Asian Journal of Medical Radiological Research Original Article Contrast Improvement of Chest Organs in Computed Tomography Images using Image Processing Technique Yousif Mohamed Yousif Abdallah 1* ,Magdolin Siddig 2 1 College of Medical Radiological Science, Sudan University of Science and Technology 2 Medical Radiology Department, National College For medical and Technical studies 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. Key Words: Obstructive jaundice, management, mortality, morbidity. INTRODUCTION Image enhancement techniques are used to refine a given image, so that desired image features become easier to perceive for the human visual system or more likely to be detected by automated image analysis systems. Image enhancement allows the observer to see details in images that may not be immediately observable in the original image. This may be the case, for example, when the dynamic range of the data and that of the display are not commensurate, when the image has a high level of noise or when contrast is insufficient. [1,2] Fundamentally, image enhancement is the transformation or mapping of one image to another. This transformation is not necessarily one –to- one, so that two different input images may transform in to the same or similar output images and medical images as illustrated in the figures after enhancement. [3] More commonly, one may want to generate multiple enhanced versions of a given image this aspect also means that enhancement techniques may be irreversible. Often the enhancement of certain features in images is accompanied by undesirable effects. Valuable image information may be lost or the enhanced image may be a poor representation of the original. Further more enhancement algorithms cannot be expected to provide information that is not present in the original image. If the image does not contain the feature to be enhanced, noise or other unwanted image components may be inadvertently enhanced with out any benefit to the user. Pixel based enhancement techniques are transformations applied to each pixel with out utilizing specifically the information in the neighborhood of the pixel. Enhancement that can de achieved with multiple images of the same scene. [4] A digital image is defined as a tow- dimensional array of numbers that represents the real, continuous Address for correspondence* Yousif Mohamed Yousif Abdallah College of Medical Radiological Science, Sudan University of Science and Technology Email: yousifmohamed@sustech.edu spatial signal is sampled at regular intervals and the intensity is quantized to a finite number of the array is referred to as a picture element or pixel. The digital image is defined as a spatially distributed intensity signal f(m ,n),where f is the intensity of the pixel, and m and define the position of the pixel, along a pair of orthogonal axes usually defined as horizontal and vertical. We shall assume that the image has M rows and N columns and that the digital image has P quantized levels of intensity (gray levels) with values ranging from 0 to P- 1. The histogram of an image, commonly used in image enhancement and image characterization, is defined as a vector that contains the count of the number of pixels in the image at each gray level. A useful image enhancement operation is convolution using local operators, also known as Kernels. Considering a Kernel w(k, l) to be an array of (2k+1+2+1) coefficients where the point(k, l) = (0,0) is the center of the Kernel, convolution of the image with the Kernel is defined by: G(m, n) = w(k, l) * f(m, n) =Type equation here. Where g(m, n) is the out come of the convolution or out put image. To convolute an image with a kernel, the kernel is centered on an image pixel (m, n), the point-by-point products of the kernel coefficients and corresponding image pixels are obtained, and the subsequent summation of these products is used as the pixel value of the out put image g(m, n) is obtained by operating the same operation on an pixels of original image. A convolution kernel can be applied to an image in order to effect of specific enhancement operation or change in the image characteristics. This typically results in desirable attributes being amplified and undesirable attributes being suppressed. The specific values of the kernel coefficients depend on the different types of enhancement that may be desired. Attention is needed at the boundaries of the image where parts of the kernel extend beyond the input image. One approach is to simply use the portion of the kernel that overlaps the input image. This approach can, however, lead to artifacts in the boundaries of the out put image. [5] The forward or inverse Fourier transform of an N×N image, computed directly with the preceding definitions, requires a 6