Segmentation of pulmonary nodules using adaptive local region energy with probability density function-based similarity distance and multi-features clustering.
Background: Pulmonary nodules in computerized tomography (CT) images are potential manifestations of lung cancer. Segmentation of potential nodule objects is the first necessary and crucial step in computer-aided detection system of pulmonary nodules. The segmentation of various types of nodules, es...
| Publicado en: | BioMedical Engineering OnLine Vol. 15; pp. 49 - 50 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
5/5/2016
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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=120353626&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120353626 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 5/5/2016 vid: 15 pid: 24147 pub: BioMed Central artinfo: ui: 120353626 120353626 NLM27150553 10.1186/s12938-016-0164-3 NLM27150553 PMC4858846 120353626 ppf: 49 ppct: 1 formats: tig: atl: Segmentation of pulmonary nodules using adaptive local region energy with probability density function-based similarity distance and multi-features clustering. aug: au: Li, Bin Chen, QingLin Peng, Guangming Guo, Yuanxing Chen, Kan Tian, LianFang Ou, Shanxing Wang, Lifei affil: School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, Guangdong, China sug: subj: Image Processing, Computer Assisted Methods Algorithms Lung Diseases Cluster Analysis Resource Databases Tomography, X-Ray Computed Scales ab: Background: Pulmonary nodules in computerized tomography (CT) images are potential manifestations of lung cancer. Segmentation of potential nodule objects is the first necessary and crucial step in computer-aided detection system of pulmonary nodules. The segmentation of various types of nodules, especially for ground-glass opacity (GGO) nodules and juxta-vascular nodules, present various challenges. The nodule with GGO characteristic possesses typical intensity inhomogeneity and weak edges, which is difficult to define the boundary; the juxta-vascular nodule is connected to a vessel, and they have very similar intensities. Traditional segmentation methods may result in the problems of boundary leakage and a small volume over-segmentation. This paper deals with the above mentioned problems. Methods: A novel segmentation method for pulmonary nodules is proposed, which uses an adaptive local region energy model with probability density function (PDF)-based similarity distance and multi-features dynamic clustering refinement method. Our approach has several novel aspects: (1) in the proposed adaptive local region energy model, the local domain for local energy model is selected adaptively based on k-nearest-neighbour (KNN) estimate method, and measurable distances between probability density functions of multi-dimension features with high class separability are used to build the cost function. (2) A multi-features dynamic clustering method is used for the segmentation refinement of juxta-vascular nodules, which is based on the nodule segmentation using active contour model (ACM) with adaptive local region energy and vessel segmentation using flow direction feature (FDF)-based region growing method. (3) it handles various types of nodules under a united framework. Results: The proposed method has been validated on a clinical dataset of 113 chest CT scans that contain 157 nodules determined by a ground truth reading process, and evaluating the algorithm on the provided data leads to an average Tanimoto/Jaccard error of 0.17, 0.20 and 0.24 for GGO, juxta-vascular and GGO juxta-vascular nodules, respectively. Conclusions: Experimental results show desirable performances of the proposed method. The proposed segmentation method outperforms the traditional methods. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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