Detection and Classification of Focal Liver Lesions using Support Vector Machine Classifiers

  • Anju Rani Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, Punjab, India
  • Deepti Mittal Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, Punjab, India
  • Ritambhara . Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, Punjab, India
Keywords: Ultrasound, Focal liver lesions, Feature extraction, Classification, Support vector machine classifier

Abstract

In the present work, two computer aided diagnostic systems are designed to detect and classify focal liver lesions such as Cyst, Hemangioma, Hepatocellular carcinoma and Metastases. The work evaluates clinically acquired ultrasound image database. Database contains 111 liver images comprising 95 images of focal lesions and 16 images of normal liver. Images are enhanced and manually segmented into 800 non-overlapping segmented-regions-of-interest. Afterwards, 208 textual features are extracted from each segmented-regions-of-interest. First diagnostic system is designed with one-against-one multiclass support vector machine classification approach showing 93.1% (512/550) overall accuracy on test dataset. Second system is designed with tree structured approach using four binary support vector machine classifiers showing 86.9% (478/550) overall accuracy on test dataset. Out of these two, one-against-one approach based diagnostic system outperforms the neural network based diagnostic system designed for the same purpose by providing 96.6% classification accuracy for typical cases and 85.3% for atypical cases.

Author Biography

Anju Rani, Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, Punjab, India
Electronics Instrumentation and Control Engineering

References

(1) Herszenyi, L., Tulassay, Z., 2010. Epidemioligy of gastrointestinal and liver tumors. European Review for Medical and Pharmacological Sciences 14, 249-258.

(2) Sujana, H., Swarnamani, S., Suresh, S., 1996. Application of artificial neural networks for the classification of liver lesions by image texture parameters. Ultrasound in Medicine and Biology 22, 1177-1181.

(3) Yoshida, H., Casalino, D.D., Keserci, B., Coskun, A., Ozturk, O., Savranlar, A., 2003. Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumors in ultrasound images. Physics in Medicine and Biology 48, 3735–3753.

(4) Balasubramanian, D., Srinivasan, P., Gurupatham, R., 2007. Automatic classification of focal lesions in ultrasound liver images using principal component analysis and neural networks. In: Proceedings of 29th International Conference IEEE EMBS, Lyon, France, pp. 2134-2137.

(5) Poonguzhali, S., Ravindran, G., 2008. Automatic classification of focal lesions in ultrasound liver images using combined texture features. Information Technology Journal 7, 205-209.

(6) Krishna, M.M.R., Banerjee, S., Chakraborty, C., Chakraborty, C., Ray, A.K., 2010. Statistical analysis of mammographic features and its classification using support vector machine, Expert Systems with Applications 37, 470-478.

(7) Tsantis, S., Cavouras, D., Kalatzis, I., Piliouras, N., Dimitropoulos, N., Nikiforidis, G., 2005. Development of a support vector machine-based image analysis system for assessing the thyroid nodule malignancy risk on ultrasound. Ultrasound in Medicine and Biology 31, 1451-1459.

(8) Cao, G., Shi, P., Hu, B., 2005. Liver fibrosis identification based on ultrasound images. In: Proceedings of 27th IEEE Annual Conference on Engineering in Medicine and Biology, Sanghai, China, pp. 6317-6320.

(9) Zhang, J., Wang, Y., Dong, Y., Wang, Y., 2007. Ultrasonographic feature selection and pattern classification for cervical lymph nodes using support vector machine. Computer Methods and Programs in Biomedicine 88, 75-84.

(10) Krishan, A., Mittal, D., 2015. Detection and Classification of Liver Cancer using CT Images.International Journal on Recent Technologies in Mechanical and Electrical Engineering 2, 93-98.

(11) Mishra, S. K., Mittal., D., sunkaria, R. K., 2015. Designing of Computer Aided Diagnostic System for the Identification of Exudates in Retinal Fundus Image. Journal of Biomedical Engineering and Medical Imaging 2, 29-40.

(12) Mittal, D., Kumar, V., Saxena, S.C., Khandelwal, N., Kalra, N., 2011. Neural network based focal liver lesion diagnosis using ultrasound images. Computerized Medical Imaging and Graphics 35, 315–323.

(13) Jain, A.K., Duin, R.P.W., Mao, J., 2000. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 4–37.

