Abstract
Medical image segmentation is an image processing technology prior to performing a variety of medical image processing. Therefore, a variety of methods have been researched for fast and accurate medical image segmentation. Performing segmentation in various organs, you need the accurate judgment of the interest region in medical image. However, the removal of interest region occurs by the lack of information to determine the interest region in a small region. In this paper, we improved segmentation results in a small region in order to improve the segmentation results using volume data with a linear equation. In order to verify the performance of the proposed method, lung region by chest CT images was segment. As a result of experiments, volume data segmentation accuracy rose from 0.978 to 0.981 and from 0.281 to 0.187 with a standard deviation improvement was confirmed.
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Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0023147).
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Chae, SH., Moon, D., Lee, D.G., Pan, S.B. (2013). Improvement of Lung Segmentation Using Volume Data and Linear Equation. In: Park, J.J., Barolli, L., Xhafa, F., Jeong, H.Y. (eds) Information Technology Convergence. Lecture Notes in Electrical Engineering, vol 253. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6996-0_23
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DOI: https://doi.org/10.1007/978-94-007-6996-0_23
Publisher Name: Springer, Dordrecht
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