COMPUTER AIDED SEGMENTATION OF BRAIN TISSUES USING SOFT COMPUTING TECHNIQUES | Science Publications

American Journal of Applied Sciences

I. Jacob Raglend¹  D. Ramkumar¹  K. Batri¹ 

关键词: Brain MRI;    ANFIS;    Curvelet;    Medical Diagnostic Imaging;    Medical Image Compression;   

DOI  :  10.3844/ajassp.2014.1016.1024

学科分类：自然科学（综合）

来源: Science Publications

【 摘 要 】

In this study, an efficient computer aided classification of brain tissue in to Gray Matter (GM), White Matter (WM) and Cerebro-Spinal Fluid (CSF) is proposed. The proposed work consists of the following sub blocks like denosing, feature extraction and Classifier. This initial partition is performed by ANFIS after extracting the textural features like local binary pattern and histogram features. The main motivation behind this research work is to classify the brain tissue. By comparing the proposed method with other conventional methods, it is clear that our algorithm can estimate the correct tissues WM, GM and CSF much more accurately than the existing algorithms with respect to ground truth image patterns. We achieved an accuracy rate of 98.9% for Gray matter segmentation, 94.1% for White matter segmentation and 90.8% for CSF segmentation.

【 授权许可】

Unknown