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Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 575086, 10 pages
http://dx.doi.org/10.1155/2013/575086
Research Article

A Semiautomatic Segmentation Algorithm for Extracting the Complete Structure of Acini from Synchrotron Micro-CT Images

1Department of Mechanical Science and Bioengineering, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan
2The Center for Advanced Medical Engineering and Informatics, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan

Received 4 January 2013; Accepted 21 January 2013

Academic Editor: Peng Feng

Copyright © 2013 Luosha Xiao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Pulmonary acinus is the largest airway unit provided with alveoli where blood/gas exchange takes place. Understanding the complete structure of acinus is necessary to measure the pathway of gas exchange and to simulate various mechanical phenomena in the lungs. The usual manual segmentation of a complete acinus structure from their experimentally obtained images is difficult and extremely time-consuming, which hampers the statistical analysis. In this study, we develop a semiautomatic segmentation algorithm for extracting the complete structure of acinus from synchrotron micro-CT images of the closed chest of mouse lungs. The algorithm uses a combination of conventional binary image processing techniques based on the multiscale and hierarchical nature of lung structures. Specifically, larger structures are removed, while smaller structures are isolated from the image by repeatedly applying erosion and dilation operators in order, adjusting the parameter referencing to previously obtained morphometric data. A cluster of isolated acini belonging to the same terminal bronchiole is obtained without floating voxels. The extracted acinar models above 98% agree well with those extracted manually. The run time is drastically shortened compared with manual methods. These findings suggest that our method may be useful for taking samples used in the statistical analysis of acinus.