%0 Journal Article %1 noauthororeditor %A Parvathavarthini, V. %A Ramesh, S. M. %A Ahamed, M. Irshad %D 2017 %E Kumar, Dr. Shiv %J International Journal of Innovative Science and Modern Engineering (IJISME) %K %N 11 %P 6-10 %T Detection of Prostate Cancer with MAPS Feature Model using Region Growing Algorithm %U https://www.ijisme.org/wp-content/uploads/papers/v4i11/K10370641117.pdf %V 4 %X 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.

@article{noauthororeditor, 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.}, added-at = {2021-09-17T13:57:28.000+0200}, author = {Parvathavarthini, V. and Ramesh, S. M. and Ahamed, M. Irshad}, biburl = {https://www.bibsonomy.org/bibtex/2aa2758df663bf5f3121aff16adc75759/ijisme_beiesp}, editor = {Kumar, Dr. Shiv}, interhash = {b3fbd166d31014586a71f63cfb840ff7}, intrahash = {aa2758df663bf5f3121aff16adc75759}, issn = {2319-6386}, journal = {International Journal of Innovative Science and Modern Engineering (IJISME)}, keywords = {}, language = {En}, month = {June}, number = 11, pages = {6-10}, timestamp = {2021-09-17T13:57:28.000+0200}, title = {Detection of Prostate Cancer with MAPS Feature Model using Region Growing Algorithm}, url = {https://www.ijisme.org/wp-content/uploads/papers/v4i11/K10370641117.pdf}, volume = 4, year = 2017 }