Segmentation of MRI Images Using Particle Swarm Optimization and Based on Support Vector Machine MRI Images for Tumor Detection Improved By Region Growing Algorithm
Author(s):
Pooja Verma, Parag Sohoni, Atul Kumar Gupta
Keywords:
Magnetic resonance imaging (MRI), computed tomography, Image Segmentation, Region of Interest (ROI)
Abstract
Brain tumor is the major cause of cancer deaths in human which is due to disorderly cells growth in brain portion. Earlier detection, diagnosis and precise repairing of brain tumor are the principal effort to avoid human death. Image segmentation can also be done in numerous approaches like thresholding, region growing, watersheds and contours. High-quality with their essential information do physical segment. In the paper, a new method is proposed for tumor detection using morphological operations to address brain tumor from MRI images to be used as a tool in real time during surgeries a new method using particle swarm optimization technique to recognize and remove the boundary condition of a brain tumor. Using abnormal images of a variety of brain tumors, this study give you an idea about that the proposed algorithm make available a robust technique in expressions of accuracy and computation time, making it suitable for real-time processing. Results also show that this algorithm is skilled of producing one-pixel-width continuous edges with accurate positioning of particular region where tumor was detected.
Article Details
Unique Paper ID: 146371

Publication Volume & Issue: Volume 4, Issue 12

Page(s): 564 - 573
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