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Statistical Textural Distinctiveness in Multi-Parametric Prostate MRI for Suspicious Region Detection

TitleStatistical Textural Distinctiveness in Multi-Parametric Prostate MRI for Suspicious Region Detection
Publication TypeConference Paper
Year of Publication2015
AuthorsChung, A., C. Scharfenberger, F. Khalvati, A. Wong, and M. A. Haider
Conference NameInternational Conference on Image Analysis and Recognition (ICIAR)
Date Published07/2015
KeywordsComputer-aided prostate cancer detection, multi-parametric magnetic resonance imaging (MP-MRI), statistical textural distinctiveness, texture-based saliency
Abstract

Prostate cancer is the most diagnosed form of cancer, but survival rates are relatively high with sufficiently early diagnosis. Current computer-aided image-based cancer detection methods face notable challenges include noise in MRI images, variability between different MRI modalities, weak contrast, and non-homogeneous texture patterns, making it difficult for diagnosticians to identify tumour candidates. We propose a novel saliency-based method for identifying suspicious regions in multi-parametric MR prostate images based on statistical texture distinctiveness. In this approach, a sparse texture model is learned via expectation maximization from features derived from multi-parametric MR prostate images, and the statistical texture distinctiveness-based saliency based on this model is used to identify suspicious regions. The proposed method was evaluated using real clinical prostate MRI data, and results demonstrate a clear improvement in suspicious region detection relative to the state-of-art method.

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