Detection of Prostate Cancer with MAPS Feature Model using Region Growing Algorithm
V. Parvathavarthini1, S. M. Ramesh2, M. Irshad Ahamed3
1V. Parvathavarthini, PG Scholor, Department of Electronics and Communication Engineering, EGS Pillay Engineering College, Nagapattinam (Tamil Nadu), India.
2S. M. Ramesh, Professor, Department of Electronics and Communication Engineering, E.G.S Pillay Engineering College, Nagapattinam (Tamil Nadu), India.
3M. Irshad Ahamed, Assistant Professor, Department of Electronics and Communication Engineering, E.G.S Pillay Engineering College, Nagapattinam (Tamil Nadu), India.
Manuscript received on June 02, 2017. | Revised Manuscript received on June 05, 2017. | Manuscript published on June 15, 2017. | PP: 6-10 | Volume-4, Issue-11, June 2017. | Retrieval Number: K10370641117/2017©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper we present a new method for automated and quantitative grading of prostate cancer. A total of 102 graph-based, morphological, and textural features are extracted from each tissue patch in order to quantify the arrangement of structures within digitized images of prostate cancer. A support vector machine (SVM) is used to classify the prostate into benign or malignant based on four appearance features extracted from registered images. Moreover, in this paper we introduce a new approach to generate level of cancer, that illustrate the propagation of diffusion in prostate tissues based on the analysis of the MAPS of the change of the gray level values of prostate voxel using (GGMRF) image model. Finally, the tumor boundaries are determined using a level set deformable model controlled by the diffusion information and the spatial interactions between the prostate voxels. Experimental results on 28 clinical diffusion weighted MRI data sets yield promising results.
Keywords: Classifiers; C Timages; MAPS; Prostate cancer.