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BioMed Research International
Volume 2013 (2013), Article ID 687607, 13 pages
Computer-Assisted System with Multiple Feature Fused Support Vector Machine for Sperm Morphology Diagnosis
1Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
2Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan
3Department of Mathematics, Tunghai University, Taichung 40704, Taiwan
4Huazhong University of Science and Technology, Wuhan 430074, China
Received 15 April 2013; Revised 4 July 2013; Accepted 23 July 2013
Academic Editor: Lei Chen
Copyright © 2013 Kuo-Kun Tseng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recognition, vol. 26, no. 9, pp. 1277–1294, 1993.
- A. Yezzi, L. Zöllei, and T. Kapur, “A variational framework for integrating segmentation and registration through active contours,” Medical Image Analysis, vol. 7, no. 2, pp. 171–185, 2003.
- R. Chrástek, M. Wolf, K. Donath et al., “Automated segmentation of the optic nerve head for diagnosis of glaucoma,” Medical Image Analysis, vol. 9, no. 4, pp. 297–314, 2005.
- H. W. Kang and S. Y. Shin, “Enhanced lane: interactive image segmentation by incremental path map construction,” Graphical Models, vol. 64, no. 5, pp. 282–303, 2002.
- J. Peters, O. Ecabert, C. Meyer, R. Kneser, and J. Weese, “Optimizing boundary detection via Simulated Search with applications to multi-modal heart segmentation,” Medical Image Analysis, vol. 14, no. 1, pp. 70–84, 2010.
- G. Behiels, F. Maes, D. Vandermeulen, and P. Suetens, “Evaluation of image features and search strategies for segmentation of bone structures in radiographs using Active Shape Models,” Medical Image Analysis, vol. 6, no. 1, pp. 47–62, 2002.
- M. Blanchard, K. Haguenoer, A. Apert et al., “Sperm morphology assessment using David's classiffication: time to switch to strict criteria? Prospective comparative analysis in a selected IVF population,” International Journal of Andrology, vol. 34, no. 2, pp. 145–152, 2010.
- M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” International Journal of Computer Vision, vol. 1, no. 4, pp. 321–331, 1988.
- T. McInerney and D. Terzopoulos, “T-snakes: topology adaptive snakes,” Medical Image Analysis, vol. 4, no. 2, pp. 73–91, 2000.
- J. Liang, T. McInerney, and D. Terzopoulos, “United Snakes,” Medical Image Analysis, vol. 10, no. 2, pp. 215–233, 2006.
- M. Zhao, Z. Meng, K. K. Tseng, J. S. Pan, and C. Y. Hsu, “Symmetry auto-detection based on contour and corner models,” in Proceedings of the 5th International Conference on Genetic and Evolutionary Computing (ICGEC '11), pp. 345–349, September 2011.
- Y. Mingqiang, K. Kidiyo, and R. Joseph, “A survey of shape feature extraction techniques,” Pattern Recognition, vol. 15, no. 7, pp. 43–90, 2008.
- Y. Wang and H. Huang, “Analysis of Human Heartbeat with EKG Signals,” 2010.
- Q. Hua, A. Ji, and Q. He, “Multiple real-valued K nearest neighbor classifiers system by feature grouping,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC '10), pp. 3922–3925, October 2010.
- P. Viswanath and T. Hitendra Sarma, “An improvement to k-nearest neighbor classifier,” in Proceedings of the IEEE Recent Advances in Intelligent Computational Systems (RAICS '11), pp. 227–231, September 2011.
- P. Wu and Q. Chen, “A novel SVM-based edge detection method,” Physics Procedia, vol. 24, pp. 2075–2082, 2012.
- D. N. Sotiropoulos and G. A. Tsihrintzis, “Artificial immune system-based classification in class-imbalanced image problems,” in Proceedings of the 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 138–141, July 2012.
- M. Ramón, F. Martínez-Pastor, O. García-Álvarez et al., “Taking advantage of the use of supervised learning methods for characterization of sperm population structure related with freezability in the Iberian red deer,” Theriogenology, vol. 77, no. 8, pp. 1661–1672, 2012.
- S. G. Goodson, Z. Zhang, J. K. Tsuruta, W. Wang, and D. A. O'Brien, “Classification of mouse sperm motility patterns using an automated multiclass support vector machines model,” Biology of Reproduction, vol. 84, no. 6, pp. 1207–1215, 2011.
- D. T. Lin and D. C. Pan, “Integrating a mixed-feature model and multiclass support vector machine for facial expression recognition,” Integrated Computer-Aided Engineering, vol. 16, no. 1, pp. 61–74, 2009.
- C. G. Cheng, Y. M. Tian, and W. Y. Jin, “A study on the early detection of colon cancer using the methods of wavelet feature extraction and SVM classifications of FTIR,” Spectroscopy, vol. 22, no. 5, pp. 397–404, 2008.
- D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.
- V. R. Nafisi, M. H. Moradi, and M. H. Nasr-Esfahani, “Sperm identification using elliptic model and tail detection,” World Academy of Science, Engineering and Technology, vol. 6, pp. 205–208, 2005.
- W. J. Yi, K. S. Park, and J. S. Paick, “Parameterized characterization of elliptic sperm heads using Fourier representation and wavelet transform,” in Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 20, pp. 974–977, 1998.
- C. C. Chang and C. J. Lin, LIBSVM—a library for support vector machines, Version 3.12, http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
- C. W. Hsu, C. C. Chang, and C. J. Lin, “A practical guide to support vector classification,” 2010, http://www.csie.ntu.edu.tw/~cjlin.
- A. Vedaldi and B. Fulkerson, “VLFeat: an open and portable library of computer vision algorithms,” 2008, http://www.vlfeat.org/.