Early Detection and Classification of Breast Cancer

Breast cancer is one of the most common cancers among women. About two out of three invasive breast cancers are found in women with age 55 or older. A Mammogram (low energy X ray of breast) done to detect breast cancer in the early stage when it is not possible feel a lump in the breast. In this paper we have proposed a method to detect microcalcifications and circumscribed masses and also classify them as Benign or malignant. The proposed method consists of three steps: The first step is to find region of interest (ROI). The second step is wavelet and texture feature extraction of ROI. The third step is classification of detected abnormality as benign or malignant using Support vector machine (SVM) classifier. The proposed method was evaluated using Mini Mammographic Image Analysis Society (Mini-MIAS) dataset. The proposed method has achieved 92% accuracy.

[1]  Jatindra Kumar Dash,et al.  Wavelet based features of circular scan lines for mammographic mass classification , 2012, 2012 1st International Conference on Recent Advances in Information Technology (RAIT).

[2]  Nan-Chyuan Tsai,et al.  Computer-aided diagnosis for early-stage breast cancer by using Wavelet Transform , 2011, Comput. Medical Imaging Graph..

[3]  P. Tay,et al.  A novel microcalcification shape metric to classify regions of interests , 2010, 2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI).

[4]  A. Kandaswamy,et al.  A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine , 2012, Comput. Biol. Medicine.

[5]  M. Dakovic,et al.  Basic feature extractions from mammograms , 2012, 2012 Mediterranean Conference on Embedded Computing (MECO).

[6]  Arianna Mencattini,et al.  Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing , 2008, IEEE Transactions on Instrumentation and Measurement.

[7]  Med Salim Bouhlel,et al.  Wavelets investigation for computer aided detection of microcalcification in breast cancer , 2009, 2009 International Conference on Multimedia Computing and Systems.

[8]  Werapon Chiracharit,et al.  Contrast enhancement mammograms using denoising in wavelet coefficients , 2013, The 2013 10th International Joint Conference on Computer Science and Software Engineering (JCSSE).

[9]  Samir Kumar Bandyopadhyay,et al.  Technique for preprocessing of digital mammogram , 2012, Comput. Methods Programs Biomed..

[10]  S. Padmanabhan,et al.  Enhanced accuracy of breast cancer detection in digital mammograms using wavelet analysis , 2012, 2012 International Conference on Machine Vision and Image Processing (MVIP).

[11]  P. Spandana,et al.  Novel image processing techniques for early detection of breast cancer, mat lab and lab view implementation , 2013, 2013 IEEE Point-of-Care Healthcare Technologies (PHT).