Cargando…

A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest

BACKGROUND: Lung parenchyma segmentation is often performed as an important pre-processing step in the computer-aided diagnosis of lung nodules based on CT image sequences. However, existing lung parenchyma image segmentation methods cannot fully segment all lung parenchyma images and have a slow pr...

Descripción completa

Detalles Bibliográficos
Autores principales: Liao, Xiaolei, Zhao, Juanjuan, Jiao, Cheng, Lei, Lei, Qiang, Yan, Cui, Qiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4988714/
https://www.ncbi.nlm.nih.gov/pubmed/27532214
http://dx.doi.org/10.1371/journal.pone.0160556
_version_ 1782448465858527232
author Liao, Xiaolei
Zhao, Juanjuan
Jiao, Cheng
Lei, Lei
Qiang, Yan
Cui, Qiang
author_facet Liao, Xiaolei
Zhao, Juanjuan
Jiao, Cheng
Lei, Lei
Qiang, Yan
Cui, Qiang
author_sort Liao, Xiaolei
collection PubMed
description BACKGROUND: Lung parenchyma segmentation is often performed as an important pre-processing step in the computer-aided diagnosis of lung nodules based on CT image sequences. However, existing lung parenchyma image segmentation methods cannot fully segment all lung parenchyma images and have a slow processing speed, particularly for images in the top and bottom of the lung and the images that contain lung nodules. METHOD: Our proposed method first uses the position of the lung parenchyma image features to obtain lung parenchyma ROI image sequences. A gradient and sequential linear iterative clustering algorithm (GSLIC) for sequence image segmentation is then proposed to segment the ROI image sequences and obtain superpixel samples. The SGNF, which is optimized by a genetic algorithm (GA), is then utilized for superpixel clustering. Finally, the grey and geometric features of the superpixel samples are used to identify and segment all of the lung parenchyma image sequences. RESULTS: Our proposed method achieves higher segmentation precision and greater accuracy in less time. It has an average processing time of 42.21 seconds for each dataset and an average volume pixel overlap ratio of 92.22 ± 4.02% for four types of lung parenchyma image sequences.
format Online
Article
Text
id pubmed-4988714
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-49887142016-08-29 A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest Liao, Xiaolei Zhao, Juanjuan Jiao, Cheng Lei, Lei Qiang, Yan Cui, Qiang PLoS One Research Article BACKGROUND: Lung parenchyma segmentation is often performed as an important pre-processing step in the computer-aided diagnosis of lung nodules based on CT image sequences. However, existing lung parenchyma image segmentation methods cannot fully segment all lung parenchyma images and have a slow processing speed, particularly for images in the top and bottom of the lung and the images that contain lung nodules. METHOD: Our proposed method first uses the position of the lung parenchyma image features to obtain lung parenchyma ROI image sequences. A gradient and sequential linear iterative clustering algorithm (GSLIC) for sequence image segmentation is then proposed to segment the ROI image sequences and obtain superpixel samples. The SGNF, which is optimized by a genetic algorithm (GA), is then utilized for superpixel clustering. Finally, the grey and geometric features of the superpixel samples are used to identify and segment all of the lung parenchyma image sequences. RESULTS: Our proposed method achieves higher segmentation precision and greater accuracy in less time. It has an average processing time of 42.21 seconds for each dataset and an average volume pixel overlap ratio of 92.22 ± 4.02% for four types of lung parenchyma image sequences. Public Library of Science 2016-08-17 /pmc/articles/PMC4988714/ /pubmed/27532214 http://dx.doi.org/10.1371/journal.pone.0160556 Text en © 2016 Liao et al http://creativecommons.org/licenses/by/4.0/ 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.
spellingShingle Research Article
Liao, Xiaolei
Zhao, Juanjuan
Jiao, Cheng
Lei, Lei
Qiang, Yan
Cui, Qiang
A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest
title A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest
title_full A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest
title_fullStr A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest
title_full_unstemmed A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest
title_short A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest
title_sort segmentation method for lung parenchyma image sequences based on superpixels and a self-generating neural forest
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4988714/
https://www.ncbi.nlm.nih.gov/pubmed/27532214
http://dx.doi.org/10.1371/journal.pone.0160556
work_keys_str_mv AT liaoxiaolei asegmentationmethodforlungparenchymaimagesequencesbasedonsuperpixelsandaselfgeneratingneuralforest
AT zhaojuanjuan asegmentationmethodforlungparenchymaimagesequencesbasedonsuperpixelsandaselfgeneratingneuralforest
AT jiaocheng asegmentationmethodforlungparenchymaimagesequencesbasedonsuperpixelsandaselfgeneratingneuralforest
AT leilei asegmentationmethodforlungparenchymaimagesequencesbasedonsuperpixelsandaselfgeneratingneuralforest
AT qiangyan asegmentationmethodforlungparenchymaimagesequencesbasedonsuperpixelsandaselfgeneratingneuralforest
AT cuiqiang asegmentationmethodforlungparenchymaimagesequencesbasedonsuperpixelsandaselfgeneratingneuralforest
AT liaoxiaolei segmentationmethodforlungparenchymaimagesequencesbasedonsuperpixelsandaselfgeneratingneuralforest
AT zhaojuanjuan segmentationmethodforlungparenchymaimagesequencesbasedonsuperpixelsandaselfgeneratingneuralforest
AT jiaocheng segmentationmethodforlungparenchymaimagesequencesbasedonsuperpixelsandaselfgeneratingneuralforest
AT leilei segmentationmethodforlungparenchymaimagesequencesbasedonsuperpixelsandaselfgeneratingneuralforest
AT qiangyan segmentationmethodforlungparenchymaimagesequencesbasedonsuperpixelsandaselfgeneratingneuralforest
AT cuiqiang segmentationmethodforlungparenchymaimagesequencesbasedonsuperpixelsandaselfgeneratingneuralforest