Extracting the Potential Features of Digital Panoramic Radiograph Images by Combining Radio Morphometry Index, Texture Analysis, and Morphological Features

Abstract

Osteoporosis is a type of disease that is not easily detected visually. It contributes to bone fracture and so early diagnosis is particularly important to prevent bone fracture. An integrated approach for extraction of cortical and trabecular bone on the digital panoramic radiograph (DPR) images was proposed to screen osteoporosis. We performed radio morphometry index (RMI), texture analysis, and morphology analysis to extract the features of DPR images. Then, the extracted features were further applied to decision tree technique which lead to obtain potential or significant features about osteoporosis. An automated classifier was developed based on Learning Vector Quantization (LVQ) to differentiate between normal and osteoporotic class. In this study, seven major features playing significant role in the osteoporosis identification. For testing purpose, the accuracy of decision tree technique resulted 96,77% and the accuracy of LVQ was 80%.