Abnormal Region Extraction from MR Brain Images using Hybrid Approach
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2018, Vol 9, Issue 12
Abstract
Automatic brain abnormality segmentation from magnetic resonance images is a key task that is performed by computer aided algorithm or manual extraction by a medical expert. The regions are often partitioned based on the similarities of intensities that persist in a particular region. MR brain image segmentation is a critical step that helps to identify the abnormal region. Accurate identification of this abnormal region helps the radiologist and surgeons in surgical process and research. Through this paper we present a hybrid approach of algorithms based on clustering approach like region and edge based algorithm involved in segmenting abnormal region from MR brain images. The method is an integration of region based (pillar K-means) and edge based (level set) segmentation algorithm that aims to segment the abnormal region precisely. Experimental results show that the proposed approach could attain segmentation efficiency of 89.2%, mitigating the segmentation errors that were prevalent with region or edge based algorithms.
Authors and Affiliations
Nikhil Gala, Kamalakar Desai
Keywords
Related Articles
- EP ID EP429162
- DOI 10.14569/IJACSA.2018.091230
- Views 88
- Downloads 0
Autonomous Monitoring System using Wi-Fi Economic
In this project, it is presented the implementation of an autonomous monitoring system using solar panels and connecting to the network through Wi-Fi. The system will collect meteorological data and transmit in real-time...
Investigating Students’ Achievements in Computing Science Using Human Metric
This study investigates the role of personality traits, motivation for career choice and study habits in students’ academic achievements in the computing sciences. A quantitative research method was employed. Data was co...
Leveraging A Multi-Objective Approach to Data Replication in Cloud Computing Environment to Support Big Data Applications
Increased data availability and high data access performance are of utmost importance in a large-scale distributed system such as data cloud. To address these issues data can be replicated in various locations in the sys...
Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey
Particle Swarm Optimization (PSO) has become popular choice for solving complex and intricate problems which are otherwise difficult to solve by traditional methods. The usage of the Particle Swarm Optimization technique...
Priority-Aware Virtual Machine Selection Algorithm in Dynamic Consolidation
In the past few years, many researchers attempted to tackle the problem of decreasing energy consumption in cloud data centers. One of the widely adopted techniques for this purpose is dynamic Virtual Machine (VM) consol...