MRI Segmentation for the Implementation of CAD System
The aim of this work 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. 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. 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. Medical image segmentation and detection at the early stage played vital roles for many health8related 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.
The aim of this work is to develop Computer Aided Diagnosis (CAD) system for the detection of brain tumor by using Metaheuristic Algorithms. Brain tissue has a complex structure, and its segmentation is an important step for deriving the computerized anatomical atlases as well as pre and intra operative guidance for therapeutic intervention .The accurate quantification of disease patterns in medical images allows the radiologists to track the status of the disease. Image analysis is still performed manually which is often a difficult and time8consuming task. As a result, there is an increasing need for computerized image analysis to facilitate image based diagnosis. Many investigators have carried out basic studies and clinical applications toward the development of modern computerized schemes called CAD system for detection and characterization of lesions in images such as brain, chest, colon, breast, liver, kidney and the vascular and skeletal systems. The early detection is the most effective way to reduce mortality
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