| Automatic Segmentation of Ground-Glass Opacities in Lung CT Images by Using Markov Random Field-Based Algorithms. | |
| | |
MedLine Citation:
|
PMID: 22089834 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
|
Chest radiologists rely on the segmentation and quantificational analysis of ground-glass opacities (GGO) to perform imaging diagnoses that evaluate the disease severity or recovery stages of diffuse parenchymal lung diseases. However, it is computationally difficult to segment and analyze patterns of GGO while compared with other lung diseases, since GGO usually do not have clear boundaries. In this paper, we present a new approach which automatically segments GGO in lung computed tomography (CT) images using algorithms derived from Markov random field theory. Further, we systematically evaluate the performance of the algorithms in segmenting GGO in lung CT images under different situations. CT image studies from 41 patients with diffuse lung diseases were enrolled in this research. The local distributions were modeled with both simple and adaptive (AMAP) models of maximum a posteriori (MAP). For best segmentation, we used the simulated annealing algorithm with a Gibbs sampler to solve the combinatorial optimization problem of MAP estimators, and we applied a knowledge-guided strategy to reduce false positive regions. We achieved AMAP-based GGO segmentation results of 86.94%, 94.33%, and 94.06% in average sensitivity, specificity, and accuracy, respectively, and we evaluated the performance using radiologists' subjective evaluation and quantificational analysis and diagnosis. We also compared the results of AMAP-based GGO segmentation with those of support vector machine-based methods, and we discuss the reliability and other issues of AMAP-based GGO segmentation. Our research results demonstrate the acceptability and usefulness of AMAP-based GGO segmentation for assisting radiologists in detecting GGO in high-resolution CT diagnostic procedures. |
| | |
Authors:
|
Yanjie Zhu; Yongqing Tan; Yanqing Hua; Guozhen Zhang; Jianguo Zhang |
Related Documents
:
|
10682984 - Syndrome simulating pseudotumor cerebri caused by partial transverse venous sinus obstr... 9152814 - Superior orbital fissure syndrome caused by intraorbital spread of a cutaneous squamous... 1414844 - Proboscis lateralis with associated orbital cyst: detailed mr and ct imaging and correl... 8470584 - Lesions causing a mass in the medial canthus of the orbit: ct and mr features. 16648154 - Quantitative measurement and visual assessment of ileal crohn's disease activity by com... 9797164 - In vivo gradient echo microimaging of rodent spinal cord at 7 t. |
Publication Detail:
|
Type: JOURNAL ARTICLE Date: 2011-11-17 |
Journal Detail:
|
Title: Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology Volume: - ISSN: 1618-727X ISO Abbreviation: - Publication Date: 2011 Nov |
Date Detail:
|
Created Date: 2011-11-17 Completed Date: - Revised Date: - |
Medline Journal Info:
|
Nlm Unique ID: 9100529 Medline TA: J Digit Imaging Country: - |
Other Details:
|
Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
|
Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 500 Yu Tian Road, Shanghai, 200083, China. |
Export Citation:
|
APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
|
|
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
Previous Document: Clinical Expression and New SPINK5 Splicing Defects in Netherton Syndrome: Unmasking a Frequent Foun...
Next Document: Breast cancer: Overview & updates.