A method for generating 3D thermal models with decoupled acquisition.

BACKGROUND AND OBJECTIVE: Both thermal imaging and 3D scanning offer convenient advantages for medical applications, namely, being contactless, non-invasive and fast. Consequently, many approaches have been proposed to combine both sensing modalities in order to acquire 3D thermal models. The predominant approach is to affix a 3D scanner and a thermal camera in the same support and calibrate them together. While this approach allows straightforward projection of thermal images over the 3D mesh, it requires their simultaneous acquisition. In this work, a method for generation of 3D thermal models that allows combination of separately acquired 3D mesh and thermal images is presented. Among the advantages of this decoupled acquisition are increased modularity of acquisition procedures and reuse of legacy equipment and data. METHODS: The proposed method is based on the projection of thermal images over a 3D mesh. Unlike previous methods, it is considered that the 3D mesh and the thermal images are acquired separately, so camera pose estimation is required to determine the correct spatial positioning from which to project the images. This is done using Structure from Motion, which requires a series of interest points correspondences between the images, for which the SIFT method was used. As thermal images of human skin are predominantly homogeneous, an intensity transformation is proposed to increase the efficacy of interest point detection and make the approach feasible. Before projection, the adequate alignment of the 3D mesh in space is determined using Particle Swarm Optimization. For validation of the method, the design and implementation of a test object is presented. It can be used to validate other methods and can be reproduced with common printed circuit board manufacturing processes. RESULTS: The proposed approach is accurate, with an average displacement error of 1.41  mm (s = 0.74  mm) with the validation test object and 4.58 mm (s = 2.12  mm) with human subjects. CONCLUSIONS: The proposed method is able to combine separately a acquired 3D mesh and thermal images into an accurate 3D thermal model. The results with human subjects suggest that the method can be successfully employed in medical applications.

Generation of 3D thermal models for dentistry applications.

There are a variety of medical imaging modalities available, although each modality focus into different aspects, for example: anatomical, physiological or geometrical information. This paper presents a new imaging modality (3D THERMO-SCAN) that combines anatomical computer tomography (CT) imaging slices, together with 2D infrared thermography images and 3D scanned shaped models of the area under study. Therefore, it is presented the 3D reconstructions involving a case study of a volunteer with bruxism. Some characteristics of bruxism are the hyperactivity of the chewing muscles, which changes the dynamics of microcirculation, also changing the correspondent skin's temperature. The emphasis is to show the corresponding structures, such as jaw/mandibular region that will produce either decrease or increase in temperature, which are related to bruxism and the associated use of an occlusal splint, respectively.

Combining 3D models with 2D infrared images for medical applications.

Infrared images are very useful for providing physiological information, although the representation is two-dimensional. On the other hand, a 3D scanning system is able to generate precise 3D spatial models of the area under study. This paper presents a methodology for combining both imaging modalities into a single representation. The Structure from Motion (SfM) technique is used in order to find the correct infrared camera's positioning and rotations in the space. Then, those 2D infrared images generate a 3D SfM model. Following this stage, the SfM model is replaced by an accurate 3D model from a scanning system, which is wrapped around by the infrared images. The experiments performed with a volunteer's face have shown that the proposed methodology successfully reconstruct a unique 3D surface model, which is able to deliver potential clinical applications.