Author:
Kumar Anuj,Bhadauria Harvendra Singh,Singh Annapurna
Abstract
In dentistry, practitioners interpret various dental X-ray imaging modalities to identify tooth-related problems, abnormalities, or teeth structure changes. Another aspect of dental imaging is that it can be helpful in the field of biometrics. Human dental image analysis is a challenging and time-consuming process due to the unspecified and uneven structures of various teeth, and hence the manual investigation of dental abnormalities is at par excellence. However, automation in the domain of dental image segmentation and examination is essentially the need of the hour in order to ensure error-free diagnosis and better treatment planning. In this article, we have provided a comprehensive survey of dental image segmentation and analysis by investigating more than 130 research works conducted through various dental imaging modalities, such as various modes of X-ray, CT (Computed Tomography), CBCT (Cone Beam Computed Tomography), etc. Overall state-of-the-art research works have been classified into three major categories, i.e., image processing, machine learning, and deep learning approaches, and their respective advantages and limitations are identified and discussed. The survey presents extensive details of the state-of-the-art methods, including image modalities, pre-processing applied for image enhancement, performance measures, and datasets utilized.
Reference149 articles.
1. Automatic segmentation of mandible in panoramic x-ray;Abdi;Journal of Medical Imaging,2015
2. Dental CT imaging: a look at the jaw;Abrahams;Radiology,2001
3. A new approach to teeth segmentation;Al-sherif,2012
4. GPU-based segmentation of dental x-ray images using active contours without edges;Ali,2015
5. Segmentation of dental x-ray images in medical imaging using neutrosophic orthogonal matrices;Ali;Expert Systems with Applications,2018
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