Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/42379
Title: An automated segmentation method for lung parenchyma image sequences based on fractal geometry and convex hull algorithm
Authors: Xiao, Xiaojiao
Zhao, Juanjuan
Qiang, Yan
Wang, Hua
Xiao, Yingze
Zhang, Xiaolong
Zhang, Yudong Y
First Published: 21-May-2018
Publisher: MDPI
Citation: Applied Sciences, 2018, 8, 832
Abstract: Statistically solitary pulmonary nodules are about 6% to 17% of juxtapleural nodules. The accurate segmentation of lung parenchyma sequences of juxtapleural nodules is the basis of subsequent pulmonary nodule segmentation and detection. In order to solve the problem of incomplete segmentation of the juxtapleural nodules and segmentation inefficiency, this paper proposes an automated framework to combine the threshold iteration method to segment the lung parenchyma images and the fractal geometry method to detect the depression boundary. The framework includes an improved convex hull repair to complete the accurate segmentation of the lung parenchyma. The evaluation results confirm that the proposed method can segment juxtapleural lung parenchymal images accurately and efficiently.
DOI Link: 10.3390/app8050832
ISSN: 2076-3417
eISSN: 2076-3417
Links: http://www.mdpi.com/2076-3417/8/5/832
http://hdl.handle.net/2381/42379
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Description: This paper uses LIDC data set. The LIDC–IDRI database is the largest open lung nodule database in the world, which contains 1080 cases. https://wiki.cancerimagingarchive.net/display/Public/ LIDC-IDRI.
Appears in Collections:Published Articles, Dept. of Computer Science

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