ADVANCED CLUSTER BASED IMAGE SEGMENTATION

Abstract
This paper presents efficient and portable implementations of a useful image segmentation technique which makes use of the faster and a variant of the conventional connected components algorithm which we call parallel Components. In the Modern world majority of the doctors are need image segmentation as the service for various purposes and also they expect this system is run faster and secure. Usually Image segmentation Algorithms are not working faster. In spite of several ongoing researches in Conventional Segmentation and its Algorithms might not be able to run faster. So we propose a cluster computing environment for parallel image Segmentation to provide faster result. This paper is the real time implementation of Distributed Image Segmentation in Clustering of Nodes. We demonstrate the effectiveness and feasibility of our method on a set of Medical CT Scan Images. Our general framework is a single address space, distributed memory programming model. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient cluster process which uses a novel approach for parallel merging. Our experimental results are consistent with the theoretical analysis and practical results. It provides the faster execution time for segmentation, when compared with Conventional method. Our test data is different CT scan images from the Medical database. More efficient implementations of Image Segmentation will likely result in even faster execution times.

Authors
D. Kesavaraja1, R. Balasubramanian2, R.S. Rajesh3 and D. Sasireka4
1Dr. Sivanthi Aditanar College of Engineering,2,3Manonmaniam Sundaranar University4Jayamatha Engineering College

Keywords
Parallel Algorithms, Region Growing, Image Enhancement, Image Segmentation, Parallel Performance
Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 2 , Issue: 2 )
Date of Publication :
November 2011

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