A Novel Super Voxel-based 3D Segmentation Method for Irritable Bowel Syndrome 
Adithya Pothan Raj V1, Dr. Mohan Kumar P2
1Adithya Pothan Raj V, Research Scholar, School of Computing, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Dr. Mohan Kumar P, Professor, Department of Information Technology, Jeppiaar Engineering College, Chennai (Tamil Nadu), India.
Manuscript received on 02 September 2019 | Revised Manuscript received on 12 September 2019 | Manuscript Published on 23 September 2019 | PP: 1325-1331 | Volume-8 Issue-5C, May 2019 | Retrieval Number: E11890585C19/19©BEIESP | DOI: 10.35940/ijeat.E1189.0585C19
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© The Authors. 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: The main aim of this research paper is to implement a model-driven machine learning based adaptive 3D Segmentation Scheme for detecting the IBS (Irritable bowel syndrome) disease. This algorithm taking into account by endoscopy driven visual images for the purpose of machine analyzing and convert that 2D RGB coordinates into 3D RGB coordinates for improving the accuracy of the segmentation. In previous segmentation schemes, the IBS images are obtained by the use of ultrasound imaginary technique, but the main issue of the imaginary was the noise present in the images. We are overcoming this issue by applying the endoscopy images. Adaptive smoothing technique used in pre-processing stages with neighboring pixel reference. The feature data extraction stages estimate the shape and color and region-based features for segmentation. The proposed scheme performance with our 50 image Database shows that the results accuracy of proposed system outperforms multiple conventional segmentation methods.
Keywords: Image Segmentation, 3D Image Segmentation, Irritable Bowel Syndrome, Ant Colony Optimization, Adaptive Thresholding.
Scope of the Article: 3D Printing