Lung region segmentation based on multi-resolution Active Shape Model
Objective:Segmentation of lung region in chest radiographs is a key point in lung disease diagnosis.Because the complexity of anatomical structures and the overlap of organ and tissues’intensity ranges in chest radiographs,it has set a higher demand for the lung region segmentation than the sharp-edged medical image,and the performance of low-level segmentation methods that use local intensity criteria only are not so satisfactory in chest radiographs.In this article,we presented a new segmentation method in chest radiographs.Methodology:We investigated Active Shape Model (ASM) segmentation in chest radiograph.However,the original ASM suffers from the loss of accuracy and low speed in real time applications.In this paper,automatic point insertion,semiautomatic adjustment of initial placement and multi-resolution framework were introduced to improve the performance of original ASM.Results:80 chest radiographs have been used to test our algorithm,and the result showed that 68 images were segmented effectively.Experiments also showed that the multi-resolution search took less CPU time required by the original method because the new method converges after less iteration.Conclusions:The improved Active Shape Model provides a fast,effective,semiautomatic and model-based method for lung region segmentation in chest radiographs.Besides chest radiographs,multi-ASM can also be used in other medical images,such as CT,MRI,ultrasound images and so on.
chest radiographs multi-ASM lung region segmentation initial placement Point Distribution Model
Chunyan Wang Shengwen Guo Jianbo Wu Qiong Liu Xiaoming Wu
Department of Biomedical Engineering,South China University of Technology,Guangzhou,China Computing Center in South Campus,South China University of Technology,Guangzhou,China
国际会议
7th Asian-Pacific Conference on Medical and Biological Engineering(第七届亚太地区生物工程学术会议)
北京
英文
2008-04-22（万方平台首次上网日期，不代表论文的发表时间）