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J. Imaging 2016, 2(3), 25; doi:10.3390/jimaging2030025

Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning

Department of Electrical and Computer Engineering, University of Denver, Denver, CO 80208, USA
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Academic Editors: Gonzalo Pajares Martinsanz, Philip Morrow and Kenji Suzuki
Received: 11 May 2016 / Revised: 5 September 2016 / Accepted: 6 September 2016 / Published: 9 September 2016
(This article belongs to the Special Issue Image and Video Processing in Medicine)
View Full-Text   |   Download PDF [3930 KB, uploaded 15 September 2016]   |  

Abstract

The Gleason grading system is generally used for histological grading of prostate cancer. In this paper, we first introduce using the Shearlet transform and its coefficients as texture features for automatic Gleason grading. The Shearlet transform is a mathematical tool defined based on affine systems and can analyze signals at various orientations and scales and detect singularities, such as image edges. These properties make the Shearlet transform more suitable for Gleason grading compared to the other transform-based feature extraction methods, such as Fourier transform, wavelet transform, etc. We also extract color channel histograms and morphological features. These features are the essential building blocks of what pathologists consider when they perform Gleason grading. Then, we use the multiple kernel learning (MKL) algorithm for fusing all three different types of extracted features. We use support vector machines (SVM) equipped with MKL for the classification of prostate slides with different Gleason grades. Using the proposed method, we achieved high classification accuracy in a dataset containing 100 prostate cancer sample images of Gleason Grades 2–5. View Full-Text
Keywords: Gleason grading; multiple kernel learning; prostate cancer; Shearlet transform; texture analysis Gleason grading; multiple kernel learning; prostate cancer; Shearlet transform; texture analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Rezaeilouyeh, H.; Mahoor, M.H. Automatic Gleason Grading of Prostate Cancer Using Shearlet Transform and Multiple Kernel Learning. J. Imaging 2016, 2, 25.

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