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CLC number: R445; TP391

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Received: 2005-06-20

Revision Accepted: 2005-08-08

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Cited: 10

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Journal of Zhejiang University SCIENCE B 2005 Vol.6 No.11 P.1107~1114

10.1631/jzus.2005.B1107


Liver fibrosis identification based on ultrasound images captured under varied imaging protocols


Author(s):  CAO Gui-tao, SHI Peng-fei, HU Bing

Affiliation(s):  Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200030, China; more

Corresponding email(s):   pfshi@sjtu.edu.cn

Key Words:  Liver fibrosis, Texture, Co-occurrence matrix, Fisher classifier, Support vector machine


CAO Gui-tao, SHI Peng-fei, HU Bing. Liver fibrosis identification based on ultrasound images captured under varied imaging protocols[J]. Journal of Zhejiang University Science B, 2005, 6(11): 1107~1114.

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T1 - Liver fibrosis identification based on ultrasound images captured under varied imaging protocols
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DOI - 10.1631/jzus.2005.B1107


Abstract: 
Diagnostic ultrasound is a useful and noninvasive method in clinical medicine. Although due to its qualitative, subjective and experience-based nature, ultrasound image interpretation can be influenced by image conditions such as scanning frequency and machine settings. In this paper, a novel method is proposed to extract the liver features using the joint features of fractal dimension and the entropies of texture edge co-occurrence matrix based on ultrasound images, which is not sensitive to changes in emission frequency and gain. Then, Fisher linear classifier and support vector machine are employed to test a group of 99 in-vivo liver fibrosis images from 18 patients, as well as other 273 liver images from 18 normal human volunteers.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[3] Canny, J.F., 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8:679-698.

[4] Haralick, R.M., Shanmugan, K., Dinstein, I., 1973. Texture feature for image classification. IEEE Transactions on Systems, Man and Cybernetics, 3:610-621.

[5] Kadah, Y.M., Farag, A.A., Zurada, J.M., Badawi, A.M., Youssef, A.M., 1996. Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images. IEEE Transactions on Medical Imaging, 15:466-478.

[6] Mandelbrot, B.B., 1982. Fractal Geometry of Nature. Freman, San Francisco, CA.

[7] Mojsilović, A., Popović, M., Marković, S., Krstić, M., 1998. Characterization of visually similar diffuse diseases from B-scan liver images using nonseparable wavelet transform. IEEE Transactions on Medical Imaging, 17:541-549.

[8] Nadler, M., Smith, E.P., 1993. Pattern Recognition Engineering. Wiley, New York.

[9] Ogawa, K., Fukushima, M., Kubota, K., Hisa, N., 1998. Computer-aided diagnostic system for diffuse liver diseases with ultrasonography by neural network. IEEE Transactions on Nuclear Science, 45:3069-3074.

[10] Oosterveld, B.J., Thijssen, J.M., Hartman, P.C., Rosenbusch, G.J.E., 1993. Detection of diffuse liver disease by quantative echography: dependence on a priori choice of parameters. Ultrasound in Medicine and Biology, 19(1):21-25.

[11] Sarkar, N., Chaudhuri, B.B., 1994. An efficient differential box-counting approach to compute fractal dimension of image. IEEE Transactions on Systems, Man and Cybernetics, 24:115-120.

[12] Wu, C.M., Chen, Y.C., 1993. Multi-threshold dimension vector for texture analysis and its application to liver tissue classification. Pattern Recognition, 26(1):137-144.

[13] Yeh, W.C., Huang, S.W., Li, P.C., 2003. Liver fibrosis grade classification with B-mode ultrasound. Ultrasound in Medicine and Biology, 29:1229-1235.

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