Fractal-Based Detection of Microcalcification Clusters in Digital Mammograms
This work addresses the challenge of improving breast cancer screening accuracy for medical professionals by enhancing microcalcification detection in mammograms, representing an incremental improvement over existing methods.
The paper tackles the problem of detecting microcalcification clusters in mammogram images by proposing a novel edge detection method using Fractal Dimension and Hurst coefficient, which replaces the fixed Fudge factor in the Sobel method with an image-dependent Hurst coefficient, resulting in more accurate edge detection compared to the conventional Sobel method.
In this paper, a novel method for edge detection of microcalcification clusters in mammogram images is presented using the concept of Fractal Dimension and Hurst co-efficient that enables to locate the microcalcifications in the mammograms. This technique detects the edges accurately than the ones obtained by the conventional Sobel method. Generally, Sobel method detects the edges of the regions/objects in an image using the Fudge factor that assumes its value as 0.5, by default. In this proposed technique, the Fudge factor is suitably replaced with Hurst Co-efficient, which is computed as the difference of Fractal dimension and the topological dimension of a given input image. These two dimensions are image-dependent, and hence the respective Hurst co-efficient too varies with respect to images. Hence, the image-dependent Hurst co-efficient based Sobel method is proved to produce better results than the Fudge factor based Sobel method. The results of the proposed method substantiate the merit of the proposed technique.