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13 June 2024 Research on continuous monitoring of subdural hematoma based on optical image mapping feature extraction method
Guang Han, Zhonghua Pan, Yuqing Liu, Jinhai Wang, Zhe Zhao, Huiquan Wang
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Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131800D (2024) https://doi.org/10.1117/12.3033759
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China
Abstract
Diffuse optical tomography (DOT) guided by medical images can be used to achieve real-time reconstruction of subdural hematoma images for monitoring purposes. However, when the hematoma is irregular and the signal to noise ratio(SNR) of input signal is low, the reconstruction effect of this method is not ideal. In order to alleviate this situation and improve the reconstruction effect of cerebral hematoma images, we proposed a optical image mapping feature extraction method in the paper. The result of experiment shows that the mean relative volume error (VRE) of the hematoma reconstruction model optimized by the optical image mapping feature extraction method is only 0.79%, and the mean value of the average Manhattan distance (AMD) between the reconstructed absorption coefficient and the true absorption coefficient is 0.0062mm-1. Compared with the model directly inputting optical information, the optical image mapping feature extraction method reduces the average VRE of the model by 93.3%, and the average AMD by 27.1%. This method provides a promising method for non-invasive continuous monitoring of clinical cerebral hematoma.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Guang Han, Zhonghua Pan, Yuqing Liu, Jinhai Wang, Zhe Zhao, and Huiquan Wang "Research on continuous monitoring of subdural hematoma based on optical image mapping feature extraction method", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131800D (13 June 2024); https://doi.org/10.1117/12.3033759
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KEYWORDS
Image restoration
Feature extraction
Associative arrays
Data modeling
Brain tissue
Image processing
3D modeling
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