3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts.
Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient...
| Publicado en: | BioMed Research International Vol. 2017; pp. 1 - 12 |
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| Autores principales: | , , , , |
| Formato: | algorithm diagnostic images equations & formulas pictorial research Journal Article |
| Publicado: |
Wiley-Blackwell
9/26/2017
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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=125343172&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125343172 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 9/26/2017 vid: 2017 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 125343172 125343172 125343172 10.1155/2017/5207685 125343172 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: 3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts. aug: au: Wu, Weiwei Wu, Shuicai Zhou, Zhuhuang Zhang, Rui Zhang, Yanhua affil: Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China sug: subj: Imaging, Three-Dimensional Tomography, X-Ray Computed Graphics Cluster Analysis Liver Neoplasms Physiopathology Computer-Aided Design Technology, Medical ab: Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy C-means (FCM) and graph cuts. With a single seed point, the tumor volume of interest (VOI) was extracted using confidence connected region growing algorithm to reduce computational cost. Then, initial foreground/background regions were labeled automatically, and a kernelized FCM with spatial information was incorporated in graph cuts segmentation to increase segmentation accuracy. The proposed method was evaluated on the public clinical dataset (3Dircadb), which included 15 CT volumes consisting of various sizes of liver tumors. We achieved an average volumetric overlap error (VOE) of 29.04% and Dice similarity coefficient (DICE) of 0.83, with an average processing time of 45 s per tumor. The experimental results showed that the proposed method was accurate for 3D liver tumor segmentation with a reduction of processing time. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas pictorial research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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