A Multi-Parametric Diffusion Magnetic Resonance Imaging Texture Feature Model for Prostate Cancer Analysis

2014 ◽  
pp. 79-88 ◽  
Author(s):  
Farzad Khalvati ◽  
Amen Modhafar ◽  
Andrew Cameron ◽  
Alexander Wong ◽  
Masoom A. Haider
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Diffusion Magnetic Resonance Imaging ◽  
Texture Feature ◽  
Feature Model ◽  
Resonance Imaging

scholarly journals Superpixel-based Prostate Cancer Detection from Diffusion Magnetic Resonance Imaging

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Junjie Zhang ◽  
Farzad Khalvati ◽  
Alexander Wong ◽  
Masoom A. Haider
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Cancer Detection ◽  
Diffusion Magnetic Resonance Imaging ◽  
Detection Accuracy ◽  
Resonance Imaging ◽  
Prostate Cancer Detection ◽  
Computation Efficiency

This paper presents a superpixel-basedapproach to detectprostate cancers from diffusion magnetic resonance imaging(dMRI).In this approach,superpixel generatedcandidate regionsare incorporated in thequantitativeradiomics model, MAPS[1], to detectprostate cancers from dMRI modalities. Experimental resultsshowthe feasibility of the proposed superpixel-based approach with improved computation efficiency and detection accuracy.

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