Textural Analysis of Mandibular Trabecular Bone Based on Grey Level Co-occurrence Matrix Features in Correlation With Age and Gender of an Egyptian Population Sample: A Cross-sectional Study Using Cone Beam CT

Scientific background

Bone health of the elderly is a major health concern which has gained a lot of attention lately since elderly populations are growing worldwide(Vijayakumar and Büsselberg, 2016). It is expected for Egypt in particular to have 130 million inhabitant by 2050, out of which 30% will be above 50 years of age(Chen et al., 2013; Gheita and Hammam, 2018). Aging is commonly accompanied by musculoskeletal disorders such as osteoporosis and osteoarthritis as a result of disturbances occurring in bone remodeling (Gheno et al., 2012). These disorders greatly increase the risk of bone fractures, such as hip and vertebral fractures, and thus compromising the quality of life of these patients(Gheno et al., 2012). The prevalence of these musculoskeletal disorders is also affected by gender, studies claim that females are more prone to osteoporosis and osteoarthritis than males(on Osteoporosis and Prevention, 2001; Gheno et al., 2012).

Bone mineral density measured by dual x-ray absorptiometry (DXA) has been considered as the gold standard for assessing bone health for a long time(Link and Heilmeier, 2016). However, the national institute of health on its osteoporosis consensus conference pointed out that bone strength depends not only on its density but also on quality(on Osteoporosis and Prevention, 2001). Therefore, complementary diagnostic methods for assessing bone quality are needed to improve the prediction of individual fracture risk(Link and Heilmeier, 2016).

Various attempts has been made by researchers since then to develop a satisfactory method to assess bone quality, trabecular bone score (TBS) is one of the most successful trials. TBS is a texture parameter related to bone microarchitecture that uses conventional DXA images of the lumbar spine to extract the gray-level texture features providing skeletal information that is not captured from the standard BMD measurement. It has been used successfully as an adjunctive to DXA, and further research attempts are made to investigate its use independently to predict fracture risk.(Martineau, Silva and Leslie, 2017; Shevroja et al., 2017) The success of TBS has led the research community to examine the potential use of texture parameters to assess bone microarchitecture in different imaging modalities(Shevroja et al., 2017). Texture is one of the main visual pattern recognition techniques used by humans to identify objects or regions of interest in an image. It is an innate property of each object, containing important information about the structural arrangement of its surface and its relation with surroundings (Haralick, Shanmugam and Dinstein, 1973). Textural analysis is a type of quantitative image assessment based on relationships between pixel intensities. It is gaining a wide range of attention nowadays in the field of biomedical imaging since it can be beneficial in tissue characterization with various diagnostic applications(Gebejes and Huertas, 2013; Summers, 2017).

There are various methods for textural analysis, Grey level co-occurrence matrix (GLCM) is one of the popular and most commonly used for extraction of texture features(Gebejes and Huertas, 2013). It is a second order statistical method of characterizing the texture of an image by considering the frequency of occurring of pixel grey value pairs and their spatial relationship(Haralick, Shanmugam and Dinstein, 1973; Gebejes and Huertas, 2013).