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http://dx.doi.org/10.9717/kmms.2016.19.4.736

Automated Prostate Cancer Detection on Multi-parametric MR imaging via Texture Analysis  

Kim, YoungGi (Dept. of Multimedia Engineering, Seoul Women's University)
Jung, Julip (Dept. of Multimedia Engineering, Seoul Women's University)
Hong, Helen (Dept. of Multimedia Engineering, Seoul Women's University)
Hwang, Sung Il (Dept. of Radiology, Seoul National University Bundang Hospital)
Publication Information
Journal of Korea Multimedia Society / v.19, no.4, 2016 , pp. 736-746 More about this Journal
Abstract
In this paper, we propose an automatic prostate cancer detection method using position, signal intensity and texture feature based on SVM in multi-parametric MR images. First, to align the prostate on DWI and ADC map to T2wMR, the transformation parameters of DWI are estimated by normalized mutual information-based rigid registration. Then, to normalize the signal intensity range among inter-patient images, histogram stretching is performed. Second, to detect prostate cancer areas in T2wMR, SVM classification with position, signal intensity and texture features was performed on T2wMR, DWI and ADC map. Our feature classification using multi-parametric MR imaging can improve the prostate cancer detection rate on T2wMR.
Keywords
Multi-parametric MRI; Prostate Cancer Detection; Texture Analysis; Support Vector Machine;
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Times Cited By KSCI : 2  (Citation Analysis)
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