A comprehensive study on feature types for osteoporosis classification in dental panoramic radiographs. (May 2020)

• Record Type: Journal Article
• Authors: Alzubaidi, Mohammad A.; Otoom, Mwaffaq
• Is Part Of: Computer methods and programs in biomedicine. Volume 188(2020)
• Publication Date: 2020-05
• Subjects: Osteoporosis detection and classification -- Dental radiograph -- Self organizing map -- Learning vector quantization -- Image processing -- Computer vision; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105301
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Osteoporosis early detection and classification. Feature types in dental radiograph. Self organizing map with learning vector quantization. Abstract: Background and Objective: Osteoporosis is a disease characterized by a decrease in bone density. It is often associated with fractures and severe pain. Previous studies have shown a high correlation between the density of the bone in the hip and in the mandibular bone in the jaw. This suggests that dental radiographs might be useful for detecting osteoporosis. Use of dental radiographs for this purpose would simplify early detection of osteoporosis. However, dental radiographs are not normally examined by radiologists. This paper explores the use of 13 different feature extractors for detection of reduced bone density in dental radiographs. Methods: The computed feature vectors are then processed with a Self-Organizing Map and Learning Vector Quantization as well as Support Vector Machines to produce a set of 26 predictive models. Results: The results show that the models based on Self-Organizing Map and Learning Vector Quantization using Gabor Filter, Edge Orientation Histogram, Haar Wavelet, and Steerable Filter feature extractors outperform the rest of the 22 models in detecting osteoporosis. The proposed Gabor-based algorithm achieved an accuracy of 92.6%, a sensitivity of 97.1%, and a specificity of 86.4%. Conclusions: The oriented edges and textures in the upper and lower jaw regions are useful for distinguishing normal patients from patients with osteoporosis.