Multiple ROI selection based focal liver lesion classification in ultrasound images

Expert Systems with Applications

Volumn 40, Issue 2, 2013, Pages 450-457

  Jeon, Jae Hyun ^a   Choi, Jae Young ^a   Lee, Sihyoung ^a   Ro, Yong Man ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

Focal liver lesion classification; Multiple ROI; ROI selection; Ultrasound images

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION METHODS; CLASSIFICATION PERFORMANCE; COMPARATIVE EXPERIMENTS; DIAGNOSTIC ULTRASOUND; ECHOGENICITY; FOCAL LIVER LESIONS; IMAGING MODALITY; LIVER IMAGES; LIVER IMAGING; MULTIPLE ROI; ROBUST CLASSIFICATION; ROI SELECTION; SELECTION BASED; SELECTION METHODS; SOFT TISSUE; ULTRASOUND IMAGES; ULTRASOUND IMAGING;

MORPHOLOGY; ULTRASONIC IMAGING;

ULTRASONICS;

EID: 84867679038     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.07.053     Document Type: Article

References (24)

1. Abou zaid, A. S.; Fakhr, M. W.; & Mohamed, A. F. A. (2006). Automatic diagnosis of liver diseases from ultrasound images. In The 2006 international conference on computer engineering and systems (pp. 313-319).
2. 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 the 29th annual international conference of the IEEE EMBS (pp. 2134-2137).
3. Bommanna Raja, K.; Madheswaran, M.; & Thyagarajah, K. (2007a). Evaluation of tissue characteristic of kidney for diagnosis and classification using first order statistics and RTS invariants. In International conference on signal processing, communications and networking 2007 (pp.483-487).
4. Bommanna Raja, K.; Madheswaran, M.; & Thyagarajah, K. (2007d). Analysis of ultrasound kidney images using content descriptive multiple features for disorder identification and ann based classification. In 12th international conference on computer theory and applications (pp. 382-388).
5. K. Bommanna Raja, M. Madheswaran, and K. Thyagarajah Quantitative and qualitative evaluation of us kidney images for disorder classification using multi-scale differential features ICGST-BIME Journal 7 1 2007 1 8
6. K. Bommanna Raja, M. Madheswaran, and K. Thyagarajah Ultrasound kidney image analysis for computerized disorder identification and classification using content descriptive power spectral features Journal of Medical Systems 31 5 2007 307 317
7. K. Bommanna Raja, M. Madheswaran, and K. Thyagarajah A hybrid fuzzy-neural system for computer-aided diagnosis of ultrasound kidney images using prominent features Journal of Medical Systems 32 1 2008 65 78
8. K. Bommanna Raja, M. Madheswaran, and K. Thyagarajah Texture pattern analysis of kidney tissues for disorder identification and classification using dominant Gabor wavelet Machine Vision and Application 21 3 2010 287 300
9. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik Gene selection for cancer classification using support vector machines Machine Learning 46 2002 389 422
10. Hofmann, T.; Puzicha, J.; & Jordan, M. I. (1999). Unsupervised learning from dyadic data. Advances in Neural Information Processing Systems (Vol. 11). MIT Press.
11. S.-T. John, and C. Nello An introduction to support vector machines and other kernel-based learning methods 2000 Cambridge University Press Cambridge, UK
12. Karule, P. T.; & Dudul, S. V. (2008). Intelligent diagnosis of liver diseases from ultrasonic liver images: Neural network approach. In The 13th International Conference on Biomedical Engineering (pp. 215-218).
13. Karule, P. T.; & Dudul, S. V. (2009). PCA NN based classifier for liver disease from ultrasonic liver images. In Second international conference on emerging trends in engineering and technology (pp. 76-80).
14. S.H. Kim, J.M. Lee, K.G. Kim, J.H. Kim, J.Y. Lee, and J.K. Han Computer-aided image analysis of focal hepatic lesions in ultrasonography: Preliminary results Abdominal Imaging 34 2009 183 191
15. Lin, H. T.; Lin, C. J.; & Weng, R. C. (2003). A note on Platt's probabilistic outputs for support vector machines. Dept. Comp. Sci.; National Taiwan Univ.; Tech. Rep. .
16. D. Mittal, V. Kumar, S.C. Saxena, N. Khandelwal, and N. Kalra Neural network based focal liver lesion diagnosis using ultrasound images Computerized Medical Imaging and Graphics 35 4 2011 315 323
17. Poonguzhali, S.; & Ravindran, G. (2006). Performance evaluation of feature extraction methods for classifying abnormalities in ultrasound liver images using neural network. In Proceedings of the 28th IEEE EMBS annual international conference (pp. 4791-4794).
18. Poonguzhali, S.; Deepalakshmi, B.; & Ravindran, G. (2007). Optimal feature selection and automatic classification of abnormal masses in ultrasound liver images. In 16th international conference on computer communications and networks (pp. 503-506).
19. S. Poonguzhali, and G. Ravindran Automatic classification of focal lesions in ultrasound liver images using combined texture features Information Technology Journal 7 1 2008 205 209
20. Saul, L.; & Pereira, F. (1997). Aggregate and mixed-order Markov models for statistical language processing. In Proceedings of the 2nd International Conference on Empirical Methods in Natural Language Processing (pp. 81-89).
21. G. Xian An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM Expert Systems with Applications 37 2010 6737 6741
22. H. Yoshida, D.D. Casalino, B. Keserci, A. Coskun, O. Ozturk, and A. Savranlar Wavelet-packet-based texture analysis for differentiation between benign and malignant liver tumours in ultrasound images Physics in Medicine and Biology 48 2003 3735 3753
23. Zhang, X. Y.; Diao, X. F.; Wang, T. F.; & Chen, S. P. (2010). Study on feature extraction for ultrasonic differentiation of liver space-occupying lesions. In The 6th international conference on bioinformatics and biomedical engineering.
24. X. Zhou, and B. Bhanu Feature fusion of side face and gait for video-based human identification Pattern Recognition 41 3 2008 778 795