Malignancy and Abnormality Detection of Mammograms using
Classifier Ensembling
Keywords: Breast Cancer, Mammogram, Support Vector Machine, Discrete
Wavelet Transforms, Ensemble Classifier.
Mehran University Research Journal of Engineering & Technology
Volume 30 , Issue 3
Nawazish Naveed , Muhammad Arfan Jaffar , Faisal Karim Shaikh ,
References
1. |
“Estimated New Cancer Cases and Deaths for 2008â€,
http://seer.cancer.gov/statistics/, 2009. |
2. |
Broeders, M.J., and Verbeek, A.L., “Breast Cancer
Epidemiology and Risk Factorsâ€, Journal of Nuclear
Medical, Volume 41, pp. 179-188, 1997. |
3. |
Wallis, M., Walsh, M., and Lee, J., “A Review of False
Negative Mammography in Asymptomatic Populationâ€,
Clinical Radiology, Volume 44, pp. 13.15, 1991. |
4. |
Cheng, H., et. al., “Approaches for Automated Detection
and Classification of Masses in Mammogramsâ€, Pattern
Recognition, Volume 39, pp. 646.668, 2006. |
5. |
Jaffar, M.A., et. al., “Multi Domain Features Based
Classification of Mammogram Images Using SVM and
MLPâ€, ICIC Express Letters, Volume 4, 2010. |
6. |
“Fuzzy Entropy and Morphology Based Fully Automated
Segmentation of Lungs from CT Scan Imagesâ€,
International Journal of Innovative Computing,
Information and Control, Volume 5, pp. 4993.5002,
2009. |
7. |
Eltonsy, N., Tourassi, G., and Elmaghraby, A., “A
Concentric Morphology Model for the Detection of
Masses in Mammographyâ€, IEEE Transactions on
Medical Imaging, Volume 26, pp. 880.889, 2007. |
8. |
“Investigating Performance of a Morphology-Based
CAD Scheme in Detecting Architectural Distortion in
Screening Mammogramsâ€, Proceedings of International
Congr. Exhibtion Comput. Assist. Radiol. Surg., 2006. |
9. |
Guo, Q., Shao, J., and Ruiz, V., “Investigation of Support
Vector Machine for the Detection of Architectural
Distortion in Mammographic Imagesâ€, International
Journal Physics Conference Series, Volume 15,
pp. 88-94, 2005. |
10. |
Matsubara, T. et al., “Detection Method for
Architectural Distortion Based on Analysis of Structure
of Mammary Gland on Mammogramsâ€, International
Congress Exhibition Compututer Assist. Radiol. Surg.,
Volume 1281, pp. 1036.1040, 2005. |
11. |
Kom, G., Tiedeu, A., and Kom, M., “Automated
Detection of Masses in Mammograms by Local Adaptive
Thresholdingâ€, Computer Biologyy Medical, Volume 37,
pp. 37.48, 2007. |
12. |
Miller, P., and Astley, S., “Automated Detection of Breast
Asymmetry Using Anatomical Featuresâ€, Machine
Perception and Artificial Intelligence, Volume 9,
pp. 247.261, 1994. |
13. |
Campanini, R., et al., “A Novel Featureless Approach to
Mass Detection in Digital Mammograms Based on
Support Vector Machinesâ€, Physics in Medicine and
Biology, Volume 49, pp. 961.975, 2004. |
14. |
Hussain, A. et. al., “Detail Preserving Fuzzy Filter for
Impulse Noise Removalâ€, International Journal of
Innovative Computing, Information and Control,
Volume 5, 2009. |
15. |
Rahman, Z., Woodell, G.A., and Jobson, D.J., “Retinex
Image Enhancement: Application to Medical Imagesâ€,
NASA Workshop on New Partnerships in Medical
Diagnostic Imaging, Greenbelt, Maryland, July, 2001. |
16. |
Sivandam, S., and Deepa, S.N., “Principles of Soft
Computingâ€, Wiley, 2007. |
17. |
Mitchell, T., “Machine Learningâ€, McGraw-Hill, 1997. |
18. |
Gunn, S.R., “Support Vector Machines for Classification
and Regressionâ€, Technical Report, 2008. |
19. |
Suri, J.S., and Rangayyan, R.M., “Recent Advances in
Breast Imaging, Mammography, and Computer-Aided
Diagnosis of Breast Cancerâ€, SPIE, 2006. |
20. |
Paredes, E.S., “Atlas of Mammographyâ€, Lippincott
Williams & Wilkins, 2007. |
21. |
“ACR Breast Imaging Reporting and Data System, Breast
Imaging Atlasâ€, 2003. |
22. |
Heath, M., et. al., “The Digital Database for Screening
Mammographyâ€, Proceedings of International
Workshop on Digital Mammography, pp. 212-218,
2000. |
23. |
Hsu, C.W., and Lin, C.J., “A Comparison of Methods for
Multiclass Support Vector Machinesâ€, IEEE Tranactions
on Neural Networks, Volume 13, pp. 415.425, 2002. |
24. |
Chen, B., Ma, L., and Hu, J., “An Improved Multi-Label
Classification Method Based on SVM with Delicate
Decision Boundaryâ€, International Journal of Innovative
Computing, Information and Control, Volume 6,
pp. 1605-1614, 2010. |
25. |
Chen, G., et. al., “Multi-Class Support Vector Machine
Active Learning for Music Annotationâ€, International
Journal of Innovative Computing, Information and
Control, Volume 6, pp. 921-930, 2010. |
26. |
“MIAS Datasetâ€, http://skye.icr.ac.uk/miasdb/
miasdb.html. |
|
|
|