TY - GEN
T1 - A new set of wavelet-and fractals-based features for gleason grading of prostate cancer histopathology images
AU - Lopez, Clara Mosquera
AU - Agaiana, Sos
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Gleason grading
KW - Haar wavelet features
KW - Prostate cancer
KW - SVM classification
KW - color fractal dimension
UR - http://www.scopus.com/inward/record.url?scp=84879536096&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84879536096&partnerID=8YFLogxK
U2 - 10.1117/12.1000193
DO - 10.1117/12.1000193
M3 - Conference contribution
AN - SCOPUS:84879536096
SN - 9780819494283
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Proceedings of SPIE-IS and T Electronic Imaging - Image Processing
T2 - Image Processing: Algorithms and Systems XI
Y2 - 4 February 2013 through 6 February 2013
ER -