.png){kind=icon}
返回
Lung field segmentation using weighted sparse shape composition with robust initialization
.png){kind=icon}
PRE
AI
DOI：10.1002/mp.12561.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
PurposeLung field segmentation for chest radiography is critical to pulmonary disease diagnosis. In this paper, we propose a new deformable model using weighted sparse shape composition with robust initialization to achieve robust and accurate lung field segmentation. MethodsOur method consists of three steps: initialization, deformation and regularization. The steps of deformation and regularization are iteratively employed until convergence. First, since a deformable model is sensitive to the initial shape, a robust initialization is obtained by using a novel voting strategy, which allows the reliable patches on the image to vote for each landmark of the initial shape. Then, each point of the initial shape independently deforms to the lung boundary under the guidance of the appearance model, which can distinguish lung tissues from nonlung tissues near the boundary. Finally, the deformed shape is regularized by weighted sparse shape composition (SSC) model, which is constrained by both boundary information and the correlations between each point of the deformed shape. ResultsOur method has been evaluated on 247 chest radiographs from well-known dataset Japanese Society of Radiological Technology (JSRT) and achieved high overlap scores (0.9550.021). ConclusionsThe experimental results show that the proposed deformable segmentation model is more robust and accurate than the traditional appearance and shape model on the JSRT database. Our method also shows higher accuracy than most state-of-the-art methods.
Keyword:
chest radiographs
deformable model
lung filed segmentation
voting strategy
weighted SSC model
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.2
论文数:
3.7W
被引数:
3.2W
机构
引用论文
FEATURE-SELECTION IN THE PATTERN-CLASSIFICATION PROBLEM OF DIGITAL CHEST RADIOGRAPH SEGMENTATION
PREAI
分享收藏Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database.png)
MEDICAL IMAGE ANALYSIS
OAAI
MEDICAL IMAGE ANALYSISIF11.8
OAAI分享收藏Deformable segmentation via sparse representation and dictionary learning基于稀疏表示和字典学习的可变形图像分割MEDICAL IMAGE ANALYSIS
MEDICAL IMAGE ANALYSISIF11.8
PREAI
分享收藏
分享收藏
查看更多内容