An Analysis on Mammograms using KDA in Multi Transform Domain
B Prathibha · International Journal of Computer Applications · 2013
Developing a robust Computer Aided Diagnosis (CADx) system for mammograms analysis has been the challenging task for years.The slight difference in X-ray attenuation between normal and abnormal glandular tissues makes the mammogram diagnosis complicated.The proposed CAD system discriminates the abnormal severity of mammograms into normal-benign (non-cancerous, non-spreadable), normalmalign (cancerous) ans benign-,malign, using wavelet features combined with spectral domain.Classification is performed on 206 different mammogram images from Mias database using Kernel Discriminant Analysis (KDA).KDA affords a non-parametric statistical approach with parzen window density estimation to estimate density function from a given sample dataset.The study reveals that the optimal smoothing parameters are increasing functions of the sample size of the complementary classes, features used to classify and value of the bandwidth.