HIERARCHICAL CONTOUR MATCHING FOR DENTAL X-RAY RADIOGRAPHS

Introduction

Human identification based on dental features has always played a very important role in forensics. The main goal of dental biometrics is to identify deceased individuals, where the conventional biometric features, i.e., iris, fingerprint, and face may not be applicable . The dental radiographs provide information about the teeth, such as shapes of the crowns and the roots, and dental works such as fillings and bridges. The radiographs acquired after death are called postmortem (PM) radiographs, and the radiographs acquired while the person is alive are called antemortem (AM) radiographs. In dental biometrics, the identification is carried out by analyzing and comparing PM dental records of a decedent against a database of AM records to find best matches. Sometimes the decedent’s teeth are compared to AM written records although the most accurate and reliable method is the comparison of AM and PM radiographs.Dental features survive most PM events that may disrupt or change other body tissues, e.g. bodies of victims of motor vehicle accidents, violent crimes, and work place accidents, whose bodies can be disfigured to such an extent that identification by a family member is neither reliable nor desirable. As a result, dental features are regarded as the best candidates for PM biometric identification; this is due to their survivability and diversity. Although there is a number of effective solutions for biometric identification that are currently available, new approaches and techniques are necessary to overcome some of the limitations of current systems. Currently we are building an automated dental identification system (ADIS) for identifying individuals using their dental X-ray records. The system can be used by law enforcement agencies to locate missing individuals using databases of X-ray dental radiographs.

Radiograph segmentation

The goal of radiograph segmentation is to localize the region of each tooth in a dental X-ray image. Dental radiographs may suffer from poor quality, low contrast and uneven exposure that complicate the task of segmentation. Dental X-ray images have three different regions: soft tissue regions and background with the lowest intensity values, bone regions with average intensity values, and teeth regions with the highest intensity values. In some cases the intensity of the bone areas is close to the intensity of the teeth, which makes it difficult to use a single threshold for segmenting the entire image. In this project used our segmentation technique introduced ,which starts by applying iterative thresholding followed by adaptive thresholding to segment the teeth from both the background and the bone areas. After thresholding, horizontal integral projection and vertical integral projection are applied to separate each individual tooth. The contour pixels for each tooth are then extracted and sampled to represent each tooth by an equal number of contour pixels. Fig. 1 shows the segmentation results on a few bite-wing images.