Automatic Classification and Segmentation of Brain Tumor in CT Images using Optimal Dominant Gray level Run length Texture Features
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2011, Vol 2, Issue 10
Abstract
Tumor classification and segmentation from brain computed tomography image data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue among different patients and in many cases, similarity between tumor and normal tissue. This paper deals with an efficient segmentation algorithm for extracting the brain tumors in computed tomography images using Support Vector Machine classifier. The objective of this work is to compare the dominant grey level run length feature extraction method with wavelet based texture feature extraction method and SGLDM method. A dominant gray level run length texture feature set is derived from the region of interest (ROI) of the image to be selected. The optimal texture features are selected using Genetic Algorithm. The selected optimal run length texture features are fed to the Support Vector Machine classifier (SVM) to classify and segment the tumor from brain CT images. The method is applied on real data of CT images of 120 images with normal and abnormal tumor images. The results are compared with radiologist labeled ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of classification accuracy. From the analysis and performance measures like classification accuracy, it is inferred that the brain tumor classification and segmentation is best done using SVM with dominant run length feature extraction method than SVM with wavelet based texture feature extraction method and SVM with SGLDM method. In this work,we have attempted to improve the computing efficiency as it selects the most suitable feature extration method that can used for classification and segmentation of brain tumor in CT images efficiently and accurately. An avearage accuracy rate of above 97% was obtained usinh this classification and segmentation algorithm.
Authors and Affiliations
A. PADMA , R. SUKANESH
Keywords
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