Abstract
Thresholding is one of the most widely used techniques for image segmentation, particularly, for medical image segmentation. The key idea is the selection of an appropriate intensity value to differentiate the background pixels from the object of interest pixels. In this paper, we propose a method for segmentation of the healthy gallbladder from a computed tomography (CT) scan image by integrating rough entropy thresholding technique along with contour and convex hull operations. Our method has been found to perform significantly better than other competing methods.
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Acknowledgements
This work is an output of the research project titled “Radiomics with Machine Learning towards Prediction of Gallbladder Cancer” funded by ICMR. The authors would like to thank R. Mala for her support in data collection at Dr. B. Borooah Cancer Institute, Guwahati. Compliance with ethical standards Conflict of Interest The authors declare that they have no competing interests. Ethics Approval Ethical approval was obtained for this study. Informed Consent Informed consent was taken from the patients involved in this study.
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Jitani, N. et al. (2022). Gallbladder CT Image Segmentation by Integrating Rough Entropy Thresholding with Contours. In: Gandhi, T.K., Konar, D., Sen, B., Sharma, K. (eds) Advanced Computational Paradigms and Hybrid Intelligent Computing . Advances in Intelligent Systems and Computing, vol 1373. Springer, Singapore. https://doi.org/10.1007/978-981-16-4369-9_62
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DOI: https://doi.org/10.1007/978-981-16-4369-9_62
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