[Bilello, M., Gokturk, S. B., Desser, T., Napel, S., Jeffrey, R. B., & Beaulieu, C. F. (2004). Automatic detection and classification of hypodense hepatic lesions on contrast-enhanced venous-phase CT. Med. Phys., 31(9), 2584-2593. DOI: 10.1118/1.1782674.10.1118/1.1782674]Search in Google Scholar
[Bruno, A., Collorec, R., Bezy-Wendling, J., Reuze, P., & Rolland, Y. (1997). Texture analysis in medical imaging. In C. Roux & J.-L. Coatrieux (Eds.), Contemporary perspectives in three-dimensional biomedical imaging (pp. 133-164). Amsterdam, Netherlands: IOS Press. DOI: 10.3233/978-1-60750-874-8-133. ]Search in Google Scholar
[Chen, C.-C., DaPonte, J., & Fox, M. (1989). Fractal feature analysis and classification in medical imaging. IEEE Trans. Med. Imag., 8(2), 133-142. DOI: 10.1109/42.24861.10.1109/42.24861]Search in Google Scholar
[Chen, E.-L., choo Chung, P., Chen, C.-L., Tsai, H.-M., & Chang, C.-I. (1998). An automatic diagnostic system for CT liver image classification. IEEE Trans.Biomed. Eng., 45(6), 783-794. DOI: 10.1109/10.678613.10.1109/10.678613]Search in Google Scholar
[Chu, A., Sehgal, C., & Greenleaf, J. (1990). Use of gray value distribution of run lengths for texture analysis. Pattern Recog. Lett., 11(6), 415-419. DOI: 10.1016/0167-8655(90)90112-F.10.1016/0167-8655(90)90112-F]Search in Google Scholar
[Duda, D., Kretowski, M., & Bezy-Wendling, J. (2004). Texture-based classification of hepatic primary tumors in multiphase CT. In C. Barillot, D. Haynor, & P. Hellier (Eds.), Medical Image Computing and Computer-Assisted Intervention MICCAI’2004 (Vol. 3217, pp. 1050-1051 Part II). Springer Berlin Heidelberg. DOI: 10.1007/978-3-540-30136-3 133.]Search in Google Scholar
[Duda, D., Kretowski, M., & Bezy-Wendling, J. (2006). Texture characterization for hepatic tumor recognition in multiphase CT. Biocybern. Biomed. Eng., 26(4), 15-24. Retrieved July 31, 2013, from http://www.ibib.waw.pl/bbe/bbefulltext/BBE264015FT.pdf.]Search in Google Scholar
[Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of online learning and an application to boosting. J. Comput. Syst. Sci., 55(1), 119-139. DOI: 10.1006/jcss.1997.1504.10.1006/jcss.1997.1504]Search in Google Scholar
[Galloway, M. M. (1975). Texture analysis using gray level run lengths. Comput. Graph. Image Process., 4(2), 172-179. DOI: 10.1016/S0146-664X(75)80008-6.10.1016/S0146-664X(75)80008-6]Search in Google Scholar
[Gletsos,M.,Mougiakakou, S.,Matsopoulos, G., Nikita, K., Nikita, A., & Kelekis, D. (2003). A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier. IEEE Trans. Inf. Technol. Biomed., 7(3), 153-162. DOI: 10.1109/TITB.2003.813793.10.1109/TITB.2003.813793]Search in Google Scholar
[Gonzalez, R. C., &Woods, R. E. (2002). Digital image processing (2nd ed.). Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc.]Search in Google Scholar
[Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. SIGKDD Explor. Newsl., 11(1), 10-18. DOI: 10.1145/1656274.1656278.10.1145/1656274.1656278]Search in Google Scholar
[Haralick, R., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Trans. Syst. Man Cybern., SMC-3(6), 610-621. DOI: 10.1109/TSMC.1973.4309314.10.1109/TSMC.1973.4309314]Search in Google Scholar
[Horng, M.-H., Sun, Y.-N., & Lin, X.-Z. (1996). Texture feature coding method for classification of liver sonography. In B. Buxton & R. Cipolla (Eds.), Computer Vision - ECCV’96 (Vol. 1064, pp. 209-218 Part I). Springer Berlin Heidelberg. DOI: 10.1007/BFb0015537. 10.1007/BFb0015537]Search in Google Scholar
[Husain, S., & Shigeru, E. (2000). Use of neural networks for feature based recognition of liver region on CT images. In Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop, 11-13 December 2000. (Vol. 2, pp. 831-840). New York, USA: The IEEE, Inc. DOI: 10.1109/NNSP.2000.890163.10.1109/NNSP.2000.890163]Search in Google Scholar
