Research Article
Research on Brain Image Segmentation Based on FCM Algorithm Optimization
@INPROCEEDINGS{10.1007/978-3-030-82565-2_23, author={Xinlei Chen and Dongming Zhao and Wei Zhong and Jiufeng Ye}, title={Research on Brain Image Segmentation Based on FCM Algorithm Optimization}, proceedings={Multimedia Technology and Enhanced Learning. Third EAI International Conference, ICMTEL 2021, Virtual Event, April 8--9, 2021, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2021}, month={7}, keywords={Brain disease Image segmentation Image processing}, doi={10.1007/978-3-030-82565-2_23} }- Xinlei Chen
Dongming Zhao
Wei Zhong
Jiufeng Ye
Year: 2021
Research on Brain Image Segmentation Based on FCM Algorithm Optimization
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-030-82565-2_23
Abstract
Brain disease is becoming a threat to human health. Many countries begin to pay attention to the research of brain science. If brain diseases are predicted in advance, diagnosed accurately and treated with comprehensive intervention, the life expectancy of patients will be greatly improved. There are many explorations and applications in the field of computer-aided disease diagnosis, which can significantly improve the efficiency of disease diagnosis. Medical image processing is one of the medical imaging technology. It can help doctors improve the diagnosis quality by processing and analyzing the medical image data by computer. An improved FCM clustering method Sagakfcm algorithm is proposed for brain tissue segmentation in MRI images. Sagakfcm model fully combines the advantages of simulated annealing algorithm and genetic algorithm, so as to obtain the best initial clustering center, reduce the iteration times of fuzzy c-means algorithm, avoid the initial clustering falling into local optimum, and accelerate the operation speed. The algorithm combines Gaussian kernel function to improve the robustness of FCM algorithm.
