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Development of Diagnostic Classifier for Ultrasound Liver Lesion Images
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 52 - Number 18 |
| Year of Publication: 2012 |
| Authors: V. Ulagamuthalvi, D. Sridharan |
10.5120/8300-1681
|
V. Ulagamuthalvi, D. Sridharan . Development of Diagnostic Classifier for Ultrasound Liver Lesion Images. International Journal of Computer Applications. 52, 18 ( August 2012), 12-15. DOI=10.5120/8300-1681
Abstract
Liver cancer is the fifth most common cancer worldwide in men and eighth in women, and is one of the few cancers still on the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal, benign and malignant liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non invasive method.
References
- Asmita, A. Moghe, Jyoti Singhai & S. C Shrivastava(2011) :Automatic Threshold based Liver Lesion Segmentation in Abdominal 2D-CT Images International Journal of Image Processing (IJIP), 5 : 2 : pp166-176.
- Haruka, D. and Teruak, A. (2007): Characterization of spatiotemporal stress distribution during food fracture by image texture analysis Methods:Journal of Food Engineering : 81: pp. 429-436.
- Joaquim Jose Furtado1*(2010) , Zhihua Cai1 & Liu Xiaobo1,: Digital Image Processing: Supervised Classification using Genetic Algorithm in MATLAB Toolbox" Report and Opinion;2(6) .
- Koss JE, Newman FD, Johnson TK, Krich DL. (1999): Abdominal organ segmentation using texture transform and Hopfield neural network. IEEE Trans Med Imaging;18:640
- Kulanthaivel G. and Ravindran G. ( 2003)"Web Based Diagnostic aid for Kidney Lesions By Image Texture Parameters", Biennial Conference of Indian Association for Medical Informatics, Chandigarh,p. 14.
- R C GonZalez, R . E. Woods (2001) - Digital Image Processing, Second Edition. Prentice-Hall.
- A. Materkaand M. Stezeleceki,(1998) "Textural Analysis methods-A Review ", COST BII Report ,Brussels, Technical University of Lonz, Institute of Elecronics ul. Stefanowskiego -18,90-124.
- Kumar ,S. S. , Dr Moni R. S. (2010) : Diagnosis of Liver Tumor from CT Images Using Fast Discrete Curvelet Transform", IJCA Special Issue on "Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications" CASCT, Pp1-6.
- Mancas M. , B. Gosselin And B. Macq,(2005) "Segmentation Using a Region Growing Thresholding" 4th Image processing : algorithms and systems :5: pp. 388-398.
- Miller P, Astley S. (1992): Classification of breast tissue by texture analysis. Image Vision Computer;10:277-282.
- Nobel A. J and Boukerroui D. :Ultrasound Image Segmentation: A survey: IEEE Trans On Medical Imaging :25:. 8:pp. 987-1010.
- Otsu, N. (1979) :A Threshold Selection Method from Gray-Level Histogram: IEEE Trans. Systems Man, and Cybernetics: 9: pp. 62-66,.
- Padma,. A,. Sukenesh. R ( 2011) , :Automatic Classification and Segmentation of Brain Tumor in CT images using optimal Gray Level Run lengthTexture Features", IJACSA) International Journal of Advanced Computer Science and Applications: Vol. 2, No. 10:pp53-59.
- Withey, D. J. and Koles, Z. J. (2007) "Three Generations of Medical Image Segmentation: Methods and Available Software,"International Journal of Bioelectromagnetism :. 9 : 2:pp 67-68.
- Wu CM, Chen YC. (1992):Texture features for classification of ultrasonic liver images. IEEE Trans Med Imaging;11:141-52.
- Xiao, G. Brady, . M Noble J. A. and Zhang, Y. (2002. ):Segmentation of ultrasound B-mode images with intensity in homogeneity correction: IEEE Trans. On Med. Imaging : 21,:1: pp. 48-57.
- Horia Stefaneseu. Radu Badea. Monica Lupsor. Simona Tripon. Teodora pop,(2007)"Telemedicine Network for Ultrasound Screening of HCC" ,Ist International Conf. on Advancements of Medicine and Health Care Through Technology (MediTech2007), pp. 107-110.
- Xie J, Jiang Y, Tsui HT. ( 2005) : Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE Trans Med Imaging:;24:45-57.
- V. Ulagamuthalvi,D. Sridharan,(2012)"Automatic Identification of Ultrasound Liver Cancer Tumor Using Support Vector Machine" Proceeding of International Conf. on Emerging Trends in Computer and Electronics Engg(ICETCEE2012), pp : 41-43.
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