Brain tumor image segmentation algorithm based on hybrid model feature extraction

Guoliang Wang; Ju Li

doi:10.1117/12.3020759

25 March 2024 Brain tumor image segmentation algorithm based on hybrid model feature extraction

Guoliang Wang, Ju Li

Proceedings Volume 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023); 130890D (2024) https://doi.org/10.1117/12.3020759
Event: Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 2023, Suzhou, China

Abstract

Aiming at the problem that there are often errors in brain tumor image segmentation due to uneven gray scale and blurred boundaries in brain MRI images, a brain tumor segmentation method based on the combination of graph cutting algorithm and MAP_MRF algorithm and cellular automata model is proposed. A MRF_GCGMM model is established to automatically select the initial seed point based on the grayscale features of the image, and then modify the state transfer function of the cellular automata to achieve accurate segmentation of the brain tumor area. Experimental results show that the proposed algorithm has a higher segmentation accuracy of brain tumor images in the BraTs dataset than that of traditional brain tumor segmentation methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guoliang Wang and Ju Li "Brain tumor image segmentation algorithm based on hybrid model feature extraction", Proc. SPIE 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 130890D (25 March 2024); https://doi.org/10.1117/12.3020759

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