Cancer detection in mammograms estimating feature weights via Kullback-Leibler measure

Abstract

In this study the aim is to determine cancerous possibility of suspicious lesions in mammograms. With this aim, probabilistic values of suspicious lesions in the image are found via exponential curve fitting and texture features in order to find weight values in the objective function. Afterwards, images are classified as normal, malign, and benign by utilizing Kullback Leibler method. Here, 3×10 mammography images set selected from Digital Database for Screening Mammography (DDSM) are used, and severity of disease is probabilistically estimated. Results are indicated on a scale to eliminate the suspicious lesions. Thus, it is considered that workload of clinicians shall be reduced by easily eliminating suspicious images out of many mammography images.