Remote photoplethysmography (rPPG) allows to optically measure vital signs, such as heart rate, without physical contact. Signal quality impacts the reliability of derived measurements, but the effects of influential factors in rPPG are not well understood. This research specifically examines five factors that are hypothesized to be important: camera type, skin tone, age, gender, and body mass index (BMI). We investigated these factors using a purposely collected dataset from a comparatively large and diverse population (n=126). For each participant, two simultaneous video streams were recorded using different quality hardware to allow to study the effect of choice of camera type. Statistical analysis based on two quality metrics (signal-to-noise ratio and mean absolute error in heart rate measurements) shows that the choice of camera type is important. Generalized linear mixed models provide evidence of a compounded effect between low quality camera and young age with respect to both signal quality decrease and an increase in measurement error. Analysis of the models coefficients brings evidences that darker skin tone also appears to reduce signal quality, but the results are statistically inconclusive in what concerns heart rate measurements. We observe no significant effect of gender or BMI on rPPG in this study. We believe that comprehensive understanding of the influential factors in rPPG will lead to more reliable and inclusive technologies.

Curvilinear structure detection filters are crucial building blocks in many medical image processing applications, where they are used to detect important structures, such as blood vessels, airways, and other similar fibrous tissues. Unfortunately, most of these filters are plagued by an implicit single structure direction assumption, which results in a loss of signal around bifurcations. This peculiarity limits the performance of all subsequent processes, such as understanding angiography acquisitions, computing an accurate segmentation or tractography, or automatically classifying image voxels. This paper presents a new 3-D curvilinear structure detection filter based on the analysis of the structure ball, a geometric construction representing second order differences sampled in many directions. The structure ball is defined formally, and its computation on a discreet image is discussed. A contrast invariant diffusion index easing voxel analysis and visualization is also introduced, and different structure ball shape descriptors are proposed. A new curvilinear structure detection filter is defined based on the shape descriptors that best characterize curvilinear structures. The new filter produces a vesselness measure that is robust to the presence of X- and Y-junctions along the structure by going beyond the single direction assumption. At the same time, it stays conceptually simple and deterministic, and allows for an intuitive representation of the structure's principal directions. Sample results are provided for synthetic images and for two medical imaging modalities.

A 2D/3D nonrigid registration method is proposed that brings a 3D centerline model of the coronary arteries into correspondence with bi-plane fluoroscopic angiograms. The registered model is overlaid on top of interventional angiograms to provide surgical assistance during image-guided chronic total occlusion procedures, thereby reducing the uncertainty inherent in 2D interventional images. The proposed methodology is divided into two parts: global structural alignment and local nonrigid registration. In both cases, vessel centerlines are automatically extracted from the 2D fluoroscopic images, and serve as the basis for the alignment and registration algorithms. In the first part, an energy minimization method is used to estimate a global affine transformation that aligns the centerline with the angiograms. The performance of nine general purpose optimizers has been assessed for this problem, and detailed results are presented. In the second part, a fully nonrigid registration method is proposed and used to compensate for any local shape discrepancy. This method is based on a variational framework, and uses a simultaneous matching and reconstruction process to compute a nonrigid registration. With a typical run time of less than 3 s, the algorithms are fast enough for interactive applications. Experiments on five different subjects are presented and show promising results.

A new level-set based active contour method for the segmentation of small blood vessels and other elongated structures is presented. Its main particularity is the presence of a length increasing force in the contour driving equation. The effect of this force is to push the active contour in the direction of thin elongated shapes. Although the proposed force is not stable in general, our experiments show that with few precautions it can successfully be integrated in a practical segmentation scheme and that it helps to segment a longer part of the structures of interest. For the segmentation of blood vessels, this may reduce the amount of user interactivity needed: only a small region inside the structure of interest need to be specified.

Segmentation of ancient documents is challenging. In the worst cases, text characters become fragmented as the results of strong degradation processes. New active contour methods allow to handle difficult cases in a spatially coherent fashion. However, most of those method use a restrictive, a priori shape information that limit their application. In this work, we propose to address this issue by combining two complementary approaches. First, multi-level classifiers, which take advantage of the stroke width a priori information, allow to locate candidate character pixels. Second, a level set active contour scheme is used to identify the boundary of a character. Tests have been conducted on a set of ancient degraded Hebraic character images. Numerical results are promising.

A novel method for the segmentation of double-sided ancient document images suffering from bleed-through effect is presented. It takes advantage of the level set framework to provide a completely integrated process for the segmentation of the text along with the removal of the bleed-through interfering patterns. This process is driven by three forces: 1) a binarization force based on an adaptive global threshold is used to identify region of low intensity, 2) a reverse diffusion force allows for the separation of interfering patterns from the true text, and 3) a small regularization force favors smooth boundaries. This integrated method achieves high quality results at reasonable computational cost, and can easily host other concepts to enhance its performance. The method is successfully applied to real and synthesized degraded document images. Also, the registration problem of the double-sided document images is addressed by introducing a level set method; the results are promising.