The week's pick
Reseach Article
An Analysis on Mammograms using KDA in Multi Transform Domain
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 79 - Number 3 |
| Year of Publication: 2013 |
| Authors: B. N. Prathibha |
10.5120/13722-1511
|
B. N. Prathibha . An Analysis on Mammograms using KDA in Multi Transform Domain. International Journal of Computer Applications. 79, 3 ( October 2013), 29-34. DOI=10.5120/13722-1511
Abstract
Developing a robust Computer Aided Diagnosis (CADx) system for mammograms analysis has been the challenging task for years. The slight difference in X-ray attenuation between normal and abnormal glandular tissues makes the mammogram diagnosis complicated. The proposed CAD system discriminates the abnormal severity of mammograms into normal-benign (non-cancerous, non-spreadable), normal-malign (cancerous) ans benign-,malign, using wavelet features combined with spectral domain. Classification is performed on 206 different mammogram images from Mias database using Kernel Discriminant Analysis (KDA). KDA affords a non-parametric statistical approach with parzen window density estimation to estimate density function from a given sample dataset. The study reveals that the optimal smoothing parameters are increasing functions of the sample size of the complementary classes, features used to classify and value of the bandwidth.
References
- Sheila Timp, Celia Varela, and Nico Karssemeijer, 'Computer-Aided Diagnosis With Temporal Analysis to Improve Radiologists Interpretation of Mammographic Mass Lesions', IEEE transactions on information technology in biomedicine, VOL. 00, NO. 00, 2010
- Mohamed Meselhy Eltoukhy , Ibrahima Faye , Brahim Belhaouari Samir . 'Breast cancer diagnosis in digital mammogram using multiscale curvelet Transform'. Computerized Medical Imaging and Graphics 34 (2010) 269–276
- Essam A Rashed, Ismail A Ismail and Sherif I. Zaki. ' Multiresolution mammogram analysis in multilevel decomposition '. Pattern Recognition Letters, 28, 2007, pp 286–292.
- Hao Jing, Yongyi Yang, Laura M. Yarusso and Robert M. Nishikawa, 'Textural feature comparison between FFDM and Film mammograms', IEEE conference on Biomedical Imaging, 2011.
- Meigui Chen, Qingxiang Wu, Rongtai Cai,Chengmei Ruan and Lijuan Fan, 'Extraction of breast cancer areas in mammography using neural network based on multiple features', Proceedings of third international conference on Artificial Intelligence and computational intelligence, Vol. 3, ISBN: 978-3-642-23895-6, 2011
- Shantanu Banik, Rangaraj M. Rangayyan, and J. E. Leo Desautels, 'Detection of Architectural Distortion in Prior Mammograms', IEEE Transactions on Medical Imaging, Vol. 30,No. 2, pp. 279-294, 2011
- Peter Mc Leod and Brijesh Sharma, "Variable Hidden Neuron Ensemble for Mass Classification in Digital Mammograms", IEEE ComputatIonal Intelligence magazine February 2013, PP. no. 68-76
- Zhimin Huo, Maryellen L. Giger, and Carl J. Vyborny, "Computerized Analysis of Multiple-Mammographic Views: Potential Usefulness of Special View Mammograms in Computer-Aided Diagnosis ", IEEE Transactions on Medical Imaging, VOL. 20, NO. 12, December 2001 pp. no1285-1292
- Arianna Mencattini, Marcello Salmeri, Giulia Rabottino and Simona Salicone, "Metrological Characterization of a CADx System for the Classification of Breast Masses in Mammograms ", IEEE Transactions on Instrumentation and Measurement, VOL. 59, NO. 11, November 2010, pp no. 2792-2799
- http://peipa. essex. ac. uk/ipa/pix/mias/(publicly available mammogram database)
- Ahmed, N. Natarajan, T. ; Rao, K. R. , " Discrete Cosine Transform ", IEEE Transactions on Computers, Vol. C-23 , ,Issue: 1 Jan. 1974 PP no. 90 - 93
- S Mallat. "A theory for multiresolution signal decomposition: The wavelet representation", IEEE Transaction Pattern Analysis Machine Intelligence. Vol 11, no 7, Jul. 1989, pp 674–693.
- Comparative Study of Techniques for Large-Scale Feature Selection. F. J. Ferria, P. Pudilb, M. Hatefc and J. Kittlerc a Dept. Inform atica i Electr onica. Universitat . .
- Marco Di Marzio, Charles C. Taylor. 'Kernel Density Classification and Boosting '. citeseer ,biometrica,vol 91,pp. 226-223
- Kobos, Mateusz, and Jacek Ma?dziuk. "Bandwidth selection in kernel density estimators for multiple-resolution classification. " Artificial Intelligence and Soft Computing. Springer Berlin Heidelberg, 2012.
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