The week's pick
Random Articles
Reseach Article
Classification of Normal and Abnormal Patterns in Digital Mammograms for Diagnosis of Breast Cancer
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 28 - Number 6 |
| Year of Publication: 2011 |
| Authors: R. Nithya, B. Santhi |
10.5120/3391-4707
|
R. Nithya, B. Santhi . Classification of Normal and Abnormal Patterns in Digital Mammograms for Diagnosis of Breast Cancer. International Journal of Computer Applications. 28, 6 ( August 2011), 21-25. DOI=10.5120/3391-4707
Abstract
In the last few years, computerized tool play important role in detection of breast cancer. This paper proposes a method for breast cancer diagnosis in digital mammograms using GLCM (Grey Level Co-occurrence Matrix) features. In this paper CAD (Computer Aided Diagnosis) system developed using GLCM feature and neural network. Mammography is an efficient tool for early detection of breast cancer. Computerized methods have recently show great tool in providing radiologists with second opinion about breast cancer diagnosis. Five GLCM features for mammogram images are extracted. Mammogram image is classified into normal image and cancer image. The effectiveness of this paper is examined on DDSM (Digital Database for Screening Mammography) database using classification accuracy, sensitivity and specificity. The overall accuracy can be improved by most relevant GLCM features, which is selected by feature selection algorithm.
References
- Brijesh Verma and Ping Zhang.”A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms”, Applied Soft Computing, no.7, pp.513-525, 2007.
- H.S.Sheshadri and A.Kandaswamy,”Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms”, Computerized Medical Imaging and Graphics, no.31, pp.46-48, 2007.
- Ali Keles, Ayturk Keles and Ugur Yavuz,”Expert system based on neuro-fuzzy rules for diagnosis breast cancer”, Experts Systems with Applications, pp.5719-5726, 2011.
- M.Vasantha. DR, V.Subbiahbharathi and R.Dhamodharan,”Medical Image Feature, Extraction, Selection and Classification”, International Journal of Engineering Science and Technology Vol. 2(6), pp. 2071-2076, 2010.
- Mohammed J.Islam, Majid Ahamadi and Maher A.Sid-Ahmed, “An efficient automatic mass classification method in digitized mammograms using artificial neural network”, Vol.1, No.3, 2010.
- T.S.Subashini, V.Ramalingam and S.Palanivel,”Automated assessment of breast tissue density in digital mammograms”, Comuter Vision and Image Understanding”, no.114, pp.33-44, 2010.
- Pasquale Delogu, Maria Evelina Fantacci, Parnian Kasae and Alessandra Retico.”Characterization of mammographic masses using a gradient-based segmentation algorithm and a neural classifier”. Computers in and Biology Medicine, no.37, pp.1479-1491, 2007.
- S.Daniel Madan Raja, A.Shanmugam,”Artificial neural networks based war scene classification using invariant moments and GLCM features: A comparative study”, International Journal of Engineering Science and Technology, Vol.3, No.2, 2011.
- Andrea Baraldi Flavio Parmiggiani,”An investigation of textural characteristics associated with gray level co-occurence matrix statistical parameters”, IEEE transactions on geosciences and remote sensing, Vol.33, No.2, 1995.
- Dr. H.B. Kekre, Sudeep D.Thepade,Tanuja K Sarode and Vashali Suryawanshi,”Image retrieval using texture features extracted from GLCM,LBG and KPE”,International journal of Computer Theory and Engineerin,Vol.2,No.5,2010.
- Ramana Reddy, A.Suresh, M.Radhika Mani and V.Vijaya Kumar, “Classification of textures based on features extracted from preprocessing images on random windows, Vol.9, 2009.
- Dipankar Hazra,”Texture recognition with combined GLCM, wavelet and rotated wavelet features, International Journal of Computer and Electrical Engineering: Vol.3, No.1, 2011.
- Norila Md Yusof, Nor Ashidi Mat Isa and Harsa Amylia Mat Sakim,”Computer –aided detection and diagnosis for microcalcifications in mammogram, International Journal of Computer Science and Network Security”, Vol.7, No.6, 2007.
- S.Saheb Basha,Dr.K.Satya Prasad, “Automatic detection of breast cancer mass in mammograms using morphological operators and fuzzy c-means clustering”, Journal of Theoretical and Information Technology.
- Shekar Singh, Dr.P.R.Gupta, “Breast cancer detection and classification using neural network”, International Journal of Advanced Engineering Sciences and Technologies, Vol No.6, Issue No.1, pp.004-009.
Index Terms
Keywords