Research Article - Biomedical Research (2019) Volume 30, Issue 1

A reliable method for morphological measurement of 3D calcaneus models from computed tomography images.

The way to assess the calcaneal fractures reduction is still open. Anatomical morphology is an important clinical value to maintain normal function and may use as references to evaluate the reduction for calcaneal fractures. The aim of study is to provide reliable and repeatable three-dimensional (3D) morphology measurement. 3D models of ninety-nine Taiwanese normal calcaneus were reconstructed and segmented from CT image. Anatomical landmark serves as measurement constraints that manually identified and localized on bone mesh surface according to Gaussian curvature analysis. Computer assisted measurement and manual measurement were performed to quantify morphological parameters. Statistically, measurement results of calcaneal morphology were analyzed. The correlation between each of morphological parameters was tested using Bivariate-Pearson correlation. Gender differences relate to parameters were also tested. Intraclass correlation coefficient (ICC) and root mean square standard deviation (RMS-SD) were performed for reliable and precision study. Significant parameter differences between males and females were found (p<0.05). Yet, the correlation for each morphology parameters shows a strong correlation. Overall measurements show acceptable reliability and repeatability (ICC>0.754). RMS-SD of precision for linear and angular measurement was adequate, around 0.8 mm and 1.7°. Morphological parameters for both male and female groups, 78.5 mm in length, 42.1 in height, 43.3 mm in width, 112.5° in Gissane angle, 33.4° in Böhler angle, which is not much different compared to Asian morphology population. The proposed method positively contributes to measurement calcaneal morphology with significant reliability and repeatability. This study may offer to be a basic data and reference for assessment calcaneal fracture reduction.

Author(s): Irwansyah Idram, Jiing-Yih Lai and Pei-Yuan Lee

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