A COMPARATIVE STUDY ON CLASSIFICATION OF MAMMOGRAM IMAGES USING DIFFERENT WAVELET TRANSFORMATIONS

Wavelet transformation is one of the most effective mathematical tools for analyzing mammogram images whichposses’ fuzzy likes texture characteristics. In this paper we carried out a comparative study of performance of discrete wavelettransformation (DWT) and stationary wavelet transformation (SWT) for classifying mammogram images into Normal, Benignand Malignant. In each wavelet transformations, a fractional part of the highest wavelet coefficients is used as features forclassification. Initially we created a class core vector for each risk level using ten percent of images from each set. This actsas the basis of the classification. Then each test image in the dataset is classified into the appropriate risk level by theEuclidean distance between the features of the test image and the class core vectors. Using discrete wavelet transformation,83 % of the images were correctly classified into exact risk level. On the other hand using stationary wavelet transformationobtained only 76% of accuracy. We also made a comparative analysis of other distance measure called Bray Curtis. But theresult obtained in Bray Curtis is not much promising. The study also reveals that the redundant nature of coefficients instationary wavelet transformation is not suitable for identifying tumors in mammograms