Computer-aided diagnostic system for prostate cancer detection and characterization combining learned dictionaries and supervised classification - Archive ouverte HAL
Communication Dans Un Congrès Année : 2014

Computer-aided diagnostic system for prostate cancer detection and characterization combining learned dictionaries and supervised classification

Résumé

This paper aims at presenting results of a computer-aided diagnostic (CAD) system for voxel based detection and characterization of prostate cancer in the peripheral zone based on multiparametric magnetic resonance (mp-MR) imaging. We propose an original scheme with the combination of a feature extraction step based on a sparse dictionary learning (DL) method and a supervised classification in order to discriminate normal {N}, normal but suspect {NS} tissues as well as different classes of cancer tissue whose aggressiveness is characterized by the Gleason score ranging from 6 {GL6} to 9 {GL9}. We compare the classification performance of two supervised methods, the linear support vector machine (SVM) and the logistic regression (LR) classifiers in a binary classification task. Classification performances were evaluated over an mp-MR image database of 35 patients where each voxel was labeled, based on a ground truth, by an expert radiologist. Results show that the proposed method in addition to being explicable thanks to the sparse representation of the voxels compares well (AUC>0.8) with recent state-of-the-art performances. Preliminary visual analysis of example patient cancer probability maps indicate that cancer probabilities tend to increase as a function of the Gleason score.
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Dates et versions

hal-01117669 , version 1 (17-02-2015)

Identifiants

Citer

J. Lehaire, R. Flamary, Olivier Rouviere, C. Lartizien. Computer-aided diagnostic system for prostate cancer detection and characterization combining learned dictionaries and supervised classification. IEEE International Conference on Image Processing (ICIP) 2014, Oct 2014, Paris, France. pp.2251 - 2255, ⟨10.1109/ICIP.2014.7025456⟩. ⟨hal-01117669⟩
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