A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.Spanish Ministry of Science, Innovation and UniversitiesEuropean Union (EU) PGC2018-101216-B-I00Regional Government of Andalusia under grant EXAISFI P18-FR-4262Instituto de Salud Carlos IIIEuropean Union (EU) DTS18/00136European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship 746592Spanish Ministry of Science, Innovation and Universities-CDTI, Neotec program 2019 EXP-00122609/SNEO-20191236European Union (EU)Xunta de Galicia ED431G 2019/01European Union (EU) RTI2018-095894-B-I0

Morphology of the inner structures of the facial skeleton in Homo neanderthalensis and the case-study of the Neanderthal from Altamura (Bari, Italy)

The PhD project has the aim to provide an accurate anatomical characterization of the facial regions (with a focus on the para-nasal areas) in the fossil human species Homo neanderthalensis, whose peculiar facial morphology is the topic of unresolved hypothesis on adaptation to climate and/or phylogenetic factors. Both can be at the origin of the variability of Neanderthals and can be taken into consideration, more in general, for the human populations from the Middle and Upper Pleistocene of Europe, thus from around 800 to 11 thousand years ago (ka). In this timespan, it can be seen a differential development of a set of cranial features which was resumed by J.J. Hublin and colleagues with the ‘accretion model’. In this scenario, a Neanderthal specimen from Italy, known as the ‘Altamura Man’ and discovered in 1993 in the Lamalunga karstic system in Apulia (southern Italy), represents a crucial subject of study, because its unique state of preservation and its antiquity, comprised between 172 and 130 ka. The nearly complete skeleton is still preserved in situ because of several factors, among which its exceptional completeness and thus has been the subject of a study of virtual paleoanthropology aimed at the reconstruction and observation of facial structures often damaged or completely absent in the fossil record

Recent Advances in Forensic Anthropological Methods and Research

Forensic anthropology, while still relatively in its infancy compared to other forensic science disciplines, adopts a wide array of methods from many disciplines for human skeletal identification in medico-legal and humanitarian contexts. The human skeleton is a dynamic tissue that can withstand the ravages of time given the right environment and may be the only remaining evidence left in a forensic case whether a week or decades old. Improved understanding of the intrinsic and extrinsic factors that modulate skeletal tissues allows researchers and practitioners to improve the accuracy and precision of identification methods ranging from establishing a biological profile such as estimating age-at-death, and population affinity, estimating time-since-death, using isotopes for geolocation of unidentified decedents, radiology for personal identification, histology to assess a live birth, to assessing traumatic injuries and so much more

Visual and Geometric Analysis of Maxillary Sinus Region Variability for Identification of Unknown Decedents

Positive identification of unknown individuals is highly important in the medicolegal field. Comparison of antemortem and postmortem radiographs is a popular and successful method of making a positive identification, but these methods are often extremely limited due to a lack of antemortem records. A positive identification method utilizing a type of radiograph that is more common in the antemortem record would be very useful for forensic anthropologists and other medicolegal professionals and could increase the likelihood of the individual in question being identified. Panoramic dental radiographs are commonly included in the standard dental exam and provide a clear view of the maxillary sinus region. Visual analysis of the maxillary sinus region of panoramic radiographs was performed by creating an online radiographic matching survey using sets of two radiographs from seven individuals and individual radiographs from seven other individuals. A total of 47 undergraduate and graduate students participated in the online survey. The results from this survey were used to calculate percentages correct for different variables and perform one-way ANOVA and chi-square analyses on the data using Statistical Package for the Social Sciences (SPSS). A preliminary geometric morphometrics analysis was also performed on the maxillary sinus outline shape using Shape 1.3. Results from both the visual and geometric analysis of maxillary sinus shape indicate that elements of the maxillary sinus area could be used as a relatively accurate method for positively identifying unknown individuals

Investigation of 3DP technology for fabrication of surgical simulation phantoms

The demand for affordable and realistic phantoms for training, in particular for functional endoscopic sinus surgery (FESS), has continuously increased in recent years. Conventional training methods, such as current physical models, virtual simulators and cadavers may have restrictions, including fidelity, accessibility, cost and ethics. In this investigation, the potential of three-dimensional printing for the manufacture of biologically representative simulation materials for surgery training phantoms has been investigated. A characterisation of sinus anatomical elements was performed through CT and micro-CT scanning of a cadaveric sinus portion. In particular, the relevant constituent tissues of each sinus region have been determined. Secondly, feedback force values experienced during surgical cutting have been quantified with an actual surgical instrument, specifically modified for this purpose. Force values from multiple post-mortem subjects and different areas of the paranasal sinuses have been gathered and used as a benchmark for the optimisation of 3D-printing materials. The research has explored the wide range of properties achievable in 3DP through post-processing methods and variation of printing parameters. For this latter element, a machine-vision system has been developed to monitor the 3DP in real time. The combination of different infiltrants allowed the reproduction of force values comparable to those registered from cadaveric human tissue. The internal characteristics of 3D printed samples were shown to influence their fracture behaviour under resection. Realistic appearance under endoscopic conditions has also been confirmed. The utilisation of some of the research has also been demonstrated in another medical (non-surgical) training application. This investigation highlights a number of capabilities, and also limitations, of 3DP for the manufacturing of representative materials for application in surgical training phantoms

Segmentation and Grading of Sinus Images

This report discusses the research done and basic understanding of the proposed topic, which is Segementation and Grading of Sinus Images. The objective of the project is to experiment and explore the usage of wavelets to improve the flaws of the existing techniques. In this project, a trajectory-learning algorithm using contourlets is proposed to enhance the CT sinus image without sacrificing accuracy. The challenge in this project is to find the most favorable contourlets that will result in high accuracy

End-to-End Deep Diagnosis of X-ray Images

In this work, we present an end-to-end deep learning framework for X-ray image diagnosis. As the first step, our system determines whether a submitted image is an X-ray or not. After it classifies the type of the X-ray, it runs the dedicated abnormality classification network. In this work, we only focus on the chest X-rays for abnormality classification. However, the system can be extended to other X-ray types easily. Our deep learning classifiers are based on DenseNet-121 architecture. The test set accuracy obtained for 'X-ray or Not', 'X-ray Type Classification', and 'Chest Abnormality Classification' tasks are 0.987, 0.976, and 0.947, respectively, resulting into an end-to-end accuracy of 0.91. For achieving better results than the state-of-the-art in the 'Chest Abnormality Classification', we utilize the new RAdam optimizer. We also use Gradient-weighted Class Activation Mapping for visual explanation of the results. Our results show the feasibility of a generalized online projectional radiography diagnosis system.Comment: 4 pages, 5 figure

Tissue thickness measurement tool for craniofacial reconstruction

Craniofacial Reconstruction is a method of recreating the appearance of the face on the skull of a deceased individual for identification purposes. Older clay methods of reconstruction are inaccurate, time consuming and inflexible. The tremendous increase in the processing power of the computers and rapid strides in visualization can be used to perform the reconstruction, saving time and providing greater accuracy and flexibility, without the necessity for a skillful modeler.;This thesis introduces our approach to computerized 3D craniofacial reconstruction. Three phases have been identified. The first phase of the project is to generate a facial tissue thickness database. In the second phase this database along with a 3D facial components database is to be used to generate a generic facial mask which is draped over the skull to recreate the facial appearance. This face is to be identified from a database of images in the third phase.;Tissue thickness measurements are necessary to generate the facial model over the skull. The thesis emphasis is on the first phase of the project. An automated facial tissue thickness measurement tool (TTMT) has been developed to populate this database