Optimization algorithm of CT image edge segmentation using improved convolution neural network

Xiaojuan Wang and Yuntao Wei

PLOS ONE, 2022, vol. 17, issue 6, 1-17

Abstract: To address the problem of high failure rate and low accuracy in computed tomography (CT) image edge segmentation, we proposed a CT sequence image edge segmentation optimization algorithm using improved convolution neural network. Firstly, the pattern clustering algorithm is applied to cluster the pixels with relationship in the CT sequence image space to extract the edge information of the real CT image; secondly, Euclidean distance is used to calculate similarity and measure similarity, according to the measurement results, convolution neural network (CNN) hierarchical optimization is carried out to improve the convergence ability of CNN; finally, the pixel classification of CT sequence images is carried out, and the edge segmentation of CT sequence images is optimized according to the classification results. The results show that the overall recognition rate of this method is at a high level. The training time is obviously reduced when the training times exceed 12 times, the recall rate is always about 90%, and the accuracy of image segmentation is high, which solves the problem of large failure rate and low accuracy.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0265338

DOI: 10.1371/journal.pone.0265338

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