Assessment of trabecular bone structure using fuzzy distance transform based on Min-Max operations

Trabecular bone consists of a network of tiny strands and plates. Micro-structural features of trabecular bone include thickness, relative volume, spacing and connectivity. The accurate evaluation of these features is of significant interest in the assessment of the mechanical and transport properties of bone. Extracting these features from μCT and μMRI images is difficult and measurements vary substantially according to image processing technique used. In this paper, we propose a fuzzy distance transform (FDT) method to measure trabecular bone thickness based on Min-Max operations and using an additive weighting term. Due to the employed fuzzy Min-Max operations along with the additive weighing term, the FDT method is performed in integer number space to consequently produce a computationally fast, robust and efficient use of memory method with taking into account the gray level of pixels. This method has been used to measure the trabecular thickness of bone samples with different bone volume fractions (BVF). Additionally, its performance was studied considering parameters such as image resolution, object rotation, noise and running time. The algorithm has proven to be very robust, precise and faster.