Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference.
Background: Image segmentation is an important part of computer-aided diagnosis (CAD), the segmentation of small ground glass opacity (GGO) pulmonary nodules is beneficial for the early detection of lung cancer. For the segmentation of small GGO pulmonary nodules, an integrated active contour model...
| Publicado en: | BioMedical Engineering OnLine Vol. 19; no. 1; pp. 1 - 21 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
6/17/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=143818353&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143818353 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 6/17/2020 vid: 19 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 143818353 143818353 NLM32552724 10.1186/s12938-020-00793-0 NLM32552724 143818353 ppf: 1 ppct: 20 formats: tig: atl: Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference. aug: au: Zhang, Shaorong Chen, Xiangmeng Zhu, Zhibin Feng, Bao Chen, Yehang Long, Wansheng affil: School of Electronic Engineering and Automation, Guilin University of Electronic Technology, 541004, Guilin, China sug: subj: Lung Neoplasms Image Processing, Computer Assisted Methods Probability Female Adult Male Tomography, X-Ray Computed Scales Adult: 19-44 years Female Male ab: Background: Image segmentation is an important part of computer-aided diagnosis (CAD), the segmentation of small ground glass opacity (GGO) pulmonary nodules is beneficial for the early detection of lung cancer. For the segmentation of small GGO pulmonary nodules, an integrated active contour model based on Markov random field energy and Bayesian probability difference (IACM_MRFEBPD) is proposed in this paper.Methods: First, the Markov random field (MRF) is constructed on the computed tomography (CT) images, then the MRF energy is calculated. The MRF energy is used to construct the region term. It can not only enhance the contrast between pulmonary nodule and the background region, but also solve the problem of intensity inhomogeneity using local spatial correlation information between neighboring pixels in the image. Second, the Gaussian mixture model is used to establish the probability model of the image, and the model parameters are estimated by the expectation maximization (EM) algorithm. So the Bayesian posterior probability difference of each pixel can be calculated. The probability difference is used to construct the boundary detection term, which is 0 at the boundary. Therefore, the blurred boundary problem can be solved. Finally, under the framework of the level set, the integrated active contour model is constructed.Results: To verify the effectiveness of the proposed method, the public data of the lung image database consortium and image database resource initiative (LIDC-IDRI) and the clinical data of the Affiliated Jiangmen Hospital of Sun Yat-sen University are used to perform experiments, and the intersection over union (IOU) score is used to evaluate the segmentation methods. Compared with other methods, the proposed method achieves the best results with the highest average IOU of 0.7444, 0.7503, and 0.7450 for LIDC-IDRI test set, clinical test set, and all test sets, respectively.Conclusions: The experiment results show that the proposed method can segment various small GGO pulmonary nodules more accurately and robustly, which is helpful for the accurate evaluation of medical imaging. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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