Automatic Liver Segmentation from CT Images Using Single-Block Linear Detection.
Automatic liver segmentation not only plays an important role in the analysis of liver disease, but also reduces the cost and humanity’s impact in segmentation. In addition, liver segmentation is a very challenging task due to countless anatomical variations and technical difficulties. Many methods...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 12 |
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| Autores principales: | , , , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/18/2016
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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=117520411&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117520411 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/18/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 117520411 117520411 117520411 10.1155/2016/9420148 117520411 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Automatic Liver Segmentation from CT Images Using Single-Block Linear Detection. aug: au: Huang, Lianfen Weng, Minghui Shuai, Haitao Huang, Yue Sun, Jianjun Gao, Fenglian affil: Xiamen University, Xiamen, Fujian 361005, China sug: subj: Liver Radiography Tomography, X-Ray Computed Methods Automation Liver Diseases Diagnosis Human Algorithms Time Factors Hemangioma Diagnosis Liver Neoplasms Diagnosis Sensitivity and Specificity Validity Descriptive Statistics Artifacts Male Female Radiography, Abdominal False Positive Results False Negative Results Funding Source Male Female ab: Automatic liver segmentation not only plays an important role in the analysis of liver disease, but also reduces the cost and humanity’s impact in segmentation. In addition, liver segmentation is a very challenging task due to countless anatomical variations and technical difficulties. Many methods have been designed to overcome these challenges, but these methods still need to be improved to obtain the desired segmentation precision. In this paper, a fast algorithm is proposed for liver extraction from CT images with single-block linear detection. The proposed method does not require iteration; thus, the computational time and complexity are decreased enormously. In addition, the initialization is not crucial in the algorithm, so the algorithm’s robustness and specificity are improved. The experimental evaluation of the proposed method revealed effective segmentation in normal and abnormal (liver hemangioma and liver cancer) abdominal CT images. The average sensitivity, accuracy, and specificity for liver cancer are 96.59%, 98.65%, and 99.03%, respectively. The results of image segmentation approximate the manual segmentation results by the technical doctor. Moreover, our method shows superior flexibility to newly published method with comparable performance. The advantage of our method is verified with experimental results, which is described in detail. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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