Abstract
Computed Tomography (CT) images are widely used due to their low cost and high effectiveness. However, artifacts caused by human motion lead to a decline in image quality, which affects diagnostic accuracy and prognosis. Recently, significant progress has been made in motion blur detection using Convolutional Neural Networks (CNNs). However, these CNN-based methods still fall short of meeting the requirements of the medical field. Furthermore, CNN-based artifacts can only handle the regular node, but do not suitable for the irregular node distribution scenario, which result in ignorance of the relationship between CT images. In this paper, a novel construction method for head CT images based on complex networks theory has been proposed. Firstly, the spatial-temporal information is utilized to construct the graph of head CT images. The relationship between different head CT images is depicted from a comprehensive perspective. The head CT images are mapped to a topology of CT image network. Secondly, structural differences are reflected by comparing topological characteristics between graph construction based on spatial-temporal domain and spatial information. Finally, multi-region image quality is classified using spatial-temporal community detection. Experimental results demonstrate that the spatial-temporal community detection method significantly improves the performance of multi-region quality assessment, achieving an accuracy of up to 99.79%. Moreover, it better satisfies the clinical requirement for the interpretability.
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Liu, Y., Wen, T., Xu, T., Li, B., Sun, W., Wu, Z. (2023). Multi-region Quality Assessment Based on Spatial-Temporal Community Detection from Computed Tomography Images. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_48
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