Detection of Masses in Mammographic Images Using Simpson's Diversity Index in Circular Regions and SVM

Breast cancer is one of the major causes of death among women all over the world. Presently, mammographic analysis is the most used method for early detection of abnormalities. This paper presents a computational methodology to help the specialist with this task. In the first step, the K-Means clustering algorithm and the Template Matching technique are used to detect suspicious regions. Next, the texture of each region is described using the Simpson's Diversity Index, which is used in Ecology to measure the biodiversity of an ecosystem. Finally, the information of texture is used by SVM to classify the suspicious regions into two classes: masses and non-masses. The tests demonstrate that the methodology has 79.12% of accuracy, 77.27% of sensitivity, and 79.66% of specificity.

[1]  Anselmo Cardoso de Paiva,et al.  Classification of Breast Masses in Mammogram Images Using Ripley's K Function and Support Vector Machine , 2007, MLDM.

[2]  Anselmo Cardoso de Paiva,et al.  Classification of Breast Tissues in Mammogram Images Using Ripley's K Function and Support Vector Machine , 2007, ICIAR.

[3]  Nico Karssemeijer,et al.  Temporal Change Analysis for Characterization of Mass Lesions in Mammography , 2007, IEEE Transactions on Medical Imaging.

[4]  Sally J. Gocker.  The Essential Physics of Medical Imaging. by Jerrold T. Bushberg, J. Anthony Seibert, Edwin M. Leidholdt, Jr., and John M. Bonne , 1995 .

[5]  Anil K. Jain,et al.  Texture Analysis , 2018, Handbook of Image Processing and Computer Vision.

[6]  Mohamed S. Kamel,et al.  Image Analysis and Recognition , 2014, Lecture Notes in Computer Science.

[7]  C. H. Chen,et al.  Handbook of Pattern Recognition and Computer Vision , 1993 .

[8]  C. D'Orsi,et al.  Influence of computer-aided detection on performance of screening mammography. , 2007, The New England journal of medicine.

[9]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[10]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .

[11]  Wei Zhong,et al.  An efficient SVM-GA feature selection model for large healthcare databases , 2008, GECCO '08.

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[14]  J. Bushberg.  The Essential Physics of Medical Imaging , 2001 .

[15]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[16]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[17]  Jihoon Yang,et al.  Feature Subset Selection Using a Genetic Algorithm , 1998, IEEE Intell. Syst..

[18]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[19]  Ron Kohavi,et al.  Irrelevant Features and the Subset Selection Problem , 1994, ICML.

[20]  E. H. Simpson.  Measurement of Diversity , 1949, Nature.

[21]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.