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

Full Text (PDF, 622KB), PP.36-44

Views: 0 Downloads: 0


Deepa Sankar 1,* Tessamma Thomas 2

1. Division of Electronics Engineering, School of Engineering, Cochin University of Science and Technology, Kochi-682022.Kerala.India.

2. Department of Electronics, Cochin University of Science and Technology, Kochi-682022.Kerala.India.

* Corresponding author.


Received: 6 Oct. 2015 / Revised: 20 Nov. 2015 / Accepted: 4 Jan. 2016 / Published: 8 Mar. 2016

Index Terms

Breast cancer, Benign, Malignant, Masses, Microcalcifications, Fractal dimension, fractal features


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).

Cite This Paper

Deepa Sankar, Tessamma Thomas"Classification of Mammograms into Normal, Benign and Malignant based on Fractal Features", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.3, pp.36-44, 2016. DOI: 10.5815/ijigsp.2016.03.05


[1] (Accessed on 15 July 2015)

[2]Cancer Treatment & Survivorship Facts & Figures 2014-2015. /acs/groups/content/@epidemiologysurveilnce/documents/document/acspc-033876.pdf (Accessed on 15 July May 2015)

[3] (Accessed on 15 July 2015)

[4]E. L. Thurfjell, K. A. Lernevall and A. A. Taube, "Benefit of independent double reading in a Population-based mammography screening program", Radiology, 191: 241–244, 1994

[5]Ton M Tonita, Joanne P Hillis and Chong-Ha Lim, "Medical Radiologic Technologist Review: Effects on a population based Breast cancer screening program", Radiology, 211: 529-533, 1999

[6]H. Li, K.J. Liu, and S.C. Lo, "Fractal Modeling and Segmentation for the Enhancement of Microcalcifications in Digital Mammograms", IEEE Trans. on Med Imaging, 16(6):785-798, 1997

[7]Muttarak M, Konmebhol P and Sukhamwang N, "Breast Calcifications: which are malignant?", Singapore Med Journal, 50(9): 907-914, 2009

[8]Tingting Mu, Asoke K. Nandi and Rangaraj M. Rangayyan, "Classification of Breast Masses Using Selected Shape, Edge-sharpness and Texture Features with Linear and Kernel-based Classifiers", J of Digital Imaging, 21( 2):153-169, 2008

[9]William Mark Morrow, Raman Bhalachandra Paranjape, Rangaraj M. Rangayyan and Joseph Edward Leo Desautels, "Region-Based Contrast Enhancement of Mammograms", IEEE Trans on Med Imaging, 11( 3):392-406, 1992

[10]R. Ramani , N.Suthanthira Vanitha , S. Valarmathy "The Pre-Processing Techniques for Breast Cancer Detection in Mammography Images", I.J. Image, Graphics and Signal Processing, 2013, 5, 47-54.

[11]Robin N. Strickland and Hee II Hahn, "Wavelet Transforms for Detecting Microcalcifications in Mammograms", IEEE Trans. on Med. Imaging, 15( 2): 218-229, 1996

[12]Bhagwati Charan Patel , G. R. Sinha ,"Energy and Region based Detection and Segmentation of Breast Cancer Mammographic Images",I.J. Image, Graphics and Signal Processing, 2012, 6, 44-51

[13]V. Oktem and I. Jouny, "Automatic Detection of Malignant Tumors in Mammograms", Proc. of the 26th Annual Int. Conf. of the IEEE EMBS, San Francisco CA,USA, 1770-1773, 2004

[14]Qi Guo, Jiaqing Shao and Virginie F. Ruiz, "Characterization and Classiļ¬cation of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms", Intl J. of Computer Assisted Radiology and Surgery, 4:11–25, 2009.

[15]Mandelbrot B B, "The Fractal Geometry of Nature", Freeman San Francisco CA, 1982

[16]Deepa Sankar and Tessamma Thomas, "A New Fast Fractal Modeling Approach for the Detection of Microcalcifications in Mammograms", J. of Digital Imaging, Springer, New York, 23(5): 538-546, 2010

[17]Nirupam Sarkar and B. B. Chaudhuri, " An Efficient Differential Box-Counting Approach to Compute Fractal Dimension of Image", IEEE Trans. on Systems, Man and Cybernetics, 24(1): 115-120, 1994

[18]Keith C.Clarke, "Computation of the fractal dimension of topographic surfaces using the triangular prism surface area method", Computers & Geosciences, 12(5): 713-722, 1986

[19]S.Peleg, Joseph Naor, Ralph Hartley and David Avnir, "Multiple Resolution Texture Analysis and Classification", IEEE Trans. on Pat. Analy and Mach. Intelligence, 6(4):518-523, 1984

[20]Yu Tao, Ernest C. M. Lam and Yuan Y. Tang, "Extraction of Fractal Feature for Pattern Recognition", Proc of IEEE Int'l Conf. on Pattern Recognition, 2: 527-530,2000

[21]B. B Chaudhuri and Nirupam Sarkar, " Texture Segmentation Using Fractal Dimension", IEEE Trans on Pat. Analy. and Machine Intelligence, 17(1): 72- 77, 1995

[22]Deepa Sankar and Tessamma Thomas, "Fractal Features based on Differential Box Counting Method for the Categorization of Digital Mammograms", Intl J of Computer Information Systems and Industrial Management (IJCISIM), 2: pp.011-019, 2010 ISSN: 2150-7988 

[23]J Suckling et al: The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series 1069 ,375-78,1994

[24]Pisano Etta D , Breast Imaging, IOS Press,1998

[25]Deepa Sankar and Tessamma Thomas, "Fractal Dimension for the Description of Digital Mammograms using Triangular Prism Surface Area Method", Proc. of the AMSE Intl Conf. on Modeling and Simulation (MS 09) Trivandrum, India, pp-197-200, 2009.

[26]Wendy L. Martinez and Angel R. Martinez, "Computational Statistics Handbook with Matlab", 2nd ed. ,Taylor and Francis Group, New York, 2008 

[27]Tom Fawcett, "An Introduction to ROC Analysis, Pattern Recognition Letters", Elsevier, 27:861-674, 2006.

[28]James A Hanely and Barbara J McNeil , "The meaning and Use of the Area Under a receiver Operating Characteristics (ROC) Curve", Radiology, 143:29-36, 1982

[29]Nancy A Obuchowski, "Fundamentals of Clinical research for Radiologists", Amer. Roentgen Ray Soc., 184:364-372, 2005