ENHANCING IMAGE CONTRAST OF MAMMOGRAM & EQUALIZATIO OF HISTOGRAMS

An intelligent Image Processing Technique employed in an system can be very helpful for radiologist in detecting and diagnosing micro calcifications’ patterns earlier and faster than typical screening programs. In this paper, we present a system based on fuzzy-C Means clustering and feature extraction techniques using texture based segmentation and genetic algorithm for detecting and diagnosing micro calcifications’ patterns in digital mammograms. We have investigated and analyzed a number of feature extraction techniques and found that a combination of three features, such as entropy, standard deviation, and number of pixels, is the best combination to distinguish a benign micro calcification pattern from one that is malignant. By contrast enhancement and analyzing its corresponding histograms we conclude that theses techniques will surely be an aid to radiologist for diagnosing of breast cancer at an early stage. The results showed that the genetic algorithm described in the present study was able to produce accurate results in the classification of breast cancer data and the classification rule identified was more acceptable and comprehensible.