Texture feature analysis for prediction of postoperative liver failure prior to surgery Conference Paper


Authors: Simpson, A. L.; Do, R. K.; Parada, E. P.; Miga, M. I.; Jarnagin, W. R.
Title: Texture feature analysis for prediction of postoperative liver failure prior to surgery
Conference Title: SPIE Medical Imaging 2014: Image Processing
Abstract: Texture analysis of preoperative CT images of the liver is undertaken in this study. Standard texture features were extracted from portal-venous phase contrast-enhanced CT scans of 36 patients prior to major hepatic resection and correlated to postoperative liver failure. Differences between patients with and without postoperative liver failure were statistically significant for contrast (measure of local variation), correlation (linear dependency of gray levels on neighboring pixels), cluster prominence (asymmetry), and normalized inverse difference moment (local homogeneity). Though texture features have been used to diagnose and characterize lesions, to our knowledge, parenchymal statistical variation has not been quantified and studied. We demonstrate that texture analysis is a valuable tool for quantifying liver function prior to surgery, which may help to identify and change the preoperative management of patients at higher risk for overall morbidity. © 2014 SPIE.
Keywords: patient monitoring; risk assessment; computerized tomography; medical imaging; surgery; image processing; contrast-enhanced ct; texture analysis; textures; cluster prominences; inverse differences; linear dependency; local variations; postoperative liver; statistical variations
Journal Title Proceedings of SPIE
Volume: 9034
Conference Dates: 2014 Feb 15-20
Conference Location: San Diego, CA
ISBN: 0277-786X
Publisher: SPIE  
Location: San Diego, CA
Date Published: 2014-03-21
Start Page: 903414
Language: English
DOI: 10.1117/12.2043055
PROVIDER: scopus
DOI/URL:
Notes: Progr. Biomed. Opt. Imaging Proc. SPIE -- Conference code: 105510 -- Export Date: 8 July 2014 -- 16 February 2014 through 18 February 2014 -- Source: Scopus
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  1. William R Jarnagin
    903 Jarnagin
  2. Kinh Gian Do
    256 Do