Modified Differential Box Counting in Breast Masses for Bioinformatics Applications

Breast cancer is one of the common invasive cancers and stands at second position for death after lung cancer. The present research work is useful in image processing for characterizing shape and gray-scale complexity. The proposed Modified Differential Box Counting (MDBC) extract Fractal features such as Fractal Dimension (FD), Lacunarity, and Succolarity for shape characterization. In traditional DBC method, the unreasonable results obtained when FD is computed for tumour regions with the same roughness of intensity surface but different gray-levels. The problem is overcome by the proposed MDBC method that uses box over counting and under counting that covers the whole image with required scale. In MDBC method, the suitable box size selection and Under Counting Shifting rule computation handles over counting problem. An advantage of the model is that the proposed MDBC work with recently developed methods showed that our method outperforms automatic detection and classification. The extracted features are fed to K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) categorizes the mammograms into normal, benign, and malignant. The method is tested on mini MIAS datasets yields good results with improved accuracy of 93%, whereas the existing FD, GLCM, Texture and Shape feature method has 91% accuracy.