Intensity-Based Detection of Microcalcification Clusters in Digital Mammograms using Fractal Dimension

Abstract

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.

Wavelet Transformation-Based Detection of Masses in Digital Mammograms

“…The approach of Median filtering is a useful tool to smoothen the image and to reduce the excessive distortions such as thinning or thickening of object boundaries. Median filtering helps to remove the digitization noise and high frequency components from the mammography images [1,2,15,[19][20][21].…”

“…Opening with the structuring element is used to eliminate specific image details smaller than the structuring element. The definitions of these operations are dependent on the image types, such as binary, gray level or color of the image being processed [1,2,15,[19][20][21].…”

“…Interpretation of a mammogram is often difficult for radiologists to find out the suspicious lesion present in it. This may lead to give results with missing true positive masses or sometimes resulting in detection of some false negative masses [5][6][7][13][14][15].…”

Spectral correlation analysis for microcalcification detection in digital mammogram images

“…Others have studied the feasibility of utilizing fractal analysis [5]. Recently, the wavelet analysis has shown its powerful effect in microcalcification detection and preprocessing [6][7][8][9][10].…”

Mammogram Mass Segmentation Using Fractal Oriented Gamma Transformation

“…Fractals are of rough geometric shapes which when subdivided into discrete parts, each possesses reduced similarities of the whole object. Fractal dimension is an imperative characteristic of fractal objects, which is an important parameter that measures the fractal property of an object [12][13][14][15]. Fractal dimension has got wider applications in the fields of image segmentation and shape recognition.…”