No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
Data mining framework for fatty liver disease classification in ultrasound: A hybrid feature extraction paradigm
1.S. Sherlock and J. Dooley, Diseases of the Liver and Biliary System (Blackwell Science, Malden, 2002).
2.V. Droga and D. Rubens, Ultrasound Secrets (Hanley and Belfus, Philadelphia, PA, 2004).
4.G. C. Farrell and C. Z. Larter, “Nonalcoholic fatty liver disease: From steatosis to cirrhosis,” Hepatology 43(2), S99–S112 (2006).
6.R. C. Cheung, “Complications of Liver Biopsy. Gastrointestinal Emergencies,” in Gastrointestinal Emergencies, edited by T. C. K. Tham, J. S. A. Collins, and R. Soetikno (Blackwell, West Sussex, UK, 2009), pp. 72–79.
7.V. Ratziu, F. Charlotte, A. Heurtier, S. Gombert, P. Giral, E. Bruckert, A. Grimaldi, F. Capron, T. Poynard, and LIDO Study Group, “Sampling variability of liver biopsy in nonalcoholic fatty liver disease,” Gastroenterology 128(7), 1898–1906 (2005).
8.S. Saadeh, Z. M. Younossi, E. M. Remer, T. Gramlich, J. P. Ong, M. Hurley, K. D. Mullen, J. N. Cooper, and M. J. Sheridan, “The utility of radiological imaging in nonalcoholic fatty liver disease,” Gastroenterology 123(3), 745–750 (2002).
9.S. F. Quinn and B. B. Gosink, “Characteristic sonographic signs of hepatic fatty infiltration,” AJR, Am. J. Roentgenol. 145(4), 753–755 (1985).
10.K. J. Foster, A. H. Griffith, K. Dewbury, C. P. Price, and R. Wright, “Liver disease in patients with diabetes mellitus,” Postgrad. Med. J. 56(661), 767–772 (1980).
11.Y. Yajima, K. Ohta, T. Narui, R. Abe, H. Suzuki, and M. Ohtsuki, “Ultrasonographical diagnosis of fatty liver: Significance of the liver-kidney contrast,” Tohoku. J. Exp. Med. 139(1), 43–50 (1983).
12.S. H. Saverymuttu, A. E. Joseph, and J. D. Maxwell, “Ultrasound scanning in the detection of hepatic fibrosis and steatosis,” Br. Med. J. (Clin. Res Ed) 292(6512), 13–15 (1986).
13.U. L. Mathiesen, L. E. Franzen, H. Aselius, M. Resjö, L. Jacobsson, U. Foberg, A. Frydén, and G. Bodemar, “Increased liver echogenicity at ultrasound examination reflects degree of steatosis but not of fibrosis in asymptomatic patients with mild/moderate abnormalities of liver transaminases,” Dig. Liver Dis. 34(7), 516–522 (2002).
14.M. H. Mendler, P. Bouillet, A. Le Sidaner, E. Lavoine, F. Labrousse, D. Sautereau, and B. Pillegand, “Dual-energy CT in the diagnosis and quantification of fatty liver: Limited clinical value in comparison to ultrasound scan and single-energy CT, with special reference to iron overload,” J. Hepatol. 28(5), 785–794 (1998).
15.S. R. Mehta, E. L. Thomas, J. D. Bell, D. G. Johnston, and S. D. Taylor-Robinson, “Non-invasive means of measuring hepatic fat content,” World J. Gastroenterol. 14(22), 3476–3483 (2008).
17.Y. M. Kadah, A. A. Farag, J. M. Zurada, A. M. Badawi, and A. M. Youssef, “Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images,” IEEE Trans. Med. Imaging 15(4), 466–478 (1996).
18.C. Nikias and A. Petropulu, Higher-Order Spectral Analysis (Prentice-Hall, Englewood Cliffs, NJ, 1997).
20.A. Ramm and A. Katsevich, The Radon Transform and Local Tomography (CRC, 1996).
21.K. C. Chua, V. Chandran, R. Acharya, and C. M. Lim, “Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: A comparative study,” in Conference Proceedings of the IEEE on Engineering in Medicine and Biology Society, Vancouver, British Columbia (IEEE, Vancouver, British Columbia, Canada, 2008), pp. 3824–3827.
23.O. Faust, U. R. Acharya, C. M. Lim, and B. H. C. Sputh, “Automatic identification of epileptic and background EEG signals using frequency domain parameters,” Int. J. Neural Syst. 20(2), 159–176 (2010).
