Automated Detection and Classification of Microcalcification Clusters with Enhanced Preprocessing and Fractal Analysis

This paper addresses the automated detection of microcalcification clusters from mammogram images by enhanced preprocessing operations on digital mammograms for automated extraction of breast tissue from background, removing artefacts occurring during image registration using X-rays, followed by fractal analysis of suspicious regions. Identification of breast of either left or right and realigning them to a standard position forms a primitive step in preprocessing of mammograms. As the next step in the process, pectoral muscles are separated. Suspicious regions of microcalcifications are identified and are subjected to further analysis of classifying it as benign or malignant. Texture features are representative of its malignancy and fractal analysis was carried out on extracted suspicious regions for its texture features. Principal Component Analysis was carried out to extract optimal features. Ten features were found to be an optimal number of reduced texture features without compromising on classification accuracy. Scaled conjugate Gradient Back propagation network was used for classification using reduced texture features obtained from PCA analysis. By varying hidden layer neurons, accuracy of results achieved by proposed methods is analysed and is calculated to reach maximum accuracy with an optimal level of 15 neurons. Accuracy of 96.3% was achieved with 10 fractal features as input to neural network and 15 hidden layer neurons in neural network designed. The design of architecture is finalised with maximised accuracy for labelling microcalcification clusters as benign or malignant.
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