Statistical and Physical Micro-Feature-Based Segmentation of Cortical Bone Images Using Artificial Intelligence

Abstract

At the micro scale, dense cortical bone is structurally comprised mainly of Osteon units that contain Haversian canals, lacunae, and concentric lamellae solid matrix. Osteons are separated from each other by cement lines. These microfeatures of cortical bone are typically captured in digital histological images. In this work, we aim to automatically segment these features utilizing optimized pulse coupled neural networks (PCNN). These networks are artificially intelligent (AI) tools that can model neural activity and produce a series of binary pulses (images) representing the segmentations of an image. Two segmentation methods were used: one statistical and another based on the physical attributes of the microfeatures. The first, statistical-based segmentation method, cost functions based on entropy (probability of gray values) considerations are calculated. For the physical-based segmentation method, cost functions based on geometrical attributes associated with microfeatures such as relative size, shape (i.e., circular or elliptical) are used as targets for the fitness function of network optimization. Both of these methods were found to result in good quality segregation of the microfeatures of micro-images of bovine cortical bone.