The week's pick
Reseach Article
Breast Cancer Detection using Local Binary Patterns
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 123 - Number 16 |
| Year of Publication: 2015 |
| Authors: S. Naresh, S. Vani Kumari |
10.5120/ijca2015905726
|
S. Naresh, S. Vani Kumari . Breast Cancer Detection using Local Binary Patterns. International Journal of Computer Applications. 123, 16 ( August 2015), 6-9. DOI=10.5120/ijca2015905726
Abstract
Breast cancer is a leading cause of cancer type for death among women in most of popular countries, breast cancer detection is important and challenging role in worldwide to save women’s life. Due to inexperience to detect cancer, the doctors and radio logistic can miss the abnormality, which leads to death. Mammography is the most used method for breast cancer detection used by the radiologists. In this experiment, the MIAS (Mammogram Image Analysis Society) database is used and the MIAS database consists of normal and abnormal type of 322 mammograms. The pre-processing is most important step to capture quality mammogram image for next study and processing in mammogram analysis. Texture analysis plays important role to identify normal and abnormal types. Texture feature extraction can be done by local binary patterns (LBP) operator and by using LBP we can consider only sign parameters, it may loss the some texture information. The local binary pattern is a rotation invariant approach for the texture analysis. In this experiment famous completed LBP (CLBP) method used for extracting texture features. Completed LBP considering the sign, magnitude and centre gray level values. By using the joint or hybrid distributions combine CLBP_Sign, CLBP_Magnitude and CLBP_Center gray level values.LBP is one type of Completed LBP for texture analysis, advantage of CLBP is rotation invariant. Finally extracted texture features can be trained and classified by using the SVM classifier for identifying the normal and abnormal cancer type.
References
- Eanes Torres Pereira and Sidney Pimentel Eleutério, “Local Binary Patterns Applied to Breast Cancer Classification in Mammographies,” on RITA _ Volume 21 _ Número 2 _ 2014.
- Zhenhua Guo and Lei Zhang, “A Completed Modelling of Local Binary Pattern Operator for Texture Classification,” on IEEE Transactions on Image Processing.
- D.Narain Ponraj and M.Evangelin Jenifer, “A Survey on the Preprocessing Techniques of Mammogram for the Detection of Breast Cancer,” on Journal of Emerging Trends in Computing and Information Sciences VOL. 2, NO. 12, December 2011 ISSN 2079-8407.
- Anupa Maria Sabu, D.Narain Ponraj& Poongodi, “COMPLETED LBP BASED TEXTURE ANALYSIS IN MAMMOGRAM,”, International Journal of Electronics Signals and Systems (IJESS), ISSN: 2231- 5969, Vol-3, Iss-1, 2013
- Jawad Nagi, Sameem Abdul Kareem, “Automated Breast Profile Segmentation for ROI Detection Using Digital Mammograms,” 2010 IEEE EMBS Conference on Biomedical Engineering & Sciences (IECBES 2010), Kuala Lumpur, Malaysia, 30th November - 2nd December 2010.
- Abdelali Elmoufidi, Khalid El Fahssi, Said Jai-Andaloussi, Abderrahim Sekkaki,,” Detection of Regions of Interest in Mammograms by Using Local Binary Pattern and Dynamic K-Means Algorithm,” Image and video processing theory and applications Vol. 1, No. 1, 30 April 2014 ISSN: 2336-099
- J. Anitha and J. Peter. “A wavelet based morphological mass detection and classification in mammograms”. In Machine Vision and Image Processing (MVIP), 2012 International Conference on, pages 25–28, 2012.
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