NDLI: Comparing clustering methods and color spaces for histological images

Content Provider	IEEE Xplore Digital Library
Author	Tafavogh, Siamak
Copyright Year	2011
Description	Author affiliation: Center of Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney PO Box 123, Broadway, NSW 2007 Australia (Tafavogh, Siamak)
Abstract	Computer Aided Diagnosis (CAD) in histopathology is beginning to be applied to histological images. The CAD system in histopathology are used to assist pathologists in diagnosing deceases. Choosing appropriate image segmentation techniques improves quality of CAD system. The aim of this paper is to evaluate and compare two popular histological segmentation techniques based on clustering such as Kmeans and Expectation Maximization (EM) clustering methods. Kmeans and EM are applied to the Hematoxylin & Eosin (H&E) stained specimens images in Lab* and HSV color spaces. Experimental results demonstrate that HSV overtakes Lab* in improving the segmentation quality of Kmeans and EM. Moreover the results of experiments indicate that segmentation quality of Kmeans outperforms EM when is implemented in HSV color space.
Starting Page	153
Ending Page	158
File Size	447074
Page Count	6
File Format	PDF
ISBN	9781457708411
ISSN	18455921
e-ISBN	9789531841597
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-04
Publisher Place	Croatia
Access Restriction	Subscribed
Subject Keyword	Image color analysis Image segmentation Design automation Measurement Tumors Training Feature extraction
Content Type	Text
Resource Type	Article

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