Segmentation of Dentin and Enamel from Panoramic Dental Radiographic Image (OPG) to Detect Tooth Wear

The healthcare domain is a very important research field with rapid technological advancement. In this study specialized field of oral health care is considered, i.e. Dentistry which is measured as a subdivision of medicine dealing with anatomy, development, and diseases of the teeth. In dentistry, dental panoramic radiography (DPR) images have currently captivated growing attention in the diagnosis process due to their correct endorsement of the clinical findings. Conventionally, diagnosis is done with the help of dental radiographs and clinical examination of patients, which is done by a dentist manually as per available infrastructure and knowledge. This abundant approaches influence researchers to use and develop new machine learning techniques, image processing techniques to understand dental radiographs. To understand radiographs through an automatic process and to speed up diagnosis process segmentation and enhancement of an image plays very significant role at initial phase of processing. Segmentation of radiograph is important to separate the different tooth anatomy part but which processing an image this is a major problem due to variation in size, shape, and arrangement of teeth, which will vary from one person to another. The main motive of this work is to apply different image enhancement and segmentation techniques on panoramic (OPG) x-ray through which isolation of dentin and enamel can be done. It is an essential and primary step for finding tooth wear index and determining tooth structure loss. This paper also deliberates the use of several image enhancement and segmentation techniques which are applied on panoramic (OPG) radiograph and its results are evaluated to check the performance efficiency, feasibility of available techniques with stated problem statement.