Dental Radiograph Segmentation and Classification—A Comparative Study of Hu’s Moments and Histogram of Oriented Gradients

摘要

This paper proposes a method to compare two feature descriptors to classify dental X-rays, using Hu’s Moments (HM) and the Histogram of Oriented Gradients (HOG). The dental radiographs are preprocessed, and the shape features of teeth are derived using HM and HOG. Support VectorMachine (SVM) is then used for tooth classification and recognition. Comparison of the results of using the two approaches as feature descriptors revealed that regardless of its orientation, size and position, moment invariant functions are very useful for object classification. The classificationof images into molar and premolar has been done on manually cropped images. This method was validated on periapical radiographs. Results obtained show that using both HM and HOG to classify and recognize teeth shape description accuracy as better than, or at least comparable, to the state-of-the-artapproaches. This work aids to improve the computer-assisted diagnosis and decision in dentistry. The forensic odonatological applications of this approach are wide and of immense benefits in both forensic and biometric identification.