Multi Sensor medical image fusion techniques and performance analysis

The fusion of images is the process of combining two or more images in to a single image retaining important features from each. Fusion is an important technique within many different fields, such as remote sensing, robotics and medical applications. The Laplacian pyramid was first introduced as a model for binocular fusion in human stereovision. It is well suited for many image analysis tasks as well as for image compression. Discrete Wavelet based fusion techniques have been reasonably effective in combining perceptually important image features. We made an attempt to analyze these two techniques by applying to medical images ( CT and MRI) and calculating the statistical parameters mean,standerd deviation and, entropy as quantitative measures . From the experimental results it is found that Laplacian pyramid is better than wavelet (DWT) in one case and DWT is better than Laplacian pyramid in another case. I.INTRODUCTION Image fusion has wide applications in medical imaging, remote sensing, night time operation and multispectral imaging, together with image registration techniques more attention have been paid on image fusion especially in medical image processing domain such as computer aided diagnosis with multimodality image, image guided therapy and surgery[1]. Image fusion combines multiple-source complementary imagery in order to enhance the information apparent in the respective source images, as well as to increase the reliability of interpretation and classification. [2] two common methods are the wavelet transform and various pyramids. As with any pyramid method, the wavelet based fusion method is a multistate analysis method. The studies[3] showed that the DWT method have more complete theoretical support as well as further development potential. This paper will discuss DWT and Laplacian pyramid methods and compare their statistical parameters. To measure the image quality, quantitative evaluation of the fused imagery is considered quite important such that performance comparisons of the respective fusion algorithms can be carried out objectively and automatically . A quantitative metric may be potentially be used as feedback to the fusion algorithm to further improve the fused image quality. In this work the quality assessment was done based on their statistical parameters mean, standard deviation and entropy. 2.THE LAPLASIAN PYRAMID Pixel to pixel correlation are first removed by subtracting a lowpass filtered copy of the image from the image itself [4]. The difference or error image has low variance and entropy, the low pass filtered image may be represented at reduced sample density. Iteration of the process at appropriately expanded scales generates a pyramid data structure. Let go(ij) be the original image and g(ij) be the result of applying an appropriate low pass filter to go . The prediction error Lo(ij) is given by Lo(ij)=go(ij)-g1(ij) The reduced image g1 is itself lowpass filtered to yield g2and a second error image is obtained L2(ij)= g1(ij) – g2(ij). By these steps we obtain a two dimensional arrays L0,L1,L2,. Ln. If we now imagine these arrays stacked one above another, the result is a tapering pyramid data structure. The value at each node at the pyramid represents the difference between two Gaussian like or related functions convolved with the original image. The difference between these two functions is similar to the Laplacian operators. The value at each node in the Laplacian pyramid is the difference between the convolution of two equivalent weighting functions with the original image. Again this is similar to convolving an appropriately scaled Laplacian weighting function with the image . The node value can be obtained directly by applying this operator . The fusion can be performed with more than two input images, this paper is constricted to the case of two input images ( CT and MRI) . The pyramid decompositions are performed on each source image, all these decomposition are integrated to form a composite representation and finally reconstructed the fused image by performing an inverse pyramid transform.