Color based texture - Classification of hysteroscopy images of the endometrium

Neofytou, MS; Tanos, V; Pattichis, MS; Pattichis, CS; Kyriacou, EC; Pavlopoulos, S

dc.contributor.author	Neofytou, MS	en
dc.contributor.author	Tanos, V	en
dc.contributor.author	Pattichis, MS	en
dc.contributor.author	Pattichis, CS	en
dc.contributor.author	Kyriacou, EC	en
dc.contributor.author	Pavlopoulos, S	en
dc.date.accessioned	2014-03-01T02:44:31Z
dc.date.available	2014-03-01T02:44:31Z
dc.date.issued	2007	en
dc.identifier.issn	05891019	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31859
dc.subject	Early Detection	en
dc.subject	Neural Network Classifier	en
dc.subject	Statistical Learning	en
dc.subject	Texture Analysis	en
dc.subject	Texture Classification	en
dc.subject	Texture Features	en
dc.subject	Gray Scale Median	en
dc.subject	Region of Interest	en
dc.subject.other	Feature extraction	en
dc.subject.other	Neural networks	en
dc.subject.other	Support vector machines	en
dc.subject.other	Textures	en
dc.subject.other	Endometrium	en
dc.subject.other	Gynaecological cancer	en
dc.subject.other	Hysteroscopy images	en
dc.subject.other	Regions of Interest (ROI)	en
dc.subject.other	Statistical learning	en
dc.subject.other	Image classification	en
dc.title	Color based texture - Classification of hysteroscopy images of the endometrium	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IEMBS.2007.4352427	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IEMBS.2007.4352427	en
heal.identifier.secondary	4352427	en
heal.publicationDate	2007	en
heal.abstract	The objective of this study was to develop a CAD system for the classification of hysteroscopy images of the endometrium based on color texture analysis for the early detection of gynaecological cancer. A total of 416 Regions of Interest (ROIs) of the endometrium were extracted (208 normal and 208 abnormal) from 40 subjects. RGB images were gamma corrected and were converted to the HSV and YCrCb color systems. The following texture features were extracted for each channel of the RGB, HSV, and YCrCb systems: (1) Statistical Features, (ii) Spatial Gray Level Dependence Matrices and (iii) Gray Level Difference Statistics. The PNN statistical learning and SVM neural network classifiers were also investigated for classifying normal and abnormal ROIs. Results show that there is significant difference (using the Wilcoxon Rank Sum Test at a=0.05) between the texture features of normal and abnormal ROIs of the endometrium. Abnormal ROIs had higher gray scale median, variance, entropy and contrast and lower gray scale median and homogeneity values when compared to the normal ROIs. The highest percentage of correct classifications score was 79% and was achieved for the SVM models trained with the SF and GLDS features for differentiating between normal and abnormal ROIs. Concluding, a CAD system based on texture analysis and SVM models can be used to classify normal and abnormal endometrium tissue. Further work is needed to validate the system in more cases and organs. © 2007 IEEE.	en
heal.journalName	Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings	en
dc.identifier.doi	10.1109/IEMBS.2007.4352427	en
dc.identifier.spage	864	en
dc.identifier.epage	867	en