OBJECTIVE: The aim of this study is to provide an automatic framework for computer-aided analysis of multiparametric magnetic resonance (mp-MR) images of prostate. METHOD: We introduce a novel method for the unsupervised analysis of the images. An evidential C-means classifier was adapted for use with a segmentation scheme to address multisource data and to manage conflicts and redundancy. RESULTS: Experiments were conducted using data from 15 patients. The evaluation protocol consisted in evaluating the method abilities to classify prostate tissues, showing the same behaviour on the mp-MR images, into homogeneous classes. As the actual diagnosis was available, thanks to the correlation with histopathological findings, the assessment focused on the ability to segment cancer foci. The method exhibited global sensitivity and specificity of 70 and 88 %, respectively. CONCLUSION: The preliminary results obtained by these initial experiments showed that the method can be applied in clinical routine practice to help making decision especially for practitioners with limited experience in prostate MRI analysis.
OBJECTIVE: The aim of this study is to provide an automatic framework for computer-aided analysis of multiparametric magnetic resonance (mp-MR) images of prostate. METHOD: We introduce a novel method for the unsupervised analysis of the images. An evidential C-means classifier was adapted for use with a segmentation scheme to address multisource data and to manage conflicts and redundancy. RESULTS: Experiments were conducted using data from 15 patients. The evaluation protocol consisted in evaluating the method abilities to classify prostate tissues, showing the same behaviour on the mp-MR images, into homogeneous classes. As the actual diagnosis was available, thanks to the correlation with histopathological findings, the assessment focused on the ability to segment cancer foci. The method exhibited global sensitivity and specificity of 70 and 88 %, respectively. CONCLUSION: The preliminary results obtained by these initial experiments showed that the method can be applied in clinical routine practice to help making decision especially for practitioners with limited experience in prostate MRI analysis.