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Texture and Shape based Classification of Brain Tumors using Linear Vector Quantization

Author(s): Neelam Marshkole | Bikesh Kumar Singh | A. S Thoke

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: 30;
Issue: 11;
Start page: 21;
Date: 2011;
Original page

Keywords: Medical image | shape | texture | malignant | benign tumor

Brain cancer is one of the most common and deadly disease in the world. Detection of the brain tumor in its early stage is the key to its cure. Early diagnosis of the brain tumor is very important. With the development in Artificial Intelligence AI and Soft Computing Techniques, ComputerAided Diagnosis CAD attracts more and more attention for brain tumor diagnosis. Accuracy and efficiency are two major issues in designing CAD systems. In the brain Magnetic Resonance Imaging MRI, the tumor may appear clearly but for further treatment, the physician also needs the quantification of the tumor area. The computer and image processing techniques can provide great help in analyzing the tumor area. In this paper features based on shape and texture of image were tested for analysis and classification of brain tumors. After feature extraction, linear vector quantization is used to classify brain tumor in to malignant and benign types. MATLAB 7.01 image processing toolbox and ANN toolbox have been used to implement the algorithm. The results show that texture and shape features can be effectively used for classifying brain tumor with high level of accuracy.
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Tango Jona
Tangokurs Rapperswil-Jona