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Detection of Tumor Using Gabor Filter for Multimodal Images

Imaging segmentation techniques play a significant factor in medical justification for diagnosis and therapy application in healthcare industries. These noninvasive procedures assist the physician to visualize the vital part of the human body planned for treatment. Multimodal fused
 images from Computer tomography (CT) and Magnetic resonance imaging (MRI) provides prominent results in detection of the tumor. Maximum information about the image cannot be obtained from individual technique to assess the location and its dimension of tumor. A fusion of multimodal images
 like MRI and CT images are used to complimentary information and its segmentation to detect the presence or absence of tumor using objective method. In this paper fusion of CT and MRI is done by a hybrid technique by combining Principal Component Analysis (PCA) and Curvelet Transformation
 (CVT). Gabor filter based segmentation of this image is applied as post-processing to obtain the presence of exact location of tumor in the image. Performance of fusion and segmentation is analyzed to obtain better quality image. The simulation consequence has shown better images using a hybrid
 fusion algorithm and Gabor filter is used for assisting the physician to find the presence or absence of tumor. Proposed approach based on simulation results has shown a better efficiency as compared to individual techniques.