Breast ultrasound image classification using fractal analysis

Recently, fractal analyses have been applied successfully for the image compression, texture analysis and texture image segmentation. The fractal dimension could be used to quantify the texture information. Several methods including box-counting, fractal Brownian motion, and iterative function system etc. can be used to estimate fractal dimension. In this study, the differences of gray value of neighboring pixels are used to estimate the fractal dimension of an ultrasound image of breast lesion by using the fractal Brownian motion. Further, a computer-aided diagnosis system based on the fractal analysis is proposed to classify the breast lesions into two classes benign and malignant. In order to improve the classification performances, the ultrasound image are pre-processed by using morphology operations and histogram equalization. Finally, k-means classification method is used to classify benign tumors from malignant ones. Experimental results will exhibit and evaluate the accuracy rate of the proposed method.