NDLI: Extraction of color features in the spectral domain to recognize centroblasts in histopathology

Content Provider	IEEE Xplore Digital Library
Author	Belkacem-Boussaid, K. Sertel, O. Lozanski, G. Shana'aah, A. Gurcan, M.
Copyright Year	2009
Description	Author affiliation: Department of Pathology, The Ohio State University, Columbus, OH 43210 USA (Lozanski, G.; Shana'aah, A.) \|\| Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210 USA (Belkacem-Boussaid, K.; Sertel, O.; Gurcan, M.)
Abstract	In this paper, we are proposing a novel automated method to recognize centroblast (CB) cells from non-centroblast (non-CB) cells for computer-assisted evaluation of follicular lymphoma tissue samples. The method is based on training and testing of a quadratic discriminant analysis (QDA) classifier. The novel aspects of this method are the identification of the CB object with prior information, and the introduction of the principal component analysis (PCA) in the spectral domain to extract color texture features. Both geometric and texture features are used to achieve the classification. Experimental results on real follicular lymphoma images demonstrate that the combined feature space improved the performance of the system significantly. The implemented method can identify centroblast cells (CB) from non-centroblast cells (non-CB) with a classification accuracy of 82.56%.
Starting Page	3685
Ending Page	3688
File Size	477658
Page Count	4
File Format	PDF
ISBN	9781424432967
ISSN	1557170X
DOI	10.1109/IEMBS.2009.5334727
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Principal component analysis Image color analysis Image processing USA Councils Testing Data mining Biomedical imaging Medical diagnostic imaging Pathology and PCA Follicular Lymphoma color texture geometric features spectral domain QDA
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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