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Multi-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features

DC FieldValueLanguage
dc.contributor.author조남훈-
dc.date.accessioned2020-02-11T06:40:07Z-
dc.date.available2020-02-11T06:40:07Z-
dc.date.issued2019-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/174777-
dc.description.abstractMicroscopic biopsy images are coloured in nature because pathologists use the haematoxylin and eosin chemical colour dyes for biopsy examinations. In this study, biopsy images are used for histological grading and the analysis of benign and malignant prostate tissues. The following PCa grades are analysed in the present study: benign, grade 3, grade 4, and grade 5. Biopsy imaging has become increasingly important for the clinical assessment of PCa. In order to analyse and classify the histological grades of prostate carcinomas, pixel-based colour moment descriptor (PCMD) and gray-level co-occurrence matrix (GLCM) methods were used to extract the most significant features for multilayer perceptron (MLP) neural network classification. Haar wavelet transformation was carried out to extract GLCM texture features, and colour features were extracted from RGB (red/green/blue) colour images of prostate tissues. The MANOVA statistical test was performed to select significant features based on F-values and P-values using the R programming language. We obtained an average highest accuracy of 92.7% using level-1 wavelet texture and colour features. The MLP classifier performed well, and our study shows promising results based on multi-feature classification of histological sections of prostate carcinomas.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherMDPI-
dc.relation.isPartOfCANCERS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleMulti-Features Classification of Prostate Carcinoma Observed in Histological Sections: Analysis of Wavelet-Based Texture and Colour Features-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Pathology (병리학교실)-
dc.contributor.googleauthorSubrata Bhattacharjee-
dc.contributor.googleauthorCho-Hee Kim-
dc.contributor.googleauthorHyeon-Gyun Park-
dc.contributor.googleauthorDeekshitha Prakash-
dc.contributor.googleauthorNuwan Madusanka-
dc.contributor.googleauthorNam-Hoon Cho-
dc.contributor.googleauthorHeung-Kook Choi-
dc.identifier.doi10.3390/cancers11121937-
dc.contributor.localIdA03812-
dc.relation.journalcodeJ03449-
dc.identifier.eissn2072-6694-
dc.identifier.pmid31817111-
dc.subject.keywordcolour features-
dc.subject.keywordhistological sections-
dc.subject.keywordmicroscopic biopsy image-
dc.subject.keywordmultilayer perceptron-
dc.subject.keywordneural network-
dc.subject.keywordprostate carcinoma-
dc.subject.keywordtexture features-
dc.subject.keywordwavelet transform-
dc.contributor.alternativeNameCho, Nam Hoon-
dc.contributor.affiliatedAuthor조남훈-
dc.citation.volume11-
dc.citation.number12-
dc.citation.startPageE1937-
dc.identifier.bibliographicCitationCANCERS, Vol.11(12) : E1937, 2019-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers

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