Contrast Improvement of Chest Organs in Computed Tomography Images using Image Processing Technique

Abstract

Image enhancement allows the observer to see details in images that may not be immediately observable in the original image. Image enhancement is the transformation or mapping of one image to another. The enhancement of certain features in images is accompanied by undesirable effects. We proposed that to achieve maximum image quality after denoising, a new, low order, local adaptive Gaussian Scale Mixture model and median Filter were presented, which accomplishes nonlinearities from scattering a new nonlinear approach for contrast enhancement of soft tissues in CT images using both clipped binning and nonlinear binning methods. The usual assumption of a distribution of Gaussian and Poisson statistics only lead to overestimation of the noise variance in regions of low intensity (small photon counts), but to underestimation in regions of high intensity and therefore to non-optional results. The contrast enhancement results were obtained and evaluated using MatLab program in 50 CT images of the chest and abdomen from two CT studies. The optimal number of bins, in particular the number of gray-levels, is chosen automatically using entropy and average distance between the histogram of the original gray-level distribution and the contrast enhancement function's curve.