Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/227
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Barstuğan, Mücahid | - |
dc.contributor.author | Ceylan, Rahime | - |
dc.contributor.author | Asoğlu, Semih | - |
dc.contributor.author | Cebeci, Hakan | - |
dc.contributor.author | Koplay, Mustafa | - |
dc.date.accessioned | 2021-12-13T10:23:53Z | - |
dc.date.available | 2021-12-13T10:23:53Z | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 0169-2607 | - |
dc.identifier.issn | 1872-7565 | - |
dc.identifier.uri | https://doi.org/10.1016/j.cmpb.2018.07.009 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/227 | - |
dc.description.abstract | Background and objective: Adrenal tumors, which occur on adrenal glands, are incidentally determined. The liver, spleen, spinal cord, and kidney surround the adrenal glands. Therefore, tumors on the adrenal glands can be adherent to other organs. This is a problem in adrenal tumor segmentation. In addition, low contrast, non-standardized shape and size, homogeneity, and heterogeneity of the tumors are considered as problems in segmentation. Methods: This study proposes a computer-aided diagnosis (CAD) system to segment adrenal tumors by eliminating the above problems. The proposed hybrid method incorporates many image processing methods, which include active contour, adaptive thresholding, contrast limited adaptive histogram equalization (CLAHE), image erosion, and region growing. Results: The performance of the proposed method was assessed on 113 Magnetic Resonance (MR) images using seven metrics: sensitivity, specificity, accuracy, precision, Dice Coefficient, Jaccard Rate, and structural similarity index (SSIM). The proposed method eliminates some of the discussed problems with success rates of 74.84%, 99.99%, 99.84%, 93.49%, 82.09%, 71.24%, 99.48% for the metrics, respectively. Conclusions: This study presents a new method for adrenal tumor segmentation, and avoids some of the problems preventing accurate segmentation, especially for cyst-based tumors. (C) 2018 Elsevier B.V. All rights reserved. | en_US |
dc.language.iso | en | en_US |
dc.publisher | ELSEVIER IRELAND LTD | en_US |
dc.relation.ispartof | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Adrenal Tumor Segmentation | en_US |
dc.subject | Cad System | en_US |
dc.subject | Hybrid Approach | en_US |
dc.subject | Mr Images | en_US |
dc.subject | Ct | en_US |
dc.subject | Volumes | en_US |
dc.title | Adrenal tumor segmentation method for MR images | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.cmpb.2018.07.009 | - |
dc.identifier.pmid | PubMed: 30195434 | en_US |
dc.identifier.scopus | 2-s2.0-85050272714 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.identifier.volume | 164 | en_US |
dc.identifier.startpage | 87 | en_US |
dc.identifier.endpage | 100 | en_US |
dc.identifier.wos | WOS:000443709500009 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57200139642 | - |
dc.authorscopusid | 12244684600 | - |
dc.authorscopusid | 57203019010 | - |
dc.authorscopusid | 56033553000 | - |
dc.authorscopusid | 55920818900 | - |
dc.identifier.scopusquality | Q1 | - |
item.languageiso639-1 | en | - |
item.fulltext | With Fulltext | - |
item.cerifentitytype | Publications | - |
item.openairetype | Article | - |
item.grantfulltext | embargo_20300101 | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collections Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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1-s2.0-S0169260718308538-main.pdf Until 2030-01-01 | 4.82 MB | Adobe PDF | View/Open Request a copy |
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