Research Article
Detection of Brain Tumor using Hybrid Topology Preserving Map
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 51 - Issue 20 |
| Published: August 2012 |
| Authors: M. C. Jobin Christ, R. M. S. Parvathi |
10.5120/8159-1943
|
M. C. Jobin Christ, R. M. S. Parvathi . Detection of Brain Tumor using Hybrid Topology Preserving Map. International Journal of Computer Applications. 51, 20 (August 2012), 29-32. DOI=10.5120/8159-1943
@article{ 10.5120/8159-1943,
author = { M. C. Jobin Christ,R. M. S. Parvathi },
title = { Detection of Brain Tumor using Hybrid Topology Preserving Map },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 51 },
number = { 20 },
pages = { 29-32 },
doi = { 10.5120/8159-1943 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A M. C. Jobin Christ
%A R. M. S. Parvathi
%T Detection of Brain Tumor using Hybrid Topology Preserving Map%T
%J International Journal of Computer Applications
%V 51
%N 20
%P 29-32
%R 10.5120/8159-1943
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Segmentation of an image is the division or separation of the image into disjoint regions of similar feature. In clinical practice, Magnetic Resonance Imaging (MRI) is used to distinguish pathologic tissue from normal tissues, especially for the detection of brain tumors. Among the thousands of segmentation methods, clustering algorithm is one of the powerful tools in segmenting medical images. In this paper we proposed a new approach based on Hybrid Topology Preserving Map (HTPM) and Adaptive Mean Shift (AMS) algorithm which combines the advantages of both mean shift based clustering algorithm and Hierarchical Topology Preserving Map. And also the performance of the proposed method is compared with the traditional method Adaptive Mean Shift (AMS) algorithm.
References
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- M. C. Jobin Christ and R. M. S. Parvathi, 2011, "Segmentation of Medical Image using Clustering and Watershed Algorithms", American Journal of Applied Sciences, Vol. 8, No. 12, pp: 1349-1352.
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