Brain tumor segmentation is one of the crucial procedures in surgical and treatment planning. Brain tumor segmentation using MRI has been an intense research area. Brain tumors can have various sizes and shapes and may appear at different locations. Varying intensity of tumors in brain magnetic resonance images (MRI) makes the automatic segmentation of tumors extremely challenging. There are various intensity based techniques which have been proposed to segment tumors on magnetic resonance images. Texture is one of most popular feature for image classification and retrieval. The multifractal texture estimation methods are more time consuming. A texture based image segmentation using GLCM (Gray-Level Co-occurence Matrix) combined with AdaBoost classifier is proposed here. From the MRI images of brain, the optimal texture features of brain tumor are extracted by utilizing GLCM. Then using these features AdaBoost classifier algorithm classifies the tumor and non-tumor tissues and tumor is segmented. This method provides more efficient brain tumor segmentation compared to the segmentation technique based on mBm and will provide more accurate result.
Athira V S, Anand J Dhas, Sreejamole S S
AdaBoost Classifier, Brain tumor, Feature extraction, GLCM, Segmentation
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|Published in :
||Volume 1 | Issue 3 | May-June - 2015
|Date of Publication
Cite This Article
Athira V S, Anand J Dhas, Sreejamole S S, "Brain Tumor Detection and Segmentation in MR images Using GLCM and AdaBoost Classifier", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 3, pp.142-146, May-June-2015.
URL : http://ijsrset.com/IJSRSET1522140.php