Computer Science ›› 2017, Vol. 44 ›› Issue (8): 296-300.doi: 10.11896/j.issn.1002-137X.2017.08.051

Special Issue: Medical Imaging

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Segmentation of Lung CT Image Sequences Based on Improved Self-generating Neural Networks

LIAO Xiao-lei and ZHAO Juan-juan   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Existing lung segmentation methods cannot fully segment all lung parenchyma images and have slow proces-sing speed.The position of the lung was used to obtain lung ROI sequences,and an algorithm of superpixel sequences segmentation was then proposed to segment the ROI image sequences.In addition,improved self-generating neural networks were utilized for superpixel clustering and the grey and geometric features were extracted to identify and segment lung image sequences.The experimental results show that our method’s average processing time is 0.61 second for a single slice and it can achieve average volume pixel overlap ratio of 92.09±1.52%.Compared with the existing me-thods,our method has higher segmentation precision and accuracy with less time.

Key words: Lung sequences segmentation,ROI sequences,Superpixels,SGNN

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