Development of image analysis using Python: Relationship between matrix ratio of composite resin and curing temperature

Abstract

The aim of this study was to establish a measurement method for filler and matrix in cured resin composite (RC) using Python programming and to investigate the correlation between matrix ratio and curing temperature rise. Eight kinds of RCs were used. Backscattered electron images were taken for each cured specimen. Matrix and filler contents were calculated using Python programming with the K-means or area segmentation method. Volume measurement methods were assessed for comparison. Heat released during the polymerization reaction was measured. The matrix ratio was calculated without human intervention. Three specimens contained only inorganic filler, and other specimens contained multiple types of fillers. Almost the same values of the matrix ratio were obtained by programming and the volume measurement methods for specimens containing a single type of inorganic filler. Moreover, a strong correlation was found between the matrix ratio obtained by the programming method and curing temperature rise (R=0.9826).