Medical image enhancement using Adaptive Multiscale Product Thresholding

Medical images are generally suffered from signal dependent noises i.e. speckle noise and broken edges. Most of the noises signals appear from machine and environment generally not contribute to the tissue differentiation. But, the noise generated due to above mentioned reason causes a grainy appearance on the image, hence image enhancement is required. This paper proposed a method based on image de-noising and edge enhancement of noisy multidimensional imaging data sets. For the intent of image denoising, Adaptive Multiscale Product Thresholding based on 2-D wavelet transform is used. In this method, contiguous wavelet sub bands are multiplied to improve edge structure while reducing noise. In multiscale products, boundaries can be successfully distinguished from noise. Adaptive threshold is designed and forced on multiscale products as an alternative of wavelet coefficients or recognize important features. For the edge enhancement, Canny Edge Detection Algorithm is used with scale multiplication technique. The proposed algorithm is implemented using MATLAB. Simulation results for image enhancement were presented both for standard test images and CT scan image. Simulation results shows that the planned technique better suppress the Poisson noise among several noises i.e. salt & pepper, speckle noise and random noise. From the qualitative analysis it is also evident that algorithm preserve edges while enhancement.