## 3 Forensic Human Identification through the Analysis of Biomedical Images
### AI-Based Approaches for Comparative Radiography
#### 3.3.2 3D-2D Approaches for Comparative Radiography  
In the manual approach, the comparison methodology requires the acquisition of PM simulated radiographs from a CT trying to simulate the AM radiographs [35; 140; 141], instead of real radiographs. The acquisition of these simulated radiographs is a time-consuming, error-prone, and subjective task. However, there are just a few automatic approaches for the comparison of AM radiographs and PM 3D images [33; 142; 143]. These approaches are based on the use of 3D laser range scanners for the acquisition of 3D surface models of the skeletal structures, clavicles in [33; 143], and patellae in [142]. They follow a procedure where a set of 2D projected images are obtained from these PM 3D surface models through the 3D model rotation. These 2D projections only contain the silhouette of the target skeletal structure. Finally, this set of PM projections is automatically compared to the manually segmented silhouette of the skeletal structure in the AM radiographs using elliptical Fourier analysis descriptors. However, the limitation of these methods lies in the set of predefined 2D projections, and in the assumption that the parameters that modulate the perspective distortions are known. Alternatively, [PERSON] et al. [144] developed an evolutionary image registration method which successfully solved this image comparison problem (see Figure 5). The AM data are clinical radiographies (2D images) of a particular bone that have to be compared against the actual PM bone (a 3D model). The promising results that were achieved led them to recently design a methodology to fully automate the ID process by CR (see Figure 3). This method was tested with frontal sinuses, clavicles and patellae obtaining a superb performance. However, the method showed the following drawbacks: (1) none of the considered projective projections reproduced the perspective distortion of radiographs where the X-ray generator was not perpendicular to the image receptor (e.g., in the Water's projection of radiographs of frontal sinuses [145]); (2) the robustness of the evolutionary algorithm employed, Differential Evolution (DE), especially with clavicles and patellae, that in some runs led to bad superimpositions due to the stochastic nature of DE and the highly multimodal search space tackled; and (3) the large amount of time required to obtain a superimposition with DE (1800 s on average). This large time is motivated by the high computational cost required by each evaluation of this evolutionary computation technique (on average, it takes 0.25 s) uncovering the computationally expensive optimization nature of the CR problem, as well as the high number of evaluations required by the optimizer to converge. They also tackled the problem of segmenting multiple organs (hearts, lungs and clavicles) in chest X-ray images using ConvNets [146]. They proposed several new deep architectures to deal with this complex problem. Their best performing proposal obtained better results than the competitor methods on clavicles with 0.0884, 0.939 and 18.022 for Jaccard Index, Dice Similarity Coefficient, and Hausdorff Distance metrics, respectively. This performance is in line with the ability of humans to accurately delimit the contour of clavicles in X-ray images. The same authors also presented the first system for the automatic segmentation of frontal sinuses in skull radiographs [147] (see Figure 6), as well as the first complete, but preliminary, computer-aided CR-based ID support system using frontal sinuses [148]. This system, that integrates automatic segmentation and registration, is able to rank the candidates in a way that 70% times the true positive ID case is ranked in first or second position, as well as is able to filter out 50% of candidates while always keeping the true positive ID case within the sample. Figure 5: Schematic representation of the automatic comparison of anatomical structures in radiographic materials. This figure uses the frontal sinuses as an example.