Texture analysis of tissues in Gleason grading of prostate cancer

Alexandratou, E; Yova, D; Gorpas, D; Maragos, P; Agrogiannis, G; Kavantzas, N

dc.contributor.author	Alexandratou, E	en
dc.contributor.author	Yova, D	en
dc.contributor.author	Gorpas, D	en
dc.contributor.author	Maragos, P	en
dc.contributor.author	Agrogiannis, G	en
dc.contributor.author	Kavantzas, N	en
dc.date.accessioned	2014-03-01T02:51:50Z
dc.date.available	2014-03-01T02:51:50Z
dc.date.issued	2008	en
dc.identifier.issn	16057422	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35674
dc.subject	Gleason scoring	en
dc.subject	Gray Level Co-occurrence Matrix (GLCM)	en
dc.subject	prostate cancer	en
dc.subject	Texture analysis techniques	en
dc.subject.other	Cancer prognosis	en
dc.subject.other	Computer vision techniques	en
dc.subject.other	Decision supports	en
dc.subject.other	Diagnostic ability	en
dc.subject.other	Discrimination analysis	en
dc.subject.other	Gleason grading	en
dc.subject.other	Gleason scores	en
dc.subject.other	Gleason scoring	en
dc.subject.other	Gold standards	en
dc.subject.other	Gray level co-occurrence matrix	en
dc.subject.other	Histopathological images	en
dc.subject.other	Image fields	en
dc.subject.other	Image properties	en
dc.subject.other	Image texture analysis	en
dc.subject.other	matrix	en
dc.subject.other	Multiparameters	en
dc.subject.other	Novel methods	en
dc.subject.other	Prostate cancers	en
dc.subject.other	Second order statistics	en
dc.subject.other	Texture analysis	en
dc.subject.other	Texture analysis techniques	en
dc.subject.other	Texture characteristics	en
dc.subject.other	Treatment planning	en
dc.subject.other	Variable selection	en
dc.subject.other	Whole process	en
dc.subject.other	Biomolecules	en
dc.subject.other	Computer vision	en
dc.subject.other	Decision support systems	en
dc.subject.other	Diseases	en
dc.subject.other	Histology	en
dc.subject.other	Image segmentation	en
dc.subject.other	Surfaces	en
dc.subject.other	Textures	en
dc.subject.other	Tissue	en
dc.subject.other	Grading	en
dc.title	Texture analysis of tissues in Gleason grading of prostate cancer	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1117/12.763377	en
heal.identifier.secondary	http://dx.doi.org/10.1117/12.763377	en
heal.identifier.secondary	685904	en
heal.publicationDate	2008	en
heal.abstract	Prostate cancer is a common malignancy among maturing men and the second leading cause of cancer death in USA. Histopathological grading of prostate cancer is based on tissue structural abnormalities. Gleason grading system is the gold standard and is based on the organization features of prostatic glands. Although Gleason score has contributed on cancer prognosis and on treatment planning, its accuracy is about 58%, with this percentage to be lower in GG2, GG3 and GG5 grading. On the other hand it is strongly affected by ""inter- and intra observer variations"", making the whole process very subjective. Therefore, there is need for the development of grading tools based on imaging and computer vision techniques for a more accurate prostate cancer prognosis. The aim of this paper is the development of a novel method for objective grading of biopsy specimen in order to support histopathological prognosis of the tumor. This new method is based on texture analysis techniques, and particularly on Gray Level Co-occurrence Matrix (GLCM) that estimates image properties related to second order statistics. Histopathological images of prostate cancer, from Gleason grade2 to Gleason grade 5, were acquired and subjected to image texture analysis. Thirteen texture characteristics were calculated from this matrix as they were proposed by Haralick. Using stepwise variable selection, a subset of four characteristics were selected and used for the description and classification of each image field. The selected characteristics profile was used for grading the specimen with the multiparameter statistical method of multiple logistic discrimination analysis. The subset of these characteristics provided 87% correct grading of the specimens. The addition of any of the remaining characteristics did not improve significantly the diagnostic ability of the method. This study demonstrated that texture analysis techniques could provide valuable grading decision support to the pathologists, concerning prostate cancer prognosis. © 2008 Copyright SPIE - The International Society for Optical Engineering.	en
heal.journalName	Progress in Biomedical Optics and Imaging - Proceedings of SPIE	en
dc.identifier.doi	10.1117/12.763377	en
dc.identifier.volume	6859	en