[Jemal, A., Bray, F., Center, M. M., Ferlay, J., Ward, E., & Forman, D. (2011). Global cancer statistics. CA: A Cancer J. Clin., 61(2), 69-90. DOI: 10.3322/ caac.20107.10.3322/caac.20107]Search in Google Scholar
[Lambrou, T., Linney, A. D., & Todd-Pokropek, A. (2006). Wavelet transform analysis and classification of the liver from computed tomography datasets. In Proceedings of the 6th International IEEE EMBS Special Topic Conference. 26-28 October 2006. Retrieved July 31, 2013, from http://medlab.cs.uoi.gr/itab2006/proceedings/medicalimaging/107.pdf.]Search in Google Scholar
[Laws, K. I. (1980). Textured image segmentation. Unpublished doctoral dissertation, University of Southern California, Los Angeles, California, USA.10.21236/ADA083283]Search in Google Scholar
[Mala, K., Sadasivam, V., & Alagappan, S. (2006). Neural network based texture analysis of liver tumor from computed tomography images. Int. J. Biol. Life Sci., 2(1), 33-40. Retrieved July 31, 2013, from http://www.waset.org/journals/ijbls/v2/v2-1-5.pdf.]Search in Google Scholar
[Mougiakakou, S., Valavanis, I., Mouravliansky, N., Nikita, K., & Nikita, K. (2009). Diagnosis: a telematics-enabled system for medical image archiving, management, and diagnosis assistance. IEEE Trans. Instrum. Meas., 58(7), 2113-2120. DOI: 10.1109/TIM.2009.2015538.10.1109/TIM.2009.2015538]Search in Google Scholar
[Quatrehomme, A.,Millet, I., Hoa, D., Subsol, G., & Puech,W. (2013). Assessing the classification of liver focal lesions by using multi-phase computer tomography scans. In H. Greenspan, H. Muller, & T. Syeda-Mahmood (Eds.), Medical Content-Based Retrieval for Clinical Decision Support MCBR-CDS 2012 (Vol. 7723, pp. 80-91). Springer Berlin Heidelberg. DOI: 10.1007/978-3-642-36678-9 8.10.1007/978-3-642-36678-9]Search in Google Scholar
[Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.]Search in Google Scholar
[Smutek, D., Shimizu, A., Tesar, L., Kobatake, H., Nawano, S., & Svacina, S. (2006). Automatic internal medicine diagnostics using statistical imaging methods. In 19th IEEE International Symposium on Computer-Based Medical Systems (CBMS’2006), 22-23 June 2006 (pp. 405-412). Los Alamitos, California, USA: The IEEE Computer Society Press. DOI: 10.1109/CBMS.2006.56.10.1109/CBMS.2006.56]Search in Google Scholar
[Stoitsis, J., Valavanis, I., Mougiakakou, S. G., Golemati, S., Nikita, A., & Nikita, K. S. (2006). Computer aided diagnosis based on medical image processing and artificial intelligence methods. Nucl. Instrum. Methods Phys. Res., Sect. A, 569(2), 591-595. DOI: 10.1016/j.nima.2006.08.134. 10.1016/j.nima.2006.08.134]Search in Google Scholar
[Wang, L., Zhang, Z., Liu, J., Jiang, B., Duan, X., Xie, Q., Hu, D., Li, Z. (2009). Classification of hepatic tissues from CT images based on texture features and multiclass Support Vector Machines. In W. Yu, H. He, & N. Zhang (Eds.), Advances in Neural Networks ISNN 2009 (Vol. 5552, pp. 374-381 Part 2). Springer Berlin Heidelberg. DOI: 10.1007/978-3-642-01510-6 43.10.1007/978-3-642-01510-6]Search in Google Scholar
[Weszka, J. S., Dyer, C. R., & Rosenfeld, A. (1976). A comparative study of texture measures for terrain classification. IEEE Trans. Syst., Man Cybern., SMC-6(4), 269-285. DOI: 10.1109/TSMC.1976.5408777.10.1109/TSMC.1976.5408777]Search in Google Scholar
[Ye, J., Sun, Y., Wang, S., Gu, L., Qian, L., & Xu, J. (2009). Multiphase CT image based hepatic lesion diagnosis by SVM. In 2nd International Conference on Biomedical Engineering and Informatics (BMEI’2009), 17-19 October 2009 (pp. 1-5). New York, USA: The IEEE, Inc. DOI: 10.1109/BMEI.2009.5304774. 10.1109/BMEI.2009.5304774]Search in Google Scholar