| ORIGINAL ARTICLE |
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| Year : 2019 | Volume
: 3
| Issue : 1 | Page : 9-11 |
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Research on medical image segmentation based on fuzzy clustering algorithm
J Li1, Y Ning2, ZM Yuan3, CJ Yang4
1 National Key Laboratory of Air Traffic Flow Management; College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Guigang, China
2 Aid Station in 75130 Units of the Chinese People's Liberation Army, Guigang, China
3 School of Medicine, Nanjing Tongren Hospital, Southeast University, Nanjing, China
4 National Satellite Meteorological Centre, Beijing, China
Correspondence Address:
Dr. J Li
National Key Laboratory of Air Traffic Flow Management, Nanjing 211106; College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106
China
 Source of Support: None, Conflict of Interest: None  | 4 |
DOI: 10.4103/MTSP.MTSP_3_19
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Objectives: The aim of the study is to apply the fuzzy clustering algorithm to medical image segmentation technology and analyze the application effect of the algorithm. Methods: In this study, the application of bacterial fuzzy clustering algorithm and bacterial foraging optimization algorithm in tooth image segmentation is analyzed. Among them, bacteria fuzzy clustering algorithm is a research group, whereas bacteria foraging optimization algorithm is a conventional group. Relevant researchers need to compare the separation index, partition coefficient, and partition index of the two algorithms. Results: Compared with the conventional group, the separation index and the partition coefficient of the experimental group were relatively high, and the two groups in the separation index and partition coefficients have a statistically significant difference (P < 0.05); compared with the experimental group, the index value was higher in the conventional group, and there was significant difference between the two groups in the zoning index (P < 0.05). Conclusions: Compared with the traditional bacterial optimization algorithm, the application of the bacterial fuzzy clustering algorithm in tooth image segmentation is more remarkable.
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