Paper Info
Title
Diagnosis of prostatic carcinoma on multiparametric magnetic resonance imaging using shearlet transform.
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.
Year
DOI
Venue
2014
10.1109/EMBC.2014.6945103
EMBC
Keywords
Field
DocType
dynamic contrast enhanced images,apparent diffusion coefficient images,visual information,malignant region classification,shearlet transform,cancer cells,multiscale decomposition,prostate cancer,wavelet transforms,roi,biodiffusion,adc,mri,prostatic carcinoma diagnosis,small contours,benign region classification,histograms of shearlet coefficients,biomedical mri,cancer,svm,hsc,feature extraction,image classification,dce,conventional wavelet filters,edge detection,directional sensitivity,transverse relaxation time weighted images,prostate cancer diagnosis,t2w,region of interest,mp-mri,support vector machines,medical image processing,multiparametric magnetic resonance imaging
Computer vision,Effective diffusion coefficient,Histogram,Multiparametric Magnetic Resonance Imaging,Pattern recognition,Computer science,Support vector machine,Shearlet,Artificial intelligence,Region of interest,Carcinoma,Wavelet
Conference
Volume
ISSN
Citations 
2014
1557-170X
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Hadi Rezaeilouyeh131.08
Mohammad H. Mahoor286155.59
Jun Jason Zhang341.79
Francisco G La Rosa411.03
Samuel Chang500.34
P. N. Werahera6224.85