Abstract
In real medical applications, proper measurement of cancer disease area is very important, particularly so, if we want to compare the disease region between pre-treatment and post-treatment CT images for the same patient. The segmentation of a specific region can be defined as the grouping of image pixels corresponding to specific features. Several supervised approaches are there to solve the region segmentation problem. In our problem, we want to find the cancer area from the pre-treatment and post-treatment CT images which are in the state of raw medical data. In this study, we are using the SVM to measure the cancer area from the raw images where new features are taken into the study including neighboring pixels’ influence. Finally, our proposed approach is compared with the state-of-the-art methods which subsequently proves that our method performs more efficiently than other concerned procedures.
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Acknowledgements
The authors are grateful to the Medical College and Hospital, Kolkata for providing them with the major raw dataset, along with some valuable feedback. It is also acknowledged that this study is supported by the Ministry of Electronics and Information Technology, Government of India.
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Chakraborty, T., Bhadra, A.K., Nandi, D. (2023). SVM-based Pre- and Post-treatment Cancer Segmentation from Lung and Abdominal CT Images via Neighborhood-Influenced Features. In: Deva Sarma, H.K., Piuri, V., Pujari, A.K. (eds) Machine Learning in Information and Communication Technology . Lecture Notes in Networks and Systems, vol 498. Springer, Singapore. https://doi.org/10.1007/978-981-19-5090-2_3
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DOI: https://doi.org/10.1007/978-981-19-5090-2_3
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