An improved decision support system for detection of lesions in mammograms using differential evolution optimized wavelet neural network

Journal of Medical Systems

Volumn 36, Issue 5, 2012, Pages 3223-3232

  Dheeba, J ^a   Tamil Selvi, S ^b  

^a NOORUL ISLAM UNIVERSITY   (India)

^b NATIONAL ENGINEERING COLLEGE   (India)

Author keywords

Breast cancer; Computer aided diagnosis; Differential evolution; Laws texture energy; Mammograms; Wavelet neural network

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; BENIGN TUMOR; BREAST CANCER; BREAST LESION; BREAST TUMOR; COMPUTER ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; DECISION SUPPORT SYSTEM; DIAGNOSTIC ACCURACY; EVOLUTIONARY ALGORITHM; HUMAN; IMAGE ANALYSIS; MAJOR CLINICAL STUDY; MAMMOGRAPHY; PERFORMANCE MEASUREMENT SYSTEM; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; WAVELET ANALYSIS; ALGORITHM; CLASSIFICATION; DIFFERENTIAL DIAGNOSIS; FEMALE; METHODOLOGY; RADIOGRAPHY;

ALGORITHMS; BREAST NEOPLASMS; DECISION SUPPORT TECHNIQUES; DIAGNOSIS, DIFFERENTIAL; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAMMOGRAPHY; NEURAL NETWORKS (COMPUTER); ROC CURVE; WAVELET ANALYSIS;

EID: 84867325919     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-011-9813-z     Document Type: Article

References (27)

1. Dominquez, A. R.; Nandi, A. K.; Toward breast cancer diagnosis based on automated segmentation of masses in mammograms. Pattern Recognition 42(6):1138-1148, 2009.
2. Anna, N.; Ioannis, S.; Spyros, G.; Filippos, N.; Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications. In: IEEE Trans. on Information Technology in Biomedicine 12(6):731-738, 2008.
3. Verma, B.; A Novel Soft Cluster Neural Network for the Classification of Suspicious Areas in Digital Mammograms. Pattern Recognition 42(9):1845-1852, 2009.
4. Cheng, H. D.; Shi, X. J.; Min, R.; Hu, L. M.; Cai, X. P.; Du, H. N.; Approaches for Automated Detection and Classification of Masses in Mammograms. Pattern Recognition 39(4):646-668, 2006.
5. Dheeba, J.; Tamil Selvi, S.; A Swarm Optimized Neural Network System for Classification of Microcalcification in Mammograms. Journal of Medical Systems, 2011. doi: 10.1007/s10916-011-9781-3.
6. Rying, E. A.; Bilbro, G. L.; Jye-Chyi, Lu, Focused Local Learning with Wavelet Neural Networks. IEEE Transactions on Neural Networks 13:304-319, 2002.
7. Guido Te Brake, M.; Karssemeijier, N.; Single and Multiscale Detection of Masses in Digital Mammograms. IEEE Trans. on Medical Imaging 18:628-639, 1999.
8. Ilonen, J.; Kamarainen, J.-K.; Lampinen, J.; Differential Evolution Training Algorithm for Feed-Forward Neural Networks. Neural Processing Letters 17(1):93-105, 2003.
9. Grim, J.; Somol, P.; Haindl, M.; Danes, J.; Computer Aided Evaluation of Screening Mammograms Based on Local Texture Model. IEEE Trans. on Image Processing 18:765-773, 2009.
10. Kai, Hu, Gao, X.; Li, F.; Detection of Suspicious Lesions by Adaptive Threshoding Based on Multiresolution Analysis in Mammograms. IEEE Transactions on Instrumentation and Measurement. 60(2):462-472, 2011.
11. Kopans, D.B. (1989) Breast Imaging. Philadelphia PA: J.B.Lippincoff:81- 95.
12. Laws, K.J. (1979) Texture Energy Measures. Proceeding DARPA Image Understanding Workshop: 47-51.
13. Wei, L.; Yang, Y.; Nishikawa, R. M.; Microcalcification classification Assisted by Content Based Image Retrieval for Breast Cancer Diagnosis. Pattern Recognition. 42(6):1126-1132, 2009.
14. Metz, C. E.; ROC Methodology in Radiologic Imaging. Investigative Radiology. 21:720-733, 1986.
15. Nancy Obuchowski, A.; Receiver Operating Characteristics Curves and their use in Radiology. Radiology 229:3-8, 2003.
16. Naga Mudigonda, R.; Rangaraj Rangayyan, M.; Leo Desautels, J. E.; Gradient and Texture Analysis for the Classification of Mammographic Masses. IEEE Trans. on Medical Imaging 19:1032-1043, 2000.
17. Qinghua, Z.; Albert, B.; Wavelet Networks. IEEE Transactions on Neural Networks 3:889-898, 1992.
18. Rangayyan, R. M.; Ayres, F. J.; Gabor filters and phase portraits for the detection of architectural distortion in mammograms. Medical and biological engineering and computing 44:883-894, 2006.
19. Rianne, H.; Nico, K.; Use of Normal Tissue Context in Computer Aided Detection of masses in Mammograms. IEEE Trans. on Medical Imaging 28:2033-2041, 2009.
20. Storn, R.; Price, K.; Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces. Journal of Global Optimization, Kluwer Academic Publishers 11:341-359, 1997.
21. Nishikawa, R. M.; Giger, M. L.; Doi, K.; Vyborny, C. J.; Schmidt, R. A.; Computer-aided detection of clustered microcalcifications on digital mammograms. Medical and Biological Engineering and Computing 33:174-178, 1995.
22. Nakayama, R.; Uchiyama, Y.; Yamamoto, K.; Watanabe, R.; Namba, K.; Computer Aided Diagnosis Scheme Using a Filter Bank for Detection of Microcalcification Clusters in Mammograms. IEEE Trans. on Biomedical Engg. 53(2):273-283, 2006.
23. Songyang, Yu, and Guan, L.; A CAD System for the Automatic Detection of Clustered Microcalcifications in Digitized Mammogram Films. IEEE Transactions on Medical Imaging. 19:115-126, 2000.
24. Suckling, J.; Parker, (1994) J. The Mammographic Images Analysis Society Digital Mammogram Database. Proc. of 2nd Int. Workshop Digital mammography, U.K:375-378.
25. Sung-Nien, Yu, and Huang, Yu-Kun, Detection of Microcalcifications in Digital Mammograms using Combined Model-based and Statistical Textural Features. Expert Systems with Applications 37(7):5461-5469, 2010.
26. Ted Wang, C.; Nicolas Karayiannis, B.; Detection of Microcalcifications in Digital Mammograms Using Wavelets. IEEE Trans. On Medical Imaging 17:498-509, 1998.
27. Yao, X.; Liu, Y.; A New Evolutionary System for Evolving Artificial Neural Network, IEEE Trans. Neural Network 8:694-713, 1997.