Computer Aided Diagnosis: Detection and Localisation of Prostate Cancer within the Peripheral Zone.

We propose a methodology for prostate cancer detection and localisation within the peripheral zone based on combining multiple segmentation techniques. We extract four image features using Gaussian and median filters.

Subsequently, we use each image feature separately to generate binary segmentations. Finally, we take the intersection of all four binary segmentations, incorporating a model of the peripheral zone, and perform erosion to remove small false positive regions. The initial evaluation of this method is based on 275 MRI images from 37 patients and 86% of the slices were classified correctly with 87% and 86% sensitivity and specificity achieved, respectively. This paper makes two contributions: firstly, a novel Computer Aided Diagnosis approach which is based on combining multiple segmentation techniques using only a small number of simple image features. Secondly, the development of the proposed method and its application in prostate cancer detection and localisation using a single MRI modality with the results comparable to the state-of-the-art multi-modality and advanced computer vision methods in the literature. This article is protected by copyright All rights reserved.

International journal for numerical methods in biomedical engineering 2015 Aug 27 [Epub ahead of print]

Andrik Rampun, Zhili Chen, Paul Malcolm, Bernie Tiddeman, Reyer Zwiggelaar

Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK , [, Shenyang Jianzhu University, Liaoning, 110168, China , Department of Radiology, Norfolk Norwich University Hospital, Norwich, NR4 7UY, UK , Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK , Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK

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