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Automatic MR Brain Tumor Detection using Possibilistic C-Means and K-Means Clustering with Color Segmentation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 56 - Number 2 |
| Year of Publication: 2012 |
| Authors: Hema Rajini.n, Bhavani.r |
10.5120/8862-2825
|
Hema Rajini.n, Bhavani.r . Automatic MR Brain Tumor Detection using Possibilistic C-Means and K-Means Clustering with Color Segmentation. International Journal of Computer Applications. 56, 2 ( October 2012), 11-17. DOI=10.5120/8862-2825
Abstract
Magnetic resonance imaging is often the medical imaging method of choice when soft-tissue delineation is necessary. This paper presents a new approach for automated detection of brain tumor based on k-means and possibilistic c-means clustering with color segmentation, which separates brain tumor from healthy tissues in magnetic resonance images. The magnetic resonance feature images used for the tumor detection consist of T1-weighted and T2-weighted images for each axial slice through the head. The proposed method consists of three stages namely pre-processing, segmentation and feature extraction. In the first stage, we have suppressed the noise using image filtering. In the second stage, segmentation is computed using an unsupervised k-means and possibilistic c-means clustering algorithm with color conversion. The segmentation accuracy is obtained using the silhouette method. The experimental results show the superiority of the possibilistic c-means clustering method. In the third stage, the key features are extracted using the threshold. The application of the proposed method for tracking tumor is demon¬strated to help pathologists distinguish exactly tumor size and region.
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