (December 1, 2011): 123001. http://dx.doi.org/10.1117/1.3662398.

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