Segregation of Cortical Bone’s Haversian Systems via Automated Image Segmentation

Abstract

The lamellar or Haversian system is comprised mainly of fundamental units “osteons”. Haversian canals run through the center of the osteons where one or more blood vessels are located. The bone matrix is comprised of concentric lamellae surrounding Haversian canals. Those lamellae are punctuated by holes called lacunae, which are connected to each other through the canaliculi supplying nutrients. Haversian canals, lacunae and canaliculi of the Haversian system constitute the main porosities in cortical bone, thus it is advantageous to segregate those systems in segmented images that will help medical image analysis in accounting for porosities. To the authors’ best knowledge, no work has been published on segregating Haversian systems with its 3 predominant components (Haversian canals, lacunae, and canaliculi) via automated image segmentation of optical microscope images. This paper aims to detect individual osteonal Haversian system via optical microscope image segmentation. Automation is assured via artificial intelligence; specifically neural networks are used to procure an automated image segmentation methodology. Biopsies are taken from cortical bone cut at mid-diaphysis femur from bovine cows (which age is about 2 year-old). Specimens followed a pathological procedure (fixation, decalcification, and staining using H&E staining treatment) in order to get slides ready for optical imaging. Optical images at 20X magnification are captured using SC30 digital microscope camera of BX-41M LED optical Olympus microscope. In order to get the targeted segmented images, utilized was an image segmentation methodology developed previously by the authors. This methodology named “PCNN-PSO-AT” combines pulse coupled neural networks to particle swarm optimization and adaptive thresholding, yielding segmented images quality. Segmentation is occurred based on a geometrical attribute namely orientation used as the fitness function for the PSO. The fitness function is built in such way to maximize the identified number of features (which are the 3 components of the osteonal system) having same orientation. The segmentation methodology is applied on several test images. Results were compared to manually segmented images using suitable quality metrics widely used for image segmentation evaluation namely precision rate, sensitivity, specificity, accuracy and dice. The main goal of segmentation algorithms is to capture as accurate as possible structures of interest, herein Haversian (osteonal) system. High quality segmented images obtained as well as high values of quality metrics (approaching unity) prove the robustness of the segmentation methodology in reaching high fidelity segments of the Haversian system.