Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers

References 

1. Taylor HM, Ros PR. Hepatic imaging: an overview. Radiol Clin North Am. 1998;36:237–245
   • Abstract
   • Full Text
   • Full-Text PDF (1080 KB)
   • CrossRef
2. Kadah YM, Frag AA, Zurada JM, Badawi AM, Youssef A-BM. Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images. IEEE Trans Med Imaging. 1996;15(4):466–478
   • CrossRef
3. Sun YN, Horng MH, Lin XZ, Wang JY. Ultrasonic image analysis for liver diagnosis. IEEE Eng Med Biol. 1996;11/12:93–101
4. Sujana H, Swarnamani S, Suresh S. Application of artificial neural networks for the classification of liver lesions by image texture parameters. Ultrasound Med Biol. 1996;22(9):1177–1181
   • Abstract
   • Full-Text PDF (533 KB)
   • CrossRef
5. Wu Ch-M, Chen Y-Ch, Sheng Hsieh K. Texture features for classification of ultrasonic liver images. IEEE Trans Med Imaging. 1992;11(2):141–151
   • CrossRef
6. Mir AH, Hanmandlu M, Tandon SN. Texture analysis of CT images. IEEE Eng Med Biol. 1995;14(6):781–786
7. Chen EL, Chung P-C, Chen CL, Tsa HM, Chang CI. An automatic diagnostic system for CT liver image classification. IEEE Trans Biomed Eng. 1998;45(6):783–794
   • MEDLINE
   • CrossRef
8. Gletsos M, Mougiakakou SG, Matsopoulos GK, Nikita KS, Nikita A, Kelekis D. A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier. IEEE Trans Inf Technol B. 2003;7(3):153–162
9. Jain AK, Duin RPW, Mao J. Statistical pattern recognition: a review. IEEE Trans Pattern Anal. 2000;22(1):4–37
10. Siedlecki W, Sklansky J. A note on genetic algorithms for large scale feature selection. Pattern Recog Lett. 1989;10:335–347
11. Dhawan AP, Chitre Y, Kaiser-Bonasso Ch, Moskowitz M. Analysis of mammographic microcalcifications using gray-level image structure features. IEEE Trans Med Imaging. 1996;15(3):246–259
    • CrossRef
12. Handels H, Roß Th, Kreusch J, Wolff HH, Pöppl SJ. Feature selection for optimized skin tumor recognition using genetic algorithms. Artif Intell Med. 1999;16:283–297
    • Abstract
    • Full Text
    • Full-Text PDF (651 KB)
    • CrossRef
13. Yamany SM, Khiani KJ, Farag AA. Application of neural networks and genetic algorithms in the classification of endothelial cells. Pattern Recog Lett. 1997;18:1205–1210
14. Roli F, Giacinto G, Vernazza G. Methods for designing multiple classifier systems. In:  Roli F,  Kittler J editor. Proceedings of 2nd international workshop on multiple classifier systems in volume 2096 of lecture notes in computer science. London, UK: Springer Verlag; 2001;p. 78–87
15. Christodoulou CI, Pattichis CS, Pantziaris M, Nicolaides A. Texture-based classification of atherosclerotic carotid plaques. IEEE Trans Med Imaging. 2003;22(7):902–912
    • MEDLINE
    • CrossRef
16. Ho TK, Hull JJ, Srihari SN. Decision combination in multiple classifier systems. IEEE Trans Pattern Anal. 1994;16(1):66–75
17. Sboner A, Eccher C, Blanzieri E, Bauer P, Cristofolini M, Zumiani G, et al. A multiple classifier system for early melanoma diagnosis. Artif Intell Med. 2003;27(1):29–44
    • Abstract
    • Full Text
    • Full-Text PDF (159 KB)
    • CrossRef
18. Jerebko AK, Malley JD, Franaszek M, Summers RM. Multiple neural network classification scheme for detection of colonic polyps in CT colonography data sets. Acad Radiol. 2003;10(2):154–160
    • Abstract
    • Full Text
    • Full-Text PDF (456 KB)
    • CrossRef
19. Cheng S-Ch, Huang Y-M. A novel approach to diagnose diabetes based on the fractal characteristics of retinal images. IEEE Trans Inf Technol Biomed. 2003;7(3):163–170
    • MEDLINE
    • CrossRef
20. Valavanis I, Mougiakakou SG, Nikita KS, Nikita A. Computer aided diagnosis of CT focal liver lesions by an ensemble of neural network and statistical classifiers. In: Proceedings of the international joint conference on neural networks (IJCNN’04), IEEE; 2004. p. 1929–34.
