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Homayoun, Hassan, Ebrahimpour-komleh, Hossein. (1400). Automated Segmentation of Abnormal Tissues in Medical Images. سامانه مدیریت نشریات علمی, 11(4), 415-424. doi: 10.31661/jbpe.v0i0.958
Hassan Homayoun; Hossein Ebrahimpour-komleh. "Automated Segmentation of Abnormal Tissues in Medical Images". سامانه مدیریت نشریات علمی, 11, 4, 1400, 415-424. doi: 10.31661/jbpe.v0i0.958
Homayoun, Hassan, Ebrahimpour-komleh, Hossein. (1400). 'Automated Segmentation of Abnormal Tissues in Medical Images', سامانه مدیریت نشریات علمی, 11(4), pp. 415-424. doi: 10.31661/jbpe.v0i0.958
Homayoun, Hassan, Ebrahimpour-komleh, Hossein. Automated Segmentation of Abnormal Tissues in Medical Images. سامانه مدیریت نشریات علمی, 1400; 11(4): 415-424. doi: 10.31661/jbpe.v0i0.958

Automated Segmentation of Abnormal Tissues in Medical Images

Journal of Biomedical Physics and Engineering
مقاله 2، دوره 11، شماره 4، آبان 2021، صفحه 415-424    اصل مقاله (558.69 K)
نوع مقاله: Blackboard
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.958
نویسندگان
Hassan Homayoun* ؛ Hossein Ebrahimpour-komleh
PhD, Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Kashan, Kashan, Iran
چکیده
Nowadays, medical image modalities are almost available everywhere. These modalities are bases of diagnosis of various diseases sensitive to specific tissue type. Usually physicians look for abnormalities in these modalities in diagnostic procedures. Count and volume of abnormalities are very important for optimal treatment of patients. Segmentation is a preliminary step for these measurements and also further analysis. Manual segmentation of abnormalities is cumbersome, error prone, and subjective. As a result, automated segmentation of abnormal tissue is a need. In this study, representative techniques for segmentation of abnormal tissues are reviewed. Main focus is on the segmentation of multiple sclerosis lesions, breast cancer masses, lung nodules, and skin lesions. As experimental results demonstrate, the methods based on deep learning techniques perform better than other methods that are usually based on handy feature engineering techniques. Finally, the most common measures to evaluate automated abnormal tissue segmentation methods are reported.
کلیدواژه‌ها
Skin Abnormalities؛ Abnormal Tissue Detection؛ Multiple Sclerosis؛ Breast Cancer؛ Multiple Pulmonary Nodules؛ Automatic Segmentation؛ Medical Imaging
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