Prostate cancer grading: Gland segmentation and structural features
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kien Nguyen | - |
dc.contributor.author | Sabata, Bikash | - |
dc.contributor.author | Jain, Anil K. | - |
dc.date.accessioned | 2021-09-06T20:09:09Z | - |
dc.date.available | 2021-09-06T20:09:09Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2012-05-01 | - |
dc.identifier.issn | 0167-8655 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/108464 | - |
dc.description.abstract | In this paper, we introduce a novel approach to grade prostate malignancy using digitized histopathological specimens of the prostate tissue. Most of the approaches proposed in the literature to address this problem utilize various textural features computed from the prostate tissue image. Our approach differs in that we only focus on the tissue structure and the well-known Gleason grading system specification. The color space representing the tissue image is investigated and basic components of the prostate tissue are detected. The components and their structural relationship constitute a complete gland region. Tissue structural features extracted from gland morphology are used to classify a tissue pattern into three major categories: benign, grade 3 carcinoma and grade 4 carcinoma. Our experiments show that the proposed method outperforms a texture-based method in the three-class classification problem and most of the two-class classification problems except for the grade 3 vs grade 4 classification. Based on these results, we propose a hierarchical (binary) classification scheme which utilizes the two methods and obtains 85.6% accuracy in classifying an input tissue pattern into one of the three classes. (C) 2011 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.title | Prostate cancer grading: Gland segmentation and structural features | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jain, Anil K. | - |
dc.identifier.doi | 10.1016/j.patrec.2011.10.001 | - |
dc.identifier.wosid | 000302973700018 | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION LETTERS, v.33, no.7, pp.951 - 961 | - |
dc.relation.isPartOf | PATTERN RECOGNITION LETTERS | - |
dc.citation.title | PATTERN RECOGNITION LETTERS | - |
dc.citation.volume | 33 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 951 | - |
dc.citation.endPage | 961 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordAuthor | Prostate cancer | - |
dc.subject.keywordAuthor | Benign | - |
dc.subject.keywordAuthor | Carcinoma | - |
dc.subject.keywordAuthor | Gleason grading system | - |
dc.subject.keywordAuthor | Gland segmentation | - |
dc.subject.keywordAuthor | Nuclei | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.