Brain Tumor Detection using Hybrid Clustering with Estimate Arguing
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Abstract
Detection of tissues from MR brain images is quite difficult task in medical field applications. Segmentation is utilized to detect the tissues accurately. Many algorithms have been presented to detect the tissues from the MR brain images. Most of them were remained failure due to their inaccurate results. To resolve this problem, an analysis of tissues detection in MR images using hybridclustering with estimate arguing (HC-EA) is proposed. Our proposed methodology consists of pre-processing, tissues detection and calculating the estimated area of clustered tissues. Extensive simulated analysis shown that the effectiveness of proposed hybrid clustering approach. Our main
concentration is on detection of multi-tissues with an enhanced accuracy over conventional clustering algorithms like fuzzy c- means (FCM), K-means and manual segmentation.
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