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

Clara Mosquera Lopez; Sos Agaian

doi:10.1117/12.1000193

19 February 2013 A new set of wavelet- and fractals-based features for Gleason grading of prostate cancer histopathology images

Clara Mosquera Lopez, Sos Agaian

Author Affiliations +

Proceedings Volume 8655, Image Processing: Algorithms and Systems XI; 865516 (2013) https://doi.org/10.1117/12.1000193
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

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.

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Clara Mosquera Lopez and Sos Agaian "A new set of wavelet- and fractals-based features for Gleason grading of prostate cancer histopathology images", Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 865516 (19 February 2013); https://doi.org/10.1117/12.1000193

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