The week's pick
Random Articles
Reseach Article
Texture Analysis of Mammographic images
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 5 - Number 6 |
| Year of Publication: 2010 |
| Authors: G.R.Udupi, Bhagyashree.S.M, D.A.Kulkarni |
10.5120/919-1297
|
G.R.Udupi, Bhagyashree.S.M, D.A.Kulkarni . Texture Analysis of Mammographic images. International Journal of Computer Applications. 5, 6 ( August 2010), 12-17. DOI=10.5120/919-1297
Abstract
Breast cancer is the most common type of cancer among women in the world. Mammography is regarded as an effective tool for early detection and diagnosis of breast cancer. Microcalcification is one of the primary signs of breast cancer. There are various image texture analysis techniques for the detection of the microcalcifications. Screen-film mammography is still the standard method used to detect early breast cancer, thus leading to early treatment. Digital mammography has recently been designated as the imaging technology with the greatest potential for improving the diagnosis of breast cancer. In this work a feature-based approach is used for analysis and classification of malignancy. Gray-level texture and Wavelet co-efficient texture methods are used for feature extraction. Probabilistic Neural Network (PNN) is used for classification of images based on extracted features. The performance of classification by PNN based on features by texture method, wavelet method and combined methods are compared. The Receiver Operating Characteristics (ROC) Analysis is used for performance evaluation.
References
- Cristina Juarez L., Volodymyr Ponomaryov, J. Luis Sanchez R “Detection of Microcalcifications in Digital Mammograms Images Using Wavelet Transform” CERMA, vol. 2, pp.58-61, Electronics, Robotics and Automotive Mechanics Conference (CERMA'06), 2006.
- L. Shen, R. M. Rangayyan, and J. E. L. Desautels, “Application of shape analysis to mammographic calcifications,” IEEE Trans. Med. Imag. vol. 13, no. 2, pp. 263–274, Jun. 1994.
- J. A. Baker, P. J. Kornguth, J. Y. Lo, and C. E. Floyd, “Artificial neural network: Improving the quality of breast biopsy recommendations,” Radiology, vol.198, no.1, Jan. 1996.
- S. K. Lee, P. Chung, C. I. Chang, C. S. Lo, T. Lee, G. C. Hsu, and C.W Yang, “Classification of clustered microcalcifications using a shape cognitron neural network”, Neural Network, vol 16, no 1, pp121-132, Jan 2003.
- H. P. Chan, B. Sahiner, K. L. Lam, N. Petrick, M. A. Helvie, M. M. Goodsitt, and D. D. Adler “Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces”, Medical Physics Journal, vol 25, issue 10, 2007
- Haralick, K.Shanmugam, and I.Dinstein,” Texture features for Image Classification”, IEEE Transaction, Syst., Man., Cybern. , vol SMC-3, no 6, pp 610-621, Nov 1973.
- D. F. Specht, “Probabilistic neural networks,” Neural Network, vol. 3, no. 1, pp. 109–118, 1990.
Index Terms
Keywords