A Novel Image Fusion Technique for Medical Images Based On Digital Shearlet Transform

Thamarai Selvi

Abstract


A tumor is abnormal tissue that develops by uncontrolled cell division. Regular cells develop in a controlled manner as new cells supplant old or harmed ones. For reasons not completely comprehended, tumor cells reproduce uncontrollably. To exact identification of size and area of brain tumor assumes an indispensable part in the diagnosis of tumors. Initially, wavelet and contourlet transforms based calculation for tumor identification which uses compliment and redundant data from the Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images. But wavelets won't get the smoothness along the contours and computational complexity of Contourlet Transform is too high and proper continuum theory is missing to detect brain tumor. A novel approach based 3D Digital Shearlet Transform (3DDST) comprises in a cascading of a multiscale decomposition and a directional filtering stage to remove noise in the image. In 3DDST, the low-pass subband is fused to use the fusion rule of the weighted sum. The fusion rule of the selecting maximum is to be denoised in the high-pass subbands. In experimental result, the 3DDST is evaluated better results in Peak signal to noise ratio (PSNR), Entropy, Space frequency and standard deviation compared with other fusion techniques Wavelet, Contourlet transform methods in terms of both visual quality and objective evaluation.


Keywords


Wavelet, Contourlet, 3DDST, FCM

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References


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