Automatic Segmentation of Abdominal Fat in MRI-Scans, Using Graph-Cuts and Image Derived Energies

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2017

DOI

View graph of relations

For many clinical studies changes in the abdominal distribution of fat is an important measure. However, the segmentation of abdominal fat in MRI scans is both difficult and time consuming using manual methods. We present here an automatic and flexible software package, that performs both bias field correction and segmentation of the fat into superficial and deep subcutaneous fat as well as visceral fat with the spinal compartment removed. Assessment when comparing to the gold standard - CT-scans - shows a correlation and bias comparable to manual segmentation. The method is flexible by tuning the image-derived energies used for the segmentation, allowing the method to be applied to other body parts, such as the thighs.
Original languageEnglish
Title of host publicationImage Analysis
Volume10270
PublisherSpringer
Publication date2017
Pages109-120
ISBN (print)9783319591285
DOIs
StatePublished - 2017
Event20th Scandinavian Conference on Image Analysis - Tromsø, Norway

Conference

Conference20th Scandinavian Conference on Image Analysis
CountryNorway
CityTromsø
Period12/06/201714/06/2017
SeriesLecture Notes in Computer Science
Volume10270
ISSN0302-9743
CitationsWeb of Science® Times Cited: No match on DOI

    Keywords

  • Computer Science, Image Processing and Computer Vision, Pattern Recognition, Artificial Intelligence (incl. Robotics), Computer Graphics, Data Mining and Knowledge Discovery
Download as:
Download as PDF
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
Word

ID: 134356498