Automated detection and grading of prostate cancer in multiparametric MRI

Prostate cancer is a major health issue worldwide and automatic segmentation of prostate from magnetic resonance imaging (MRI) is crucial task in image guided intervention. The objective of this paper is to develop a transparent and meticulous feature learning framework for prostate cancer detection and grading of prostate cancer using multiparametric magnetic resonance imaging (MPMRI). Prostate cancer is confirmed using approved rules of prostate cancer diagnosis from MPMRI data. The clustering is done in apparent diffusion coefficient (ADC) and diffusion weighted images (DWI) to obtain a probabilistic map which confirms cancerous region. The performance of proposed work is enormously tested on the dataset that contains T2Weighted, DWI and ADC map images of 236 subjects. In this study a total of 218 regions were used for analysis which includes 53 non-cancerous regions and 165 cancerous lesions. We have obtained tumour detection accuracy of 93.2% and AUC of 0.94 by using random forest classifier. The results yield by proposed algorithm is validated by two experienced radiologists.