Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation.
The automated segmentation of liver and tumor from CT images is of great importance in medical diagnoses and clinical treatment. However, accurate and automatic segmentation of liver and tumor is generally complicated due to the complex anatomical structures and low contrast. This paper proposes a r...
| Publicado en: | BioMed Research International Vol. 2018; pp. 1 - 12 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
9/24/2018
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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=131931707&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131931707 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 9/24/2018 vid: 2018 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 131931707 131931707 131931707 10.1155/2018/8536854 131931707 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation. aug: au: Jiang, Huiyan Li, Shaojie Li, Siqi affil: Department of Software College, Northeastern University, Shenyang 110819, China sug: subj: Liver Neoplasms Radiography Tomography, X-Ray Computed Human ab: The automated segmentation of liver and tumor from CT images is of great importance in medical diagnoses and clinical treatment. However, accurate and automatic segmentation of liver and tumor is generally complicated due to the complex anatomical structures and low contrast. This paper proposes a registration-based organ positioning (ROP) and joint segmentation method for liver and tumor segmentation from CT images. First, a ROP method is developed to obtain liver’s bounding box accurately and efficiently. Second, a joint segmentation method based on fuzzy c-means (FCM) and extreme learning machine (ELM) is designed to perform coarse liver segmentation. Third, the coarse segmentation is regarded as the initial contour of active contour model (ACM) to refine liver boundary by considering the topological information. Finally, tumor segmentation is performed using another ELM. Experiments on two datasets demonstrate the performance advantages of our proposed method compared with other related works. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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