A study of intracortical porosity’s area fractions and aspect ratios using computer vision and pulse-coupled neural networks

Employing computer vision (CV) and optimized pulse-coupled neural networks (PCNN), this work automatically quantifies the geometrical attributes of intracortical bone porosity (namely lacunae and canaliculi (L-C), Haversian canals, and resorption cavities). Fifty pathological slides of cortical bone (× 20 magnification) were prepared from middiaphysis of bovine forelegs collected fresh from butcher. Biopsies were subdivided into sectors encircling arcs (θ of 10°) and radial distances (R) originating from the bone’s geometric center toward posterior regions and spanning 3.3 mm. Microscopically, each pore is classified according to whether it belonged to primary or secondary osteon. Globally, each pore is assigned as being located in anterior or posterior regions. For each pore, area and major/minor axes lengths were determined as raw measures from which derived geometric measures, namely, area fraction (AF) and aspect ratio (AR), were derived. Said measures were plotted versus R (for different angles). Plots of AF and AR trends were found to vary linearly along the radial distance. Area fractions (%) significantly decreased linearly with R (p < 0.01) in the anterior region. In the posterior region, area fraction values are flat versus R. These findings are indicative of maturing osteons at the outer cortex with predominately near circular-shaped pores.