NDLI: Diagnosis of prostatic Carcinoma on multiparametric magnetic resonance imaging using shearlet transform

Content Provider	IEEE Xplore Digital Library
Author	Rezaeilouyeh, H. Mahoor, M.H. Zhang, J.J. La Rosa, F.G. Chang, S. Werahera, P.N.
Copyright Year	2014
Description	Author affiliation: Anschutz Med. Campus, Univ. of Colorado, Aurora, CO, USA (Chang, S.; Werahera, P.N.) \|\| Dept. of Electr. & Comput. Eng., Univ. of Denver, Denver, CO, USA (Rezaeilouyeh, H.; Mahoor, M.H.; Zhang, J.J.; La Rosa, F.G.)
Abstract	This paper presents a method to diagnose prostate cancer on multiparametric magnetic resonance imaging (Mp-MRI) using the shearlet transform. The objective is classification of benign and malignant regions on transverse relaxation time weighted (T2W), dynamic contrast enhanced (DCE), and apparent diffusion coefficient (ADC) images. Compared with conventional wavelet filters, shearlet has inherent directional sensitivity, which makes it suitable for characterizing small contours of cancer cells. By applying a multi-scale decomposition, the shearlet transform captures visual information provided by edges detected at different orientations and multiple scales in each region of interest (ROI) of the images. ROIs are represented by histograms of shearlet coefficients (HSC) and then used as features in Support Vector Machines (SVM) to classify ROIs as benign or malignant. Experimental results show that our method can recognize carcinoma in T2W, DCE, and ADC with overall sensitivity of 92%, 100%, and 89%, respectively. Hence, application of shearlet transform may further increase utility of Mp-MRI for prostate cancer diagnosis.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	6442
Ending Page	6445
File Size	827834
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6945103
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Transforms Prostate cancer Imaging Tumors Feature extraction Histograms Shearlet transform MRI
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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