A Trends Analysis of Dental Image Processing

With the recent development of medical imaging equipment, image segmentation techniques for medical diagnosis have become important role as digital image acquisition with good clarity has become possible. In addition, a lot of dental imaging studies have been conducted due to the active segmentation, classification and recognition research using artificial intelligence such as deep learning and CNN (Convolutional Neural Network). In the paper, trends reviews are conducted on dental image processing. For methods using deep learning, AlexNet, GoogLeNet, and other various methods were conducted. For general methods, Otsu's method, O. Nomir's method, Level-Set, Watershed, and other various methods were used. As a result, these methods mostly showed 80% ~ 90% accuracy in the case of dental image segmentation.