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Automatic Threshold based Liver Lesion Segmentation in Abdominal 2D-CT Images
Asmita A Moghe, Jyoti Singhai, S.C Shrivastava
Pages - 166 - 176     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 5   Issue - 2    |    Publication Date - May / June 2011  Table of Contents
Segmentation, lesion, Thresholding.
Liver lesion segmentation using single threshold in 2D abdominal CT images proves insufficient. The variations in gray level between liver and liver lesion, presence of similar gray levels in adjoining liver regions and type of lesion may vary from person to person. Thus, with threshold based segmentation, choice of appropriate thresholds for each case becomes a crucial task. An automatic threshold based liver lesion segmentation method for 2D abdominal CT pre contrast and post contrast image is proposed in this paper. The two thresholds, Lower Threshold and Higher Threshold are determined from statistical moments and texture measures. In pre contrast images, gray level difference in liver and liver lesion is very feeble as compared to post contrast images, which makes segmentation of lesion difficult. Proposed method is able to determine the accurate lesion boundaries in pre-contrast images also. It is able to segment lesions of various types and sizes in both pre contrast and post contrast images and also improves radiological analysis and diagnosis. Algorithm is tested on various cases and four peculiar cases are discussed in detail to evaluate the performance of algorithm.
CITED BY (8)  
1 Thakur, R., & Mittal, D. (2015). Segmentation of Liver from Abdomen CT images and 3D Visualization. Journal of Biomedical Engineering and Medical Imaging, 2(5), 46.
2 Mohamed, A. S. E. D., Salem, M. A., Hegazy, D., & Shedeed, H. A. (2015, December). Probablistic-based framework for medical CT images segmentation. In 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS) (pp. 149-155). IEEE.
3 Gao, Y., & Li, J. (2015, August). Defect detection algorithm based on gradient and multithreshold optimization. In Information and Automation, 2015 IEEE International Conference on (pp. 1393-1396). IEEE.
4 Wu, T. Y., Lin, S. F., Wang, P. C., & Wang, Y. C. (2013). A Method for Automatic Extraction of Parotid Lesions in CT Images with Feature-Based Segmentation and Active Contour.
5 Moghe, A. A., & Singhai, J. (2013). Image Registration: A review of elastic registration methods applied to medical imaging. International Journal of Computer Applications, 70(7).
6 Wu, T. Y., & Lin, S. F. (2013). A Method for Extracting Suspected Parotid Lesions in CT Images using Feature-based Segmentation and Active Contours based on Stationary Wavelet Transform. Measurement Science Review, 13(5), 237-247.
7 Kumar, S. S., Moni, R. S., & Rajeesh, J. (2013). Automatic Segmentation of Liver Tumour Using a Possibilistic Alternative Fuzzy C-Means Clustering. International Journal of Computers and Applications, 35(1), 6-12.
8 Bhosale, S., Aphale, A., Macwan, I., Faezipour, M., Bhosale, P., & Patra, P. (2012, August). Computer assisted detection of liver neoplasm (CADLN). In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE (pp. 1510-1513). IEEE.
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Associate Professor Asmita A Moghe
UIT, RGPV , Bhopal - India
Dr. Jyoti Singhai
MANIT Bhopal - India
Dr. S.C Shrivastava
MANIT Bhopal - India

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