生物医学工程研究2018，Vol.37Issue(2)：153-158,6.DOI:10.19529/j.cnki.1672-6278.2018.02.07
基于模糊区域对比度增强的肺实质鲁棒分割
Robust segmentation of lung parenchyma based on fuzzy region contrast enhancement
摘要
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.关键词
肺实质分割/超像素/局部对比度增强/细化分割/形态学处理Key words
Lung parenchyma segmentation/Super-pixel/Local contrast enhancement/Refinement segmentation/Morphological processing分类
医药卫生引用本文复制引用
武振宇,白培瑞,刘艺炜,任延德..基于模糊区域对比度增强的肺实质鲁棒分割[J].生物医学工程研究,2018,37(2):153-158,6.基金项目
国家自然科学基金资助项目(61471225). （61471225）
