Abstract
This study focuses on how to enhance the suspicious regions in the mammography and circumscribe the regions of interest (ROI) automatically. We introduce use of histogram equalization & contrast enhancement (HECE) algorithm and Difference of Gaussian (DoG) filter to enhance the suspicious masses and micro-calcifications. The DoG filter and Bezier curve are then used to find the boundary of breast in the mammography, which can provide useful orientation information to doctors during diagnosing the mammograms. Methods using self-organization map (SOM) neural network and morphological techniques are also developed to separate the suspicious circumscribed masses from the mammography.The experimental results had been assessed by Dr. Tiu, the Chairwoman of the Pediatric Radiology Department, Veterans General Hospital, Taipei, to determine the efficacy of the proposed methods. It is concluded by Dr. Tiu that the enhancement is good and can be valuable to doctors during the mammogram diagnosis. A comparison of the suspicious mass circumscription results with that circumscribed manually by Dr. Tiu was also performed. It was found that our algorithms can detect all the masses in the mammography without any loss in our experiments, i.e., the proposed method can not only mark out the regions spotted by doctors but also those suspicious regions which might not be marked by doctors in the first glance, thus reducing the misdiagnosis probability.