Processing of optical images of bone has been a topic of considerable interest in the past and continues to be so. Image processing can be used in medicine in order to improve the image visualization to detect diseases, and to compute properties such as area for abnormal cells. Several studies of bone images have been conducted using several methods including segmentation and image enhancement. The aim of this paper is to generate a standalone automated code for segmenting colored optical microscope images in order to show the microstructure of a cortical bone as a multi-phase (here 4 phases) composite: Lamella (matrix), Haversian canals, osteoblast lamella boundaries (freshly generated lamella lining), and lacunae (containing living cells).

For this purpose, we investigate the use of MATLAB, which contains image-processing toolboxes with many analytical capabilities that have been advertised to be useful for many applications including biological systems. In this work, such capabilities are utilized in image processing of the microstructure of bovine cortical bone, which is generally accepted as proxy for human bone. Two specimens of the cortical regions of a bovine femur bones were imaged using Olympus optical microscope. One of the specimens was treated with the Masson’s trichrome staining treatment and the other with the Hematoxylin and Eosin (H&E) treatment. The images from the microscope were captured using a DP12 camera.

Furthermore, MATLAB results are contrasted against Stream®, a commercially available software package procured along with the Olympus optical microscope. Via color-coding to facilitate the bone microstructure identification, the image analysis results were compared after computing the areas of each of the 4 constituent microstructural phases. Areas of each phase were calculated and comparisons made between the results obtained from the Stream® software and those obtained from MATLAB. The relative error was found to be quite small (<1%), which proves that MATLAB may be an effective software for medical image processing and may be the tool of choice for standalone applications.

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