Automatic Enhancement of Mammography Images using Contrast Algorithm

Mammography imaging is an incontestable vital tool for diagnosis, it provides in non-invasive manner the internal structure of the body to detect eventually diseases or abnormalities tissues. Unfortunately, the presence of speckle noise in these images affects edges and fine details which limit the contrast resolution and make diagnostic more difficult. The main objective of this study was to enhance of mammography images using Filtering Technique in order to evaluate contrast enhancement pattern in different breast images such as grey color and to evaluate the usage of new nonlinear approach for contrast enhancement of soft tissues in breast images. The data analyzed by using MatLab program to enhance the contrast within the soft tissues, the gray levels in both enhanced and unenhanced images and noise variance. The main techniques of enhancement used in this study were F special convolution kernel technique and Deblurring images using Wiener Algorithm. In this thesis, prominent constraints are firstly preservation of image& #039; s overall look; secondly preservation of the diagnostic content in the image and thirdly detection of small low contrast details in diagnostic content of the image. As shown in previously, state of the art methods provide non-convincing results. The new approach is funded on an attempt to interpret the problem from the view of blind source separation (BSS), thus to see the breast image as a simple mixture of (unwanted) background information, diagnostic information and noise.