Implementation of Computer Aided Diagnosis System Based on Parallel Approach of Ant Based Medical Image Segmentation

Abstract

Problem statement: The aim of this study is to develop Computer Aided Diagnosis (CAD) system for the detection of brain tumor by using parallel implementation of ACO system for medical image segmentation applications due to the rapid execution for obtaining and extracting the Region of Interest (ROI) from the images for diagnostic purposes in medical field. Approach: For ROI segmentation, metaheuristic based Parallel Ant colony Optimization (PACO) approach has been implemented. The system has been simulated in the Mat lab for the parallel processing, using the master slave approach and information exchange. The scheme is tested up to 10 real time MRI brain images. Here parallelism is inherent in program loops, which focused on performing searching operation in parallel. Results: The computational results shows that parallel ACO systems uses the concept of the parallelization approach enabled the utilization of the intensity similarity measurement technique because of the capability of parallel processing. Conclusion: Medical image segmentation and detection at the early stage played vital roles for many health-related applications such as medical diagnostics, drug evaluation, medical research, training and teaching. Due to the rapid progress in the technologies for segmenting digital images for diagnostic purposes in medical field parallel Ant based CAD system are technologically feasible for Medical Domain which will certainly reduce the mortality rate.