Paper Title
Study on Quantification of Trabecular Bone Distribution on Periapical Radiography Image

Apical periodontitis is a bacterial infection in the tooth that causes acute or chronic inflammation in the periapical tissue. Clinical diagnosis of pulp and periapical tissues can be performed by assessing the periapical X-ray images and clinical symptoms. In particular, for an infected periapical tissue, the X-ray images feature areas that are significantly darker than that of healthy tissues and symptoms such as knee pain, pain when biting, loose teeth, and redness. Because the judgment of different physicians and the grayscale and contrast of the images at different time points the same periapical X-ray image could be interpreted differently. This study aims to develop a set of algorithms to quantify the trabecular bone distribution for objectively assessing the state of the apical alveolar bone. In this study, our proposed quantitative analysis algorithm for trabecular bone distribution comprises two parts: multiple-scale field-of-view fuzzy-logic foreground capture method (trabecular bone capture) and trabeculae-contour box-counting method (quantification of trabecular bone distribution). Trabeculae are often used as the basis for assessing bone quality and related diseases; therefore, this study chooses trabecular bone as the quantification target. People look at the field of view of different sizes of the image, the brain will be based on the concept of fuzzy logic to determine which part of an image is the foreground. Therefore, the proposed algorithm is developed based on this concept. The foreground, i.e., trabeculae , is determined based on the fuzzy system, and the trabecular bone distribution is quantified by the box-counting method. In addition, this study improves the quantitative stability. This improvement is achieved in the following way. The paired quantification index of the same position in the periapical X-ray images of the same tooth taken at two points in time is used as a sample, and 36 such samples are evaluated. The results show that the accuracy of the algorithm is 88.9%. Our robust binarization method can capture the foreground, which is less susceptible to grayscale extremum, grayscale averaging, and grayscale contrast. This results in a more stable quantitative analysis of the trabecular bone distribution for an objective assessment of bone quality and to assist dentists in clinical diagnosis. In addition, long-term recovery or deterioration of lesions can be observed using this algorithm, thereby saving dentists’ time and medical resources. Keywords- Diseases of periapical tissues; Trabecular bone; Periapical X-ray image; Box-counting method