The most common cancer of women is breast cancer which is the leading cause of cancer-related death among women aged 15 to 54. The risk of cancer increases after the age of 40's. Thus earlier detection of breast cancer increases the probability of survival of the patient. For its detection mammography is done, but many of the masses remain either undetected or falsely detected due to poor contrast and noise present in mammographic images. Thus for earlier detection of cancerous masses many enhancement techniques are applied. In this paper various set of performance metrics that measure the quality of the image enhancement of mammographic images in a CAD framework that automatically finds masses using machine learning techniques. These performance metrics quantitatively measures the best suited image enhancement on a per mammogram basis, which improves the quality of ensuing image segmentation much better than using the same enhancement method for all mammograms.

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The use of modified version of PSO had declared the optimum solutions and act as remedy incase of particle stagnation. Traditional PSO lost its identity due to impact and strength of MPSO. Recent literatures show how modified particle swarm had achieved its name and fame over its parental algorithm called as PSO by optimizing. In this paper we exploit its advantage over image enhancement for improving image contents. This makes handy for visualizing the information from enhanced images. Work deal with use of parameterized transformation and objective function for local/global information and entropy/edge information respectively by modified PSO. Quality of Image is controlled by scaling factor and helps us in situation like gamut. Enhancement is wide study and we had attempted to use MPSO for this field. Results clearly visualize the effect of color image enhancement and gray scale level enhancement.