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Proceedings Paper

A new set of wavelet- and fractals-based features for Gleason grading of prostate cancer histopathology images
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Paper Abstract

Prostate cancer detection and staging is an important step towards patient treatment selection. Advancements in digital pathology allow the application of new quantitative image analysis algorithms for computer-assisted diagnosis (CAD) on digitized histopathology images. In this paper, we introduce a new set of features to automatically grade pathological images using the well-known Gleason grading system. The goal of this study is to classify biopsy images belonging to Gleason patterns 3, 4, and 5 by using a combination of wavelet and fractal features. For image classification we use pairwise coupling Support Vector Machine (SVM) classifiers. The accuracy of the system, which is close to 97%, is estimated through three different cross-validation schemes. The proposed system offers the potential for automating classification of histological images and supporting prostate cancer diagnosis.

Paper Details

Date Published: 19 February 2013
PDF: 12 pages
Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 865516 (19 February 2013); doi: 10.1117/12.1000193
Show Author Affiliations
Clara Mosquera Lopez, The Univ. of Texas at San Antonio (United States)
Sos Agaian, The Univ. of Texas at San Antonio (United States)

Published in SPIE Proceedings Vol. 8655:
Image Processing: Algorithms and Systems XI
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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