Abstract
Pathological changes in lymph nodes (LN) can be diagnosed using biopsy, which is a time consuming process. Compared to biopsy, sonography is a better material for detecting pathology in the LN. However, there is lack of consistency between different ultrasound systems, which tend to produce images with different properties. To overcome this problem, a method was proposed in this paper to identify and select universal imaging features to standardize the classification of LN for different ultrasound imaging systems. This will help in the diagnosis of various pathological conditions. The support vector machine (SVM), which combines correlation and performance analysis for the selection of proper imaging features, was adopted for this classification system. Experimental results demonstrated that each selected feature set could be used to classify respective pathological conditions in the LN for images acquired from different ultrasound imaging machines.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chang, C.Y., Lai, C.T., Chen, S.J.: Applying the Particle Swarm Optimization and Boltzmann Function for Feature Selection and Classification of Lymph Node in Ultrasound Images. In: 2008 Eighth International Conference on Intelligent Systems Design and Applications, pp. 55–60. IEEE Press, Taiwan (2008)
Chen, X.Y., Zheng, S.J., Tao, T.: Framework for Efficient Letter Selection in Genetic Algorithm Based Data Mining. In: 2008 International Symposium on Distributed Computing and Applications, pp. 334–338. ISTP Press, China (2008)
Chang, C.C., Lin, T.Y.: Linear feature extraction by integrating pairwise and global discriminatory information via sequential forward floating selection and kernel QR factorization with column pivoting. Pattern Recognition 41, 1373–1383 (2008)
Chen, S., Yu, S., Tzeng, J., Chen, Y., Chang, K., Cheng, K., Hsiao, F., Wei, C.: Characterization of the major histopathological components of thyroid nodules using sonographic textural features for clinical diagnosis and management. Ultrasound in Medicine & Biology 35, 201–208 (2009)
Chang, C.Y., Chen, S.J., Tsai, M.F.: Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images. Pattern Recognition 43, 3494–3506 (2010)
Lewandowski, D., Cooke, R., Tebbens, R.: Sample-based estimation of correlation ratio with polynomial approximation. ACM Transactions on Modeling and Computer Simulation 18, 1–17 (2007)
Milko, S., Melvar, E., Samset, E., Kadir, T.: Evaluation of bivariate correlation ratio similarity metric for rigid registration of US/MR images of the liver. International Journal of Computer Assisted Radiology and Surgery 4, 147–155 (2009)
Coelho, S.T., Ynoguti, C.A.: A Histogram Based Method for Multiclass Classification Using SVMs. Advances in Experimental Medicine and Biology 657, 233–242 (2010)
Chang, C.Y., Huang, H.C., Chen, S.J.: Automatic Thyroid Nodule Segmentation and Component Analysis in Ultrasound Images. Biomedical Engineering: Applications, Basis and Communications 22, 81–89 (2010)
Chen, S.J., Cheng, K.S., Chen, Y.T., Dai, Y.C., Sun, Y.N., Chang, K.Y., Yu, S.N.: Quantitative correlation between sonographic textural feature and histopathological components for breast cancer: preliminary results. Clinical Imaging 32, 93–102 (2008)
Sun, X., Chuang, S., Li, J., McKenzie, F.: Automatic diagnosis for prostate cancer using run-length matrix method. Progress in Biomedical Optics and Imaging 7260, 1–8 (2009)
Chang, C.Y., Wu, Y.L., Tsai, Y.S.: Integrating the Validation Incremental Neural Network and Radial-Basis Function Neural Network for Segmenting Prostate in Ultrasound Images. In: 2009 Ninth International Conference on Hybrid Intelligent Systems, pp. 198–203. IEEE Press, China (2009)
Antonini, M., Barlaud, M., Mathieu, P., Daubechies, I.: Image coding using wavelet transform. IEEE Transactions on Image Processing 1, 205–220 (1992)
Chen, D.R., Chang, R.F., Chen, C.J., Ho, M.F., Kuo, S.J., Chen, S.T., Hung, S.J., Moon, W.K.: Classification of breast ultrasound images using fractal feature. Clinical Imaging 29, 235–245 (2005)
LIBSVM: a library for support vector machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm
Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13, 415–425 (2002)
Paste, A.: LOGIQ 700 Expert Series: Conformance Statement for DICOM V3.0. Operating Instructions, UK (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chang, CY., Chang, SH., Chen, SJ. (2010). Feature Analysis and Classification of Lymph Nodes. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6423. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16696-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-16696-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16695-2
Online ISBN: 978-3-642-16696-9
eBook Packages: Computer ScienceComputer Science (R0)