Indian Journal of Science and Technology

Abstract

Fractal dimension is an important tool for the analysis of medical images. MR images and the mammographic images give a detailed view of both benign and malignant lesions. This work focuses on the extraction of boundary and analysis of its fractal dimensions to classify the benign and malignant lesions. The boundary of the lesion is extracted using fuzzy c-means clustering. The lesion contour is then converted into time series using “freeman’s chain code”. The resultant time series is used for fractal analysis using non-linear technique called Higuchi algorithm. This algorithm generates a single value which discriminates between benign and malignant structures. This classification of lesions is made using k-nearest neighbor pattern classifier. As a means of confirming the result the histopathological images of the tissues were used. The steps involved include selecting the region of interest of the histopatholgical images and analysis of its fractal dimensions to classify the benign and malignant lesion cells. Box-Counting algorithm is applied on the selected region to obtain various fractal features such as coarseness, lacunarity and complexity of the cells. Based on the obtained values classification of lesions is made using k-nearest neighbor pattern classifier. The results proved that these methods serve as an effective tool for diagnosis.
Keywords: Box-Counting, c-means Clustering, Coarseness and Complexity, Fractal Dimension, Freeman’s Chain Code, Higuchi Algorithm, k-NN Classifier, Lacunarity, MATLAB