Investigation of wave atom transform by using the classification of mammograms

This paper presents an approach for breast cancer diagnosis in digital mammograms using wave atom transform.I examine the wave atom transform to determine which couple (scale, ratio of biggest coefficients) will give the highest classification rate.The system uses two sets of feature matrixes obtained from two different database; MIAS and DDSM database.These are tested with classifiers in changing ratios (10% and 90% of coefficients) for each scale.The classification is performed using two different classifiers; Support Vector Machine and k-Nearest Neighbors. This paper presents an approach for breast cancer diagnosis in digital mammograms using wave atom transform. Wave atom is a recent member of the multi-resolution representation methods. Primarily, the mammogram images are decomposed on the basis of wave atoms, and then a special set of the biggest coefficients from wave atom transform is used as a feature vector. Two different classifiers, support vector machine and k-nearest neighbors, are employed to classify mammograms. The method is tested using two different sets of images provided by MIAS and DDSM database.