This paper presents a computer-aided diagnosis scheme for the detection of prostate cancer. The pattern recognition scheme proposed, utilizes fused dynamic and morphological features extracted from magnetic resonance images (MRIs). The performance of the proposed scheme has been evaluated through extensive training and testing on several patient cases, where the staging of their condition has been previously evaluated by both ultrasoundguided biopsy and radiological assessment. The classification scheme is based on Probabilistic Neural Networks (PNNs), whose parameters are estimated using the Expectation-Maximization (EM) algorithm during a training phase. Fusion of the image characteristics is performed by properly aligning the respective TI-weighted dynamic and T2-weighted morphological images, allowing accurate feature selection from both images. The proposed classification scheme as well as the effect of fusion on the extracted features is tested, with respect to the correct classification rate (CCR) of each case.

Abstract-This paper presents an overview of a computeraided system for the detection of carcinomas in the prostate gland. The proposed system incorporates information from two different types of Magnetic Resonance Images (MRIs), namely the T2-weighted morphological images and the T1-weighted Dynamic Contrast Enhanced (DCE) images, to extract discriminative features that will be used in the training phase of a classification algorithm for the differentiation between malignant and benign tissue. The resulting feature vectors are also used for the assessment of new patient cases. The pattern recognition scheme is based on Probabilistic Neural Networks (PNNs). The parameters of the PNNs are estimated using the ExpectationMaximization (EM) algorithm. The performance of the proposed computer-aided detection system is evaluated through training and testing on several patient cases, whose condition has been previously assessed through ultrasound-guided biopsy and MRI examination.

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