(14) Bottou, L., Cortes, C., Denker, J., Drucker, H., Guyon, I., Jackel, L., LeCun, Y., Muller, U., Sackinger, E., Simard, P., Vapnik, V., 1994. Comparison of classifier methods: A case study in handwriting digit recognition. In: Proceedings of International Conference on Pattern Recognition, pp. 77–87.

(15) Friedman, J., 1996. Another approach to polychotomous classification. Stanford, CA: Department of Statistics and Stanford Linear Accelerator Center, Stanford University, available at http://www-stat.stanford.edu/~jhf/ftp/poly.pdf.

(16) Vapnik, V.N., 1998. Statistical Learning Theory. Wiley, New York.

(17) Allwein, E.L., Schapire, R.E., Singer, Y., 2000. Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research 1, 113–141.

(18) Platt, J.C., Cristianini, N., Shawe-Taylor, J., 2000. Large margin DAGs for multiclass

classification, in: Sollam, S.A., Leen, T.K., Mu¨ller, K.-R. (Eds.), Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, pp. 547–553.

(19) Dietterich, T.G., Bakiri, G., 1995. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286.

(20) Crammer, K., Singer, Y., 2001. On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research 2, 265–292.

(21) Chen, Y., Crawford, M.M., Ghosh, J., 2004. Integrating support vector machines in a hierarchical output space decomposition framework. In: Proceedings of International Geoscience and Remote Sensing Symposium, pp. 949–952.

(22) García-Pedrajas, N., Ortiz-Boyer, D., 2006. Improving multiclass pattern recognition by the combination of two strategies. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1001-1006.

(23) Fei, B., Liu, J., 2006. Binary tree of SVM: A new fast multiclass training and classification algorithm. IEEE Transactions on Neural Network 17, 696–704.

(24) Pujol, O., Radeva, P., Vitià, J, 2006. Discriminant ECOC: A heuristic method for application dependent design of error correcting output codes. IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1007-1012.

(25) Chen, J., Wang, C., Wang, R., 2008. Combining support vector machines with a pairwise decision tree. IEEE Geoscience and Remote Sensing Letters 5, 409–413.

(26) Rifkin, R., Klautau, A., 2004. In defense of one-vs-all classification. Journal of Machine Learning Research 5, 101–141.

(27) Hsu, C., Lin, C., 2002. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Network 13, 415–425.

(28) Chen, J., Wang, C., Wang, R., 2009. Using stacked generalization to combine SVMs in magnitude and shape feature spaces for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 47, 2193-2205.

(29) Mittal, D., Kumar, V., Saxena, S.C., Khandelwal, N., Kalra, N., 2010. Enhancement of the ultrasound images by modified anisotropic diffusion method. Medical & Biological Engineering & Computing 48, 1281–1291.

(30) Golemati, S., Tegos, T.J., Sassano, A., Nikita, K.S., Nicolaides, A.N., 2004. Echogenicity of B-mode sonographic images of the carotid artery. Journal of Ultrasound in Medicine 23, 659–669.

(31) Sonnad, S.S., 2002. Describing data: statistical and graphical methods. Radiology 225, 622–628.

(32) Haralick, R.M., Shanmugam, K., Dinstein, I., 1973. Texture features for image classification. IEEE Transactions on Systems Man and Cybernetics SMC-3, 610-621.

(33) Soh, L.-K., Tsatsoulis, C., 1999. Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Transactions on Geoscience and Remote Sensing 37(2), 780-795.

(34) Clausi, D.A., 2002. An analysis of co-occurrence texture statistics as a function of grey level quantization. Canadian Journal of Remote Sensing 28, 45-62.

(35) Galloway, M.M., 1975. Texture analysis using gray level run lengths. Computer Graphics and Image Processing 4, 172-179.

(36) Tang, X., 1998. Texture information in run-length matrices. IEEE Transactions on Image Processing 7(11), 1602-1609.

(37) Laws, K.I., 1980. Rapid texture identification. SPIE 238, 376-380.

(38) Arivazhagan, S., Ganesan, L., Priyal, S.P., 2006. Texture classification using Gabor wavelets based rotation invariant features. Pattern Recognition Letters 27, 1976-1982.

(39) Manjunath, B.S., Ma, W.Y., 1996. Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 837–842.

Published
2016-03-03
How to Cite
Rani, A., Mittal, D., & ., R. (2016). Detection and Classification of Focal Liver Lesions using Support Vector Machine Classifiers. Journal of Biomedical Engineering and Medical Imaging, 3(1), 21. https://doi.org/10.14738/jbemi.31.1821