Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume 79, Issue 15-16, Pages 10955-10973Publisher
SPRINGER
DOI: 10.1007/s11042-019-7324-y
Keywords
Magnetic resonance imaging (MRI); Gray level co-occurrence matrix (GLCM); Potential differential diffusion filter (PDDF); Local binary pattern (LBP); LBP based GLCM (C2LBPGLCM)
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The physical appearance of a brain tumor in human beings may be an indication of problems in psychological (cognitive) functions. Such functions include learning, understanding, problem solving, decision making, and planning. Early brain tumor detection can be done by using the proper procedure of screening. MRI is used for the detection of disease staging and follow-up without ionization radiation. In this manuscript, an automated system is proposed for the analysis of brain data and detection of cognitive functions abnormalities. The region of interest (ROI) is enhanced using a proposed partial differential diffusion filter (PDDF) which is a modified form of anisotropic diffusion filter. Otsu algorithm is used for better segmentation. Moreover, a new method is also proposed for feature extraction which is a concatenation of local binary pattern (LBP) and Gray level co-occurrence matrix (C2LBPGLCM). The proposed method accurately distinguishes between healthy and unhealthy images with high specificity, sensitivity, and area under the curve.
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