Mahua Bhattacharya1*, Arpita Das2*
1Indian Institute of Information Technology & Management, Morena Link Road, Gwalior-474003, India
2Institute of Radio Physics & Electronics, University of Calcutta, 92, A.P.C. Road, Kolkata-700009, India.
* Corresponding Author : arpita.rpe@caluniv.ac.in
Received : - Accepted : - Published : 21-12-2009
Volume : 1 Issue : 2 Pages : 37 - 46
Int J Bioinformatics Res 1.2 (2009):37-46
DOI : http://dx.doi.org/10.9735/0975-3087.1.2.37-46
Keywords : Breast Cancer Diagnosis system, Breast Masses, Fourier Descriptors, Textural Descriptors, Fuzzy
c-means Clustering, ANFIS model
Conflict of Interest : None declared
An intelligent computer aided diagnosis system can be very helpful for radiologist in detecting and diagnosing breast cancer faster than typical screening program. This study attempted to segment the masses accurately and distinguish malignant from benign masses. The suspicious location of the breast masses are specified by the radiologists and then masses are accurately segmented using fuzzy c-means clustering technique. Fourier descriptors are utilized for the extraction of shape features of mammographic masses. These shape features along with the texture features are fed to the input of the ANFIS classifier for determination of the masses as benign, lobular or malignant. The classification system utilizes a simple Euclidian distance metric to determine the degree of malignancy. The study involves 40 digitized mammograms from MIAS, BIRADS database and has to be found 87% correct classification rate. Keyword: Breast Cancer Diagnosis system, Breast Masses, Fourier Descriptors, Textural Descriptors, Fuzzy c-means Clustering, ANFIS model.
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