Automatic Red Blood Cell Classification for MICAD Based on PSO-CSVM

Abstract:

The automatic classification of erythrocyte is critical to clinic blood-related disease treatment in Medical Image Computer Aided Diagnosing(MICAD). After 3D height field recovered from the varied shading, the depth map of each point on the surfaces is applied to calculate Gaussian curvature and mean curvature, which are used to produce surface type label image. Accordingly the surface is segmented into different parts through multi-scale bivariate polynomials function fitting. The count of different surface types is used to design a classifier for training and classifing the red blood cell by means of support vector machine and particle swarm optimization. The experimental result shows that this approach is easily to implement and promising.

• Periodical: Applied Mechanics and Materials (Volumes 121-126)
• Pages: 1952-1956
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.121-126.1952
• Online since: October 2011
• Authors: Rui Hu Wang
• Keywords: CSVM, MICAD, Particle Swarm Optimization Algorithm (PSO), Red Blood Cell

References

[1] Emmanuel Prados, Olivier Faugeras, Shape from Shading. Handbook of Mathematical Models in Computer Vision, Springer, (2006).

DOI: 10.1007/0-387-28831-7_23

[2] Ruo Zhang, Shape from Shading: A survey. IEEE transactions on pattern analysis and machine intelligence, 21, 690-706.

DOI: 10.1109/34.784284

[3] Ping-Sing Tsai and Mubarak Shah. Shape From Shading Using Linear Approximation, IVC, Volume 12, No, 8, 487: 498.

DOI: 10.1016/0262-8856(94)90002-7

[4] Paul J. Besl, Ramesh C. Jain. it Surface in Range Image Understanding, Springer-Verlag New York Inc. (1988).

[5] Paul J. Besl, Ramesh C. Jain. Segmentation Through Variable-Order Surface Fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 10, No. 2: 167-192, (1988).

DOI: 10.1109/34.3881

[6] Y. Ma, Xiaoqing Ding. Face Detection based on Cost-sensitive Support Vector Machines. Lecture Notes in Computer Science, Springer-Verlag Berlin, (2002).

DOI: 10.1007/3-540-45665-1_20

[7] James Kennedy and Russel Eberhart. Particle Swarm Optimization. Proceedings of IEEE international Conference on Neural Networks, IEEE Service Center, Piscataway, NJ, (1995).

[8] Russel Eberhart, James Kennedy. A new optimizer using particle swarm theory. The sixth international symposium on micro machine and human science, 1995: 39-43.

DOI: 10.1109/mhs.1995.494215

[9] Yuhui Shi. Particel Swarm Optimization. IEEE Neural Network Society, February (2004).

[10] Yuhui Shi, Russel Eberhart. A modified Particel Swarm Optimizer. Proceedings of the IEEE congress on Evolutionary Computation (CEC 1999: 69-73, Piscataway NJ, (1999).

[11] Li-Juan Tang, Yan-Ping Zhou et. al . Radius Basis Function Network-Based Transform for a Nonlinear Support Vector Machine as Optimized by a Particle Swarm Optimization Algorithm with Application to QSAR Studies[J]. J. Chem. Inf. Model. 2007, 47, 1438-1445.

DOI: 10.1021/ci700047x.s001

[12] Farid Melgani and Yakoub Bazi. Classification of Electrocardiogram Signals with Support Vector Machines and Particle Swarm Optimization[J]. IEEE Transaction on Information Technology in Biomedicine. Vol. 12, No. 4, September (2008).

DOI: 10.1109/titb.2008.923147

[13] U Paquet, AP Englbrecht. Training Support Vector Machines with Particles Swarms. Proceedings of the International Joint Conference on Neural Network, 2003 Vol. 2. 1593-1598.

[14] X.C. Guo and J.H. Yang, et. al. A novel LS-SVMs hyper-parameter selection based on particle swarm optimization[J]. NeuroComputing, 71(2008) 3211-3215.

DOI: 10.1016/j.neucom.2008.04.027