24.K. C. Chua, V. Chandran, U. R. Acharya, and C. M. Lim, “Cardiac state diagnosis using higher order spectra of heart rate variability,” J. Med. Eng. Technol. 32(27), 145–155 (2008).
25.M. Mirmehdi, X. Xie, and J. S. Suri, Handbook of Texture Analysis (Imperial College Press, London, 2009).
27.M. M. Galloway, “Texture analysis using grey level run lengths,” NASA STI/Recon Technical Report No. N 75, 1974.
28.I. M. Kapetanovic, S. Rosenfeld, and G. Izmirlian, “Overview of commonly used bioinformatics methods and their applications,” Ann. N.Y. Acad. Sci. 1020, 10–21 (2004).
29.D. T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining (Wiley Interscience, New Jersey, 2004), pp. 107–126.
30.T. J. Ross, Fuzzy Logic with Engineering Applications (Wiley, West Sussex, 2004).
31.M. Sugeno, Industrial Applications of Fuzzy Control (Elsevier Science, North-Holland, 1985).
33.M. H. Zweig and G. Campbell, “Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine,” Clin. Chem. 39(4), 561–577 (1993).
34.E. Kyriacou, S. Pavlopoulos, G. Konnis, D. Koutsouris, P. Zoumpoulis, and I. Theotokas, “Computer assisted characterization of diffused liver disease using image texture analysis techniques on B-scan images,” in Proceedings of the IEEE Nuclear Science Symposium (IEEE, Albuquerque, NM, 1997), Vol. 2, pp. 1479–1483.
35.E. Kyriacou, S. Pavlopoulos, D. Koutsouris, P. Zoumpoulis, and L. Theotokas, “Computer assisted characterization of liver tissue using image texture analysis techniques on B-scan images,” in Proceedings of the 19th International Conference of IEEE EMBS (IEEE, Chicago, IL, 1997), Vol. 2, pp. 806–809.
36.S. Pavlopoulos, E. Kyriacou, D. Koutsouris, K. Blekas, A. Stafylopatis, and P. Zoumpoulis, “Fuzzy neural network-based texture analysis of ultrasonic images,” IEEE Eng. Med. Biol. Mag. 19(1), 39–47 (2000).
37.A. M. Badawi, A. S. Derbala, and A. M. Youssef, “Fuzzy logic algorithm for quantitative tissue characterization of diffuse liver diseases from ultrasound images,” Int. J. Med. Inf. 55(2), 135–147 (1999).
38.J. Wan and S. Zhou, “Features extraction based on wavelet packet transform for B-mode ultrasound liver images,” in the 3rd International Congress Image Signal Proceedings (CISP) (IEEE, Yantai, 2010), Vol. 2, pp. 949–955.
39.W. L. Lee, Y. C. Chen, and K. S. Hsieh, “Ultrasonic liver tissues classification by fractal feature vector based on M-Band wavelet transform,” IEEE Trans. Med. Imaging 22(3), 382–392 (2003).
40.R. Ribeiro and J. Sanches, “Fatty liver characterization and classification by ultrasound,” in Proceedings of the 4th Iberian Conference on Pattern Recognition And Image Analysis, Lecture Notes in Computer Science Vol. 5524 (Springer-Verlag Berlin, Heidelberg, 2009), pp. 354–361.
42.S. Mukherjee, A. Chakravorty, K. Ghosh, M. Roy, A. Adhikari, and S. Mazumdar, “Corroborating the subjective classification of ultrasound images of normal and fatty human livers by the radiologist through texture analysis and SOM,” in Proceedings of the International Conference on Advanced Computing and Communications (IEEE, Guwahati, Assam, 2007), pp. 197–202.
43.S. G. Mougiakakou, I. K. Valavanis, K. S. Nikita, A. Nikita, and D. Kelekis, “Characterization of CT liver lesions based on texture features and a multiple neural network classification scheme,” in Proceedings of the 25th International Conference of IEEE EMBS (IEEE, Cancun, Mexico, 2003), Vol. 2, pp. 1287–1290.
44.S. G. Mougiakakou, I. K. Valavanis, N. A. Mouravliansky, A. Nikita, and K. S. Nikita, “DIAGNOSIS: A telematics-enabled system for medical image archiving, management, and diagnosis assistance,” IEEE Trans. Instrum. Meas. 58(7), 2113–2120 (2009).
Article metrics loading...
Full text loading...
Most read this month