Computer-Aided Diagnosis of Liver Tumors Based on Multi-Image Texture Analysis of Contrast-Enhanced CT. Selection of the Most Appropriate Texture Features

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: 10.1118/1.1782674.10.1118/1.1782674Search 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: 10.1109/42.24861.10.1109/42.24861Search 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: 10.1109/10.678613.10.1109/10.678613Search 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-FSearch 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.1997.1504Search 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)80008-6Search 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.2003.813793Search 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: 10.1145/1656274.1656278.10.1145/1656274.1656278Search 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.1973.4309314Search 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/BFb0015537Search 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.2000.890163Search 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.20107Search 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/ADA083283Search 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.2009.2015538Search 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 8.10.1007/978-3-642-36678-9Search 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.2006.56Search 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.2006.08.134Search 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 43.10.1007/978-3-642-01510-6Search 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.1976.5408777Search 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.2009.5304774Search in Google Scholar