Computed Tomography Image Analysis of Body Fat Based on Multi-Image Information.
Body fat assessment is required as part of an objective health assessment, both for nonobese and obese people. Image-based body fat assessment will enable faster diagnosis. Body fat analysis that accounts for age and sex will help in both diagnosis and correlating diseases and fat distribution. Afte...
| Publicado en: | BioMed Research International pp. 1 - 12 |
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| Autores principales: | , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
6/20/2022
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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=157548861&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157548861 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 6/20/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 157548861 157548861 157548861 10.1155/2022/8265211 157548861 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Computed Tomography Image Analysis of Body Fat Based on Multi-Image Information. aug: au: Zang, Wei Zhu, Fengrui Yu, Yang affil: Department of 2nd Clinical College, China Medical University, Shenyang 110004, China sug: subj: Tomography, X-Ray Computed Adipose Tissue Radiography Image Processing, Computer Assisted Human Graphical User Interface Abdominal Fat Radiography Algorithms Scales ab: Body fat assessment is required as part of an objective health assessment, both for nonobese and obese people. Image-based body fat assessment will enable faster diagnosis. Body fat analysis that accounts for age and sex will help in both diagnosis and correlating diseases and fat distribution. After evaluating computed tomography imaging algorithms to identify and segment human abdominal and subcutaneous fat, we present an improved region growing scale-invariant feature transform algorithm. It applies Naive Bayes image thresholding for key point selection and image matching. This method enables rapid and accurate comparison and matching of images from multiple databases and improves the efficiency of image processing. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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