A segmentation method for lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise

The fast and accurate segmentation of lung nodule image sequences is the basis of subsequent processing and diagnostic analyses. However, previous research investigating nodule segmentation algorithms cannot entirely segment cavitary nodules, and the segmentation of juxta-vascular nodules is inaccur...

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Vydané v:PloS one Ročník 12; číslo 9; s. e0184290
Hlavní autori: Zhang, Wei, Zhang, Xiaolong, Zhao, Juanjuan, Qiang, Yan, Tian, Qi, Tang, Xiaoxian
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 07.09.2017
Public Library of Science (PLoS)
Predmet:
Accuracy
Algorithms
Biology and Life Sciences
CAT scans
Change detection
Classification
Cluster Analysis
Clustering
Computed tomography
Computer science
Databases, Factual
Datasets
Diagnostic systems
Drug dosages
Dynamic programming
Entropy
Health aspects
Image enhancement
Image processing
Image segmentation
Imaging, Three-Dimensional
International conferences
Iterative methods
Lung
Lung - pathology
Lung cancer
Lung nodules
Medical imaging
Medicine and Health Sciences
Methods
Multivariate analysis
Nodules
Noise
Physical Sciences
Preprocessing
Radiographic Image Interpretation, Computer-Assisted
Research and Analysis Methods
Solitary Pulmonary Nodule - pathology
Tomography, X-Ray Computed
Tumors
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí:The fast and accurate segmentation of lung nodule image sequences is the basis of subsequent processing and diagnostic analyses. However, previous research investigating nodule segmentation algorithms cannot entirely segment cavitary nodules, and the segmentation of juxta-vascular nodules is inaccurate and inefficient. To solve these problems, we propose a new method for the segmentation of lung nodule image sequences based on superpixels and density-based spatial clustering of applications with noise (DBSCAN). First, our method uses three-dimensional computed tomography image features of the average intensity projection combined with multi-scale dot enhancement for preprocessing. Hexagonal clustering and morphological optimized sequential linear iterative clustering (HMSLIC) for sequence image oversegmentation is then proposed to obtain superpixel blocks. The adaptive weight coefficient is then constructed to calculate the distance required between superpixels to achieve precise lung nodules positioning and to obtain the subsequent clustering starting block. Moreover, by fitting the distance and detecting the change in slope, an accurate clustering threshold is obtained. Thereafter, a fast DBSCAN superpixel sequence clustering algorithm, which is optimized by the strategy of only clustering the lung nodules and adaptive threshold, is then used to obtain lung nodule mask sequences. Finally, the lung nodule image sequences are obtained. The experimental results show that our method rapidly, completely and accurately segments various types of lung nodule image sequences.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0184290