21. Mougiakakou SG, Valavanis I, Nikita KS, Nikita A, Kelekis D. Characterization of CT liver lesions based on texture features and a multiple neural network classification scheme. In: Proceedings of the engineering in medicine and biology conference (EMBC’03), IEEE; 2003. p. 1287–90.
22. Efron B. Bootstrap methods: another look at the jackknife. Ann Stat. 1979;7:1–26
23. Efron B. The jackknife, the bootstrap, and other resampling plans. In: CBMS-NSF regional conference series in applied mathematics, society for industrial and applied mathematics (SIAM). Philadelphia; 1982.
24. Chernick MR, Friis RH. Introductory biostatistics for the health sciences: modern applications including bootstrap. In:  Hoboken NJ editors. Wiley series in probability and statistics. Wiley–Interscience; 2003;
25. Chen D-R, Kuo W-J, Chang R-F, Moon WK, Lee CC. Use of the bootstrap technique with small training sets for computer-aided diagnosis in breast ultrasound. Ultrasound Med Biol. 2002;28(7):897–902
    • Abstract
    • Full Text
    • Full-Text PDF (88 KB)
    • CrossRef
26. Fan X, Wang L. Comparability of jackknife and bootstrap results: an investigation for a case of canonical correlation analysis. J Exp Educ. 1996;64:173–189
27. Haralick RM, Shanmugan K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;3(6):610–622
    • CrossRef
28. Haralick RM, Shaphiro LG. Computer and robot vision. vol. I. Boston, MA, USA: Addison-Wesley Publishing Company; 1992;
29. Weszka JS, Dryer CR, Rosenfeld A. A comparative study of texture measures for terrain classification. IEEE Trans Syst Man Cybern. 1976;SMC-6:269–285
30. Laws KI. Rapid texture identification. In: Image processing for missile guidance, Proceedings of the society of photo-optical instrumentation engineers conference. Bellingham, WA, USA. 1980;238, p. 376–80
31. Mandelbrot BB. Fractal geometry of nature. New York: W.H. Freeman and Co.; 1983;
32. Goldberg D. Genetic algorithms in search, optimization and machine learning. Boston, MA, USA: Addison-Wesley Publishing Company; 1989;
33. Haykin S. Neural networks: a comprehensive foundation. Upper Saddle River, NJ, USA: Prentice-Hall; 1999;
34. Wu Y, Ianakiev Kr, Govindaraju V. Improved k-nearest neighbor classification. Pattern Recog. 2002;35:2311–2318
35. Duin RPW. The combining classifier: to train or not to train?. In:  Kasturi R,  Laurendeau D,  Suen C editor. ICPR16, Proceedings 16th International Conference on Pattern Recognition. 2002;p. 765–770
36. Lam L, Suen Ch. Application of majority voting to pattern recognition: an analysis of its behavior and performance. IEEE Trans Syst Man Cybern A. 1997;27(5):553–568
37. Verikas A, Arunas L, Malmqvist K, Bacauskiene M, Gelzinis A. Soft combination of neural classifiers: a comparative study. Pattern Recog Lett. 1999;20:429–444
38. Džeroski S, Ženko B. Is combining classifiers better than selecting the best one?. Mach Learn. 2004;54:255–273
39. Patel AC, Markey MK. Comparison of three-class classification performance metrics: a case study in breast cancer CAD. In:  Eckstein MP,  Jiang Y editor. Medical imaging 2005, image perception, observer performance, and technology assessment, Proceedings of SPIE 2005. SPIE, Bellingham, WA. 2005;5749, p. 581–89
40. Mougiakakou SG, Valavanis I, Mouravliansky NA, Nikita KS, Nikita A. DIAGNOSIS: a telematics enabled system for medical image archiving, management and diagnosis assistance. In: Proceedings of the 2006 IEEE international workshop on imaging systems and techniques (IEEE-IST); 2006. p. 43–8.