Robust segmentation of lung parenchyma based on fuzzy region contrast enhancement
WU Zhenyu
BAI Peirui
LIU Yiwei
REN Yande
Abstract：In this paper, we proposed a novel lung parenchyma segmentation algorithm which is to combine contrast enhancement of fuzzy region with refinement segmentation using threshold and morphological.This algorithm could deal with effectively negative effects of lung adhesion region to lung parenchyma segmentation.First, the original CT image was pre-segmented into several super-pixel patches using the linear iterative clustering (SLIC0)in terms of gray intensity.Second, the fuzzy regions on CT image were located automatically by statistic information of the super-pixel patches, and contrast enhancement was implemented adaptively in the corre-sponding regions.Finally, refinement segmentation was performed by employing thresholding and morphological operation to extract the lung adhesion regions and lung parenchyma accurately.The performance of the proposed algorithm was validated on 300 CT images of 30 patients which were obtained from the open lung dataset,i.e.kaggle.The experimental results demonstrate that the mean dice coef-ficient of the proposed algorithm is 98.65%, the mean over-segmentation is 0.21%and the mean under-segmentation is 1.33%re-spectively.The segmentation performance of the proposed algorithm outperforms obviously the classical threshold operation and morpho-logical methods.
Keywords：Lung parenchyma segmentationSuper-pixelLocal contrast enhancementRefinement segmentationMorphological processing
Publication Date：2018-01-01
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：6( 153-158 )