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

Amber L. Simpson; Richard K. Do; E. Patricia Parada; Michael I. Miga; William R. Jarnagin

doi:10.1117/12.2043055

21 March 2014 Texture feature analysis for prediction of postoperative liver failure prior to surgery

Amber L. Simpson, Richard K. Do, E. Patricia Parada, Michael I. Miga, William R. Jarnagin

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 903414 (2014) https://doi.org/10.1117/12.2043055
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

Citation Download Citation

Amber L. Simpson, Richard K. Do, E. Patricia Parada, Michael I. Miga, and William R. Jarnagin "Texture feature analysis for prediction of postoperative liver failure prior to surgery", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903414 (21 March 2014); https://doi.org/10.1117/12.2043055

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