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. The methodology proposed combines three separately used methods for image segmentation which are: pulse coupled neural network (PCNN), particle swarm optimization (PSO) and adaptive threshold (AT). Two segmentation attributes were used: one statistical and another based on the physical attributes of the micro-features. The first, statistical-based segmentation method, where 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 micro-features such as relative size (i.e., 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 micro-features of micro-images of bovine cortical bone.