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Panoramic X-ray images are the major source used in field of dental image segmentation. However, such images suffers from the disturbances like low contrast, presence of jaw bones, nose bones, spinal bone, and artifacts. Thus, to observe these images manually is a tedious task, requires expertise of dentist and is time consuming. Hence, there is need to develop an automated tool for teeth segmentation. Recently, few deep models have been developed for dental image segmentation. But, such models possess large number of training parameters, thus making the segmentation a very complex task. Also, these models are based only on conventional CNN and lacks in exploiting multimodal CNN features for dental image segmentation. Thus, to address these issues, a novel encoder-decoder model based on multimodal-feature extraction for automatic segmentation of teeth area is proposed. The encoder has three different CNN based architectures: conventional CNN, atrous-CNN, and separable CNN to encode rich contextual information. Whereas decoder contains a single stream of deconvolutional layers for segmentation. The proposed model is tested on 1500 panoramic X-ray images and uses very less parameters when compared to state-of-the-art methods. Besides this, the precision and recall are 95.01% and 94.06%, which out performs the state-of-the art methods. [ABSTRACT FROM AUTHOR]

Due to the damage to biometric properties in the event of natural disasters, like fire or earthquakes, it is very difficult to identify human remains. As teeth are more durable than other biometric properties, identifying information obtained from them is much more reliable. Therefore, in cases where alternative biometric properties cannot be obtained or used, information taken from teeth may be used to identify a person's remains. In recent years, many studies have shown how the identification process, previously performed manually by a forensic dental specialist, can be made faster and more reliable with the assistance of computers and technology. In these studies, the x-ray image is subdivided into meaningful parts, including jaws and teeth, and dental properties are extracted and matched. In order to extract the features accurately and ensure better matching, it is important to segment images properly. In this study, (i) lower and upper jaw and (ii) tooth separation was performed to segment panoramic dental x-ray images to assist in identifying human remains. To separate the jaws, a novel meta-heuristic optimization-based model is proposed. To separate teeth, a user-assisted, semi-automatic approach is presented. The proposed methods have been performed with a computer program. The results of the implementation of these methods of jaw and tooth separation in panoramic tooth images are encouraging. [ABSTRACT FROM AUTHOR]

The craniological collection at the Human Anatomy Museum of the University of Turin consists of 1090 skulls and 64 postcranial skeletons prepared mostly during the second half of the nineteenth century. The collection presents individuals of both sexes and of different age groups and includes 712 skulls of known age and sex and 378 of which only the sex is known. Most individuals are associated with a documentation that includes sex, age-at-death, dates of birth and a death certificate. The collection comes from several regions of Italy, between 1880 and 1915, received by the former Anatomical Institute of the University of Turin from city's prisons and hospitals. The whole craniological collection of known age was subjected to panoramic radiographs. The craniological collection combined with the panoramic digital X-ray images represents an important contribution in anthropology and forensic odontology, as there is now no craniological collection available in the world available from a radiological perspective, for investigating dental age assessment and sex dimorphism using radiographs as well as other research and teaching potentials. • Identified craniological collections are paramount for research purposes in forensic anthropology and odontology. • The craniological collection consists of 712 skulls of known age and sex, aged from 0 to 104 years. • The panoramic images of the craniological collection represents a unique resource for investigating dental age assessment and sex dimorphism. [ABSTRACT FROM AUTHOR]