Identification of deceased individuals based on dental characteristics is receiving increased attention, especially with the large volume of victims encountered in mass disasters. An important problem in automated dental identification is automatic classification of teeth into four classes (molars, premolars, canines, and incisors). An equally important problem is the construction of a dental chart, which is a data structure that guides tooth-to-tooth matching. Dental charts are the key for avoiding illogical comparisons that inefficiently consume the limited computational resources and may mislead decision making. Labeling of the teeth is a challenging task which has received inadequate attention in the literature. We tackle this composite problem using a two-stage approach. The first stage utilizes low computational cost, appearance-based features for assigning an initial class. The second stage applies a string matching technique, based on teeth neighborhood rules, to validate initial teeth-classes and, hence, to assign each tooth a number corresponding to its location in the dental chart. Based on a large test dataset of 507 bitewing and periapical films that contain 2027 teeth, the proposed approach achieves classification accuracy of 87%. Experimental results indicate that the proposed approach works very fast, and achieves high performance compared to other approaches suggested in the literature.

Automating the process of postmortem identification of individuals using dental records is receiving increased attention. Teeth segmentation from dental radiographic films is an essential step for achieving highly automated postmortem identification. In this paper, we offer a mathematical morphology approach to the problem of teeth segmentation. We also propose a grayscale contrast stretching transformation to improve the performance of teeth segmentation. We compare and contrast our approach with other approaches proposed in the literature based on a theoretical and empirical basis. The results show that in addition to its capability of handling bitewing and periapical dental radiographic views, our approach exhibits the lowest failure rate among all approaches studied.

Risk assessment is an essential part in managing software development. Performing risk assessment during the early development phases enhances resource allocation decisions. In order to improve the software development process and the quality of software products, we need to be able to build risk analysis models based on data that can be collected early in the development process. These models will help identify the high-risk components and connectors of the product architecture, so that remedial actions may be taken in order to control and optimize the development process and improve the quality of the product. In this paper, we present a risk assessment methodology which can be used in the early phases of the software life cycle. We use the Unified Modeling Language (UML) and commercial modeling environment Rational Rose Real Time (RoseRT) to obtain UML model statistics. First, for each component and connector in software architecture, a dynamic heuristic risk factor is obtained and severity is assessed based on hazard analysis. Then, a Markov model is constructed to obtain scenarios risk factors. The risk factors of use cases and the overall system risk factor are estimated using the scenarios risk factors. Within our methodology, we also identify critical components and connectors that would require careful analysis, design, implementation, and more testing effort. The risk assessment methodology is applied on a pacemaker case study.