Abstract
Medical image segmentation is a relevant and active research field of medical image processing. The proposal of various algorithms not only enriches the means to solve the problem of medical image segmentation but also makes the algorithm classification and summary urgent. At present, a variety of classification methods are mostly based on the characteristics of the algorithm itself. If the classification principle of the algorithm is determined according to the essential elements of the organ plane space, such as point, line, and surface, a new classification method will be formed, and it is more in line with people’s intuitive feelings. Using this new segmentation principle to classify medical image segmentation algorithms is helpful to clarify the relationship between various algorithms.
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References
Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2, 315–337 (2000)
Withey, D.J., Koles, Z.J.: Medical image segmentation: methods and software. In: 2007 NFSI-ICFBI, pp. 140–143 (2007)
Sharma, N., Aggarwal, L.M.: Automated medical image segmentation techniques. J. Med. Phys. 35(1), 3–14 (2010)
Elnakib, A., et al.: Medical image segmentation: a brief survey. In: El-Baz, A., Acharya, U.R., Laine, A., Suri, J. (eds.) Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies, pp. 1–39. Springer, New York (2011)
Erdt, M., Steger, S., Sakas, G.: Regmentation: a new view of image segmentation and registration. J. Radiat. Oncol. Inform. 4(1), 1–23 (2012)
Norouzi, A., et al.: Medical image segmentation methods, algorithms, and applications. IETE Tech. Rev. 31(3), 199–213 (2014)
Zhou, L.Q., et al.: Artificial intelligence in medical imaging of the liver. World J. Gastroenterol. 25(6), 672–682 (2019)
Aganj, I., et al.: Unsupervised medical image segmentation based on the local center of mass. Sci. Rep. 8(1), 13012 (2018)
Voronin, V.V., et al.: Medical image segmentation by combing the local, global enhancement, and active contour model. In: Anomaly Detection and Imaging with X-Rays (ADIX) IV (2019)
Sakinis, T., et al.: Interactive segmentation of medical images through fully convolutional neural networks. ar**v e-prints (2019)
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Kong, Y., Dun, Y., Meng, J., Wang, L., Zhang, W., Li, X. (2020). A Novel Classification Method of Medical Image Segmentation Algorithm. In: Su, R., Liu, H. (eds) Medical Imaging and Computer-Aided Diagnosis. MICAD 2020. Lecture Notes in Electrical Engineering, vol 633. Springer, Singapore. https://doi.org/10.1007/978-981-15-5199-4_11
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DOI: https://doi.org/10.1007/978-981-15-5199-4_11
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