Fractal Modeling of Mammograms Based on Mean and Variance for the Detection of Microcalcifications

In this paper the breast background tissues are modeled using deterministic fractal model based on the mean and variance of the image blocks for detecting the presence of microcalcifications in mammograms are presented. Only those image blocks whose variance difference is between 0.01 and 1 are classified according to their mean value and used in the matching block searching process and therefore the time taken to model the mammograms was considerably reduced to about one third the time required to encode in the conventional fractal encoding scheme. The modeled image will be visually close to the original image and if the difference between the original and the modeled image is taken the presence of microcalcifications can be detected. The method was tested by using the mammograms obtained from MIAS database. The average correlation between the original and the modeled mammograms were obtained as 0.9740 and the average mean square error was found to be 5.939. The results show that the true positive rate is 82% with an average of 0.214 negative clusters per image for 28 mammograms were obtained.