DMCA
Comparative Analysis of Brain Tumor Detection using Different Segmentation Techniques
BibTeX
@MISC{Reddy_comparativeanalysis,
author = {Ramaswamy Reddy and E. V. Prasad and L. S. S. Reddy},
title = {Comparative Analysis of Brain Tumor Detection using Different Segmentation Techniques},
year = {}
}
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Abstract
In this study, we would like to present brain tumor detection methods, based on the conventional K-means technique, Expectation Maximization (EM) algorithm and a new Spatial Fuzzy-technique analysis of brain MR images. Though, the K-means and EM algorithm were already used in Brain MR image segmentation, as well as image segmentation in general, it fails to utilize the strong spatial correlation between neighboring pixels. A spatial Fuzzy C-means (SFCM’s) technique, which is utilize the spatial information properly and produce high quality segmentation of brain tumor images. Five ground truth images are taken to test the segmentation performance of K-means, EM algorithms and Spatial Fuzzy C-means technique. The segmentation results, which are proved more accurate segmentation by the SFCM’s compared to that of K-means and EM algorithm, are presented statistically and graphically.
Keyphrases
different segmentation technique comparative analysis brain tumor detection em algorithm segmentation performance new spatial fuzzy-technique analysis brain mr image segmentation image segmentation strong spatial correlation spatial information brain tumor image segmentation result accurate segmentation ground truth image conventional k-means technique expectation maximization high quality segmentation spatial fuzzy c-means spatial fuzzy c-means technique brain mr image brain tumor detection method
