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Automatic detection of brain tumor in magnetic resonance images using multi‐texton histogram and support vector machine
Author(s) -
Jayachandran A.,
Dhanasekaran R.
Publication year - 2013
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22041
Subject(s) - support vector machine , computer science , artificial intelligence , pattern recognition (psychology) , histogram , segmentation , brain tumor , artificial neural network , magnetic resonance imaging , feature extraction , feature vector , computer vision , image (mathematics) , radiology , medicine , pathology
Segmentation is the process of labeling objects in image data. It is a decisive phase in several medical imaging processing tasks for operation planning, radio therapy or diagnostics, and widely useful for studying the differences of healthy persons and persons with tumor. Magnetic Resonance Imaging brain tumor segmentation is a complicated task due to the variance and intricacy of tumors. In this article, a tumor segmentation scheme is presented, which focuses on the structural analysis on both tumorous and normal tissues. Our proposed method hits the target with the aid of the following major steps: (i) Tumor Region Location, (ii) Feature Extraction using Multi‐texton Technique, and (iii) Final Classification using support vector machine (SVM). The results for the tumor detection are validated through evaluation metrics such as, sensitivity, specificity, and accuracy. The comparative analysis is carried out by Radial Basis Function neural network and Feed Forward Neural Network. The obtained results depict that the proposed Multi‐texton histogram and support vector machine based brain tumor detection approach is more robust than the other classifiers in terms of sensitivity, specificity, and accuracy. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 97–103, 2013