A New Approach for Mammogram Image Classification Using Fractal Properties

Don, S.; Chung, Duckwon; Revathy, K.; Choi, Eunmi; Min, Dugki

doi:10.2478/cait-2012-0013

Abstract

Accurate classification of images is essential for the analysis of mammograms in computer aided diagnosis of breast cancer. We propose a new approach to classify mammogram images based on fractal features. Given a mammogram image, we first eliminate all the artifacts and extract the salient features such as Fractal Dimension (FD) and Fractal Signature (FS). These features provide good descriptive values of the region. Second, a trainable multilayer feed forward neural network has been designed for the classification purposes and we compared the classification test results with K-Means. The result reveals that the proposed approach can classify with a good performance rate of 98%.

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A New Approach for Mammogram Image Classification Using Fractal Properties

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