Abstract
As a routine tool for screening and examination, CT plays an important role in disease detection and diagnosis. Real-time table removal in CT images becomes a fundamental task to improve readability, interpretation and treatment planning. Meanwhile, it makes data management simple and benefits information sharing and communication in picture archiving and communication system. In this paper, we proposed an automated framework which utilized parallel programming to address this problem. Eight full-body CT images were collected and analyzed. Experimental results have shown that with parallel programming, the proposed framework can accelerate the patient table removal task up to three times faster when it was running on a personal computer with four-core central processing unit. Moreover, the segmentation accuracy reaches 99 % of Dice coefficient. The idea behind this approach refreshes many algorithms for real-time medical image processing without extra hardware spending.
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Acknowledgment
This work is supported by grants from National Natural Science Foundation of China (Grant No. 81501463), Guangdong Innovative Research Team Program (Grant No. 2011S013), National 863 Programs of China (Grant No. 2015AA043203), Shenzhen Fundamental Research Program (Grant Nos. JCYJ20140417113430726, JCYJ20140417113430665 and JCYJ201500731154850923) and Beijing Center for Mathematics and Information Interdisciplinary Sciences.
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Chen, L., Wu, S., Zhang, Z., Yu, S., Xie, Y., Zhang, H. (2016). Real-Time Patient Table Removal in CT Images. In: Yin, X., Geller, J., Li, Y., Zhou, R., Wang, H., Zhang, Y. (eds) Health Information Science. HIS 2016. Lecture Notes in Computer Science(), vol 10038. Springer, Cham. https://doi.org/10.1007/978-3-319-48335-1_1
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DOI: https://doi.org/10.1007/978-3-319-48335-1_1
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