Medical Image Fusion using NSCT and Spatial Fuzzy Segmentation

Medical image fusion is the tool for the clinical applications. For medical diagnosis, the edges and outlines of the interested objects is more important than other information. Therefore, how to preserve the edge-like features is worthy of investigating for medical image fusion. As we know, the image with higher contrast contains more edge-like features. In terms of this view, this paper proposed a new medical image fusion scheme based on Non Subsampled Contourlet Transform (NSCT) and pixel level fusion rule, which is useful to provide more details about edges at curves. It is used to improve the edge information of fused image by reducing the distortion. This transformation will decompose the image into finer and coarser details and finest details will be decomposed into different resolution in different orientation. The pixel level fusion rule will be applied and low frequency and high frequency coefficients are selected, in these fusion rule we are following Gabor filter bank and Gradient based fusion algorithm. The fused contourlet coefficients are reconstructed by Inverse Non Subsampled Contourlet Transformation (NSCT).The goal of image fusion is to obtain useful complementary information from CT/MRI multimodality images. By this method we can get more complementary information and also Better correlation coefficient, PSNR (Peak-Signal-to-Noise Ratio) and less MSE (Mean square error). The fused image structure segmentation and its analysis will be performed using spatial fuzzy clustering algorithm. This method is proposed to segment normal tissues and abnormal tissues from fused images automatically. Finally results will be presented as segmented tissues with parameter evaluation to show algorithm efficiency.