Image-Based Computer-Aided Diagnostic System for Early Diagnosis of Prostate Cancer

The goal of this paper is to develop a computer-aided diagnostic (CAD) system for early detection of prostate cancer from diffusion-weighted magnetic resonance imaging (DW-MRI) acquired at different b-values. The proposed system consists of three main steps. First, the prostate is segmented using a hybrid framework that integrates geometric deformable model (level-sets) and nonnegative matrix factorization (NMF). Secondly, the apparent diffusion coefficient (ADC) of the segmented prostate volume is first estimated at different b-values and is then normalized and refined using a generalized Gauss-Markov random field (GGMRF) image model. Then, the cumulative distribution function (CDF) of the refined ADCs at different b-values are constructed. Finally, a two-stage structure of stacked non-negativity constraint auto-encoder (SNCAE) is trained to classify the prostate tumor as benign or malignant based on the constructed CDFs. In the first stage, classification probabilities are estimated at each b-value and in the second stage, those probabilities are fused and fed into the prediction stage SNCAE to calculate the final classification. Preliminary experiments on 53 clinical DW-MRI datasets resulted in \(98.11\,\%\) correct classification (sensitivity \(=96.15\,\%\) and specificity = \(100\,\%\)), indicating the high performance of the proposed CAD system and holding promise of the proposed system as a reliable non-invasive diagnostic tool.