Automated Segmentation of Abnormal Tissues in Medical Images
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 import...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Shiraz University of Medical Sciences
2021-08-01
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| Series: | Journal of Biomedical Physics and Engineering |
| Subjects: | |
| Online Access: | https://jbpe.sums.ac.ir/article_45654_9a5b539ad72696b984bf2f3d65d619af.pdf |
| _version_ | 1819239312338714624 |
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| author | Hassan Homayoun Hossein Ebrahimpour-komleh |
| author_facet | Hassan Homayoun Hossein Ebrahimpour-komleh |
| author_sort | Hassan Homayoun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T13:50:07Z |
| format | Article |
| id | doaj.art-6acceb03f17a46a99e86e76dbe535a67 |
| institution | Directory Open Access Journal |
| issn | 2251-7200 2251-7200 |
| language | English |
| last_indexed | 2024-12-23T13:50:07Z |
| publishDate | 2021-08-01 |
| publisher | Shiraz University of Medical Sciences |
| record_format | Article |
| series | Journal of Biomedical Physics and Engineering |
| spelling | doaj.art-6acceb03f17a46a99e86e76dbe535a672022-12-21T17:44:37ZengShiraz University of Medical SciencesJournal of Biomedical Physics and Engineering2251-72002251-72002021-08-0111441542410.31661/jbpe.v0i0.95845654Automated Segmentation of Abnormal Tissues in Medical ImagesHassan Homayoun0Hossein Ebrahimpour-komleh1PhD, Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Kashan, Kashan, IranPhD, Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Kashan, Kashan, IranNowadays, 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.https://jbpe.sums.ac.ir/article_45654_9a5b539ad72696b984bf2f3d65d619af.pdfskin abnormalitiesabnormal tissue detectionmultiple sclerosisbreast cancermultiple pulmonary nodulesautomatic segmentationmedical imaging |
| spellingShingle | Hassan Homayoun Hossein Ebrahimpour-komleh Automated Segmentation of Abnormal Tissues in Medical Images Journal of Biomedical Physics and Engineering skin abnormalities abnormal tissue detection multiple sclerosis breast cancer multiple pulmonary nodules automatic segmentation medical imaging |
| title | Automated Segmentation of Abnormal Tissues in Medical Images |
| title_full | Automated Segmentation of Abnormal Tissues in Medical Images |
| title_fullStr | Automated Segmentation of Abnormal Tissues in Medical Images |
| title_full_unstemmed | Automated Segmentation of Abnormal Tissues in Medical Images |
| title_short | Automated Segmentation of Abnormal Tissues in Medical Images |
| title_sort | automated segmentation of abnormal tissues in medical images |
| topic | skin abnormalities abnormal tissue detection multiple sclerosis breast cancer multiple pulmonary nodules automatic segmentation medical imaging |
| url | https://jbpe.sums.ac.ir/article_45654_9a5b539ad72696b984bf2f3d65d619af.pdf |
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