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.
Highlights
Chest, colon, breast, liver, kidney and the vascular and skeletal systems
The aim of this study 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
Summary
Chest, colon, breast, liver, kidney and the vascular and skeletal systems. The early detection is the most. The aim of this study is to develop Computer Aided Diagnosis (CAD) system for the detection of brain tumor by using Metaheuristic Algorithms. Most of the radiologists achieve this goal with the process of image perception to recognize the unique image pattern to identify the relationship between the perceived patterns and the possible diagnosis Both detection and characterization processes depend heavily on the radiologists’ empirical knowledge, memory, intuition and diligence. The two key steps involved in the implementation of CAD system are segmentation and classification of suspicious regions (Jaya and Thanushkodi, 2009a). Enhancement: The pre-processed MRI brain image contains a high intensity salt and pepper noise which appears due to the presence of gray scale variations in using CAD system is performed in four phases namely: the image which is removed by applying suitable filters.
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