Tessamma Thomas

Work place: Department of Electronics, Cochin University of Science and Technology, Kochi-682022.Kerala.India

E-mail: tess@cusat.ac.in


Research Interests: Medical Image Computing, Image Processing, Image Manipulation, Image Compression


Tessamma Thomas is working as Professor in the Department of Electronics, Cochin University of Science and Technology, Kerala India. She has more than thirty years of teaching and research experience. She has more than 100 research papers to her credit. Her areas of interest include Medical Image Processing, Biometrics, Super resolution, and Genomic signal processing.

Author Articles
Classification of Mammograms into Normal, Benign and Malignant based on Fractal Features

By Deepa Sankar Tessamma Thomas

DOI: https://doi.org/10.5815/ijigsp.2016.03.05, Pub. Date: 8 Mar. 2016

Modern life style of women has made them more vulnerable to breast cancer and it is considered as the largest cause of mortality among women. This paper presents a novel method to classify mammograms into normal ones, with benign and malignant microcalcifications, and with malignant and benign tumors using fractal features derived from fractal dimension. Here, three fractal dimension estimation methods such as Differential Box Counting (DBC), Triangular Prism Surface Area (TPSA) and Blanket methods are used for computing the six fractal features utilized for the classification. The new fractal feature f6 obtained using TPSA method is found to be the best with 100% classification accuracy. The average value of f6 is found to be 0.1110, 0.2875, 0.4743, 0.5271 and 0.8558, for normal, benign masses, benign and malignant microcalcifications and malignant masses respectively. The classification performance of the different features was analyzed using the Receiver Operating Characteristics (ROC).

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