Open AccessValidation of Shape Context Based Image Registration Method Using Digital Image Correlation Measurement on a Rat Stomach
Author(s) -
Donghua Liao,
Peng Wang,
Jingbo Zhao,
Hans Gregersen
Publication year - 2014
Publication title -
journal of computational medicine
Language(s) - English
Resource type - Journals
eISSN - 2314-5080
pISSN - 2314-5099
DOI - 10.1155/2014/504656
Subject(s) - digital image correlation , context (archaeology) , sample (material) , surface (topology) , artificial intelligence , deformation (meteorology) , tracking (education) , image registration , computer vision , materials science , biomedical engineering , mathematics , computer science , image (mathematics) , optics , chemistry , geometry , physics , geology , engineering , chromatography , psychology , paleontology , pedagogy , composite material
Recently we developed analysis for 3D visceral organ deformation by combining the shape context (SC) method with a full-field strain (strain distribution on a whole 3D surface) analysis for calculating distension-induced rat stomach deformation. The surface deformation detected by the SC method needs to be further verified by using a feature tracking measurement. Hence, the aim of this study was to verify the SC method-based calculation by using digital image correlation (DIC) measurement on a rat stomach. The rat stomach exposed to distension pressures 0.0, 0.2, 0.4, and 0.6 kPa were studied using both 3D DIC system and SC-based image registration calculation. Three different surface sample counts between the reference and the target surfaces were used to gauge the effect of the surface sample counts on the calculation. Each pair of the surface points between the DIC measured target surface and the SC calculated correspondence surface was compared. Compared with DIC measurement, the SC calculated surface had errors from 5% to 23% at pressures from 0.2 to 0.6 kPa with different surface sample counts between the reference surface and the target surface. This indicates good qualitative and quantitative agreement on the surfaces with small dissimilarity and small sample count difference between the reference surface and the target surface. In conclusion, this is the first study to validate the 3D SC-based image registration method by using unique tracking features measurement. The developed method can be used in the future for analysing scientific and clinical data of visceral organ geometry and biomechanical properties in health and disease