Unobtrusive monitoring of vital signs is relevant for both medical (patient monitoring) and non-medical applications (e.g., stress and fatigue monitoring). In this paper, we focus on the use of imaging photoplethysmography (iPPG). High frame rate videos were acquired by using a monochrome camera and an optical band-pass filter ([Formula: see text] nm). To enhance iPPG signal, we investigated the use of independent component analysis (ICA) pre-processing applied to iPPG signal from different regions of the face. Methodology was tested on [Formula: see text] healthy volunteers. Heart rate (HR) and standard time and frequency domain descriptors of heart rate variability (HRV), simultaneously extracted from videos and ECG data, were compared. A mean absolute error (MAE) about 3.812 ms was observed for normal-to-normal intervals with or without ICA pre-processing. Smaller MAE values of frequency domain descriptors were observed when ICA pre-processing was used. The impact of both video frame rate and video signal interval were also analyzed. All the results support the conclusion that proposed ICA pre-processing can effectively improve the HR and HRV assessment from iPPG.

Cardiovascular diseases are the leading cause of mortality worldwide and impose a tremendous burden on socio-economic costs. At present, prevention strategies based on personalized lifestyle interventions are considered the best way to contain the epidemics of these diseases. In this view, availability of innovative technological systems able to assist people in self-monitoring and self-coaching is expected to play a crucial role. Aiming to this, we have developed a multi-sensing device, called Wize Mirror which has the appearance of a conventional mirror. The Wize Mirror allows the users to self-monitor their cardio-metabolic risk also providing personalized lifestyle guidance. The mirror has undergone a validation study in three centers to assess the coherence of mirror measurements with standard clinical tests and to evaluate its acceptability by potential users. In this paper, following a summary of the major functionalities of the mirror, we report on the analysis of data about system acceptability assessed during the validation study. Acceptability was measured by means of the System Usability Scale, which resulted in ”good” usability of the Wize Mirror.

A person's well-being status is reflected by their face through a combination of facial expressions and physical signs. The SEMEOTICONS project translates the semeiotic code of the human face into measurements and computational descriptors that are automatically extracted from images, videos, and three-dimensional scans of the face. SEMEOTICONS developed a multisensory platform in the form of a smart mirror to identify signs related to cardio-metabolic risk. The aim was to enable users to self-monitor their well-being status over time and guide them to improve their lifestyle. Significant scientific and technological challenges have been addressed to build the multisensory mirror, from touchless data acquisition, to real-time processing and integration of multimodal data.

The face reveals the healthy status of an individual, through a combination of physical signs and facial expressions. The project SEMEOTICONS is translating the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, images, and 3D scans of the face. SEMEOTICONS is developing a multisensory platform, in the form of a smart mirror, looking for signs related to cardio-metabolic risk. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. Building the multisensory mirror requires addressing significant scientific and technological challenges, from touch-less data acquisition, to real-time processing and integration of multimodal data.

Facial expressions are visible signs of the affective and psychological state of a person, which is strictly correlated with the pathogenesis of clinically relevant diseases and more in general with individuals' wellbeing. The main idea highlighted in this paper is the exploitation of the facial expression analysis for wellbeing monitoring and self-management. This will occur by an innovative multisensory device that will be able to collect images and signals, extract quantitative features of facial expression related to stress, anxiety and fatigue and map them to computational descriptors of an individual's wellbeing. The latter phase will be based on a virtual individual's model conceived to allow the computation and tracing of the daily evolution of individual's wellness. Personalized advices and coaching messages will support the user in keeping a healthy lifestyle and counteract potentially harmful behaviours. The work is part of the FP7 STREP SEMEOTICONS project whose application field will be the prevention of cardio-metabolic risk, for which healthcare systems are registering an exponential growth of social costs.

Nodule growth as observed in computed tomography (CT) scans acquired at different times is the primary feature to malignancy of indeterminate small lung nodules. In this paper, we propose the estimation of nodule size through a scale-space representation which needs no segmentation and has high intra- and inter-operator reproducibility. Lung nodules usually appear in CT images as blob-like patterns and can be analyzed in the scale-space by Laplacian of Gaussian ( LoG ) kernels. For each nodular pattern the LoG scale-space signature was computed and the related characteristic scale adopted as measurement of nodule size. Both in vitro and in vivo validation of LoG characteristic scale were carried out. In vitro validation was done by 40 nondeformable phantoms and 10 deformable phantoms. A close relationship between the characteristic scale and the equivalent diameter, i.e., the diameter of the sphere having the same volume of nodules, (Pearson correlation coefficient was 0.99) and, for nodules undergoing little deformations (obtained at constant volume), small variability of the characteristic scale was observed. The in vivo validation was performed on low and standard-dose CT scans collected from the ITALUNG screening trial (86 nodules) and from the LIDC public data set (89 solid nodules and 40 part-solid nodules or ground-glass opacities). The Pearson correlation coefficient between characteristic scale and equivalent diameter was 0.83-0.93 for ITALUNG and 0.68-0.83 for LIDC data set. Intra- and inter-operator reproducibility of characteristic scale was excellent: on a set of 40 lung nodules of ITALUNG data, two radiologists produced identical results in repeated measurements. The scan-rescan variability of the characteristic scale was also investigated on 86 two-year-stable solid lung nodules (each one observed, on average, in four CT scans) identified in the ITALUNG screening trial: a coefficient of repeatability of about 0.9 mm was observed. Experimental evidence supports the clinical use of the LoG characteristic scale to measure nodule size in CT imaging.

The paper describes a neural-network-based system for the computer aided detection of lung nodules in chest radiograms. Our approach is based on multiscale processing and artificial neural networks (ANNs). The problem of nodule detection is faced by using a two-stage architecture including: 1) an attention focusing subsystem that processes whole radiographs to locate possible nodular regions ensuring high sensitivity; 2) a validation subsystem that processes regions of interest to evaluate the likelihood of the presence of a nodule, so as to reduce false alarms and increase detection specificity. Biologically inspired filters (both LoG and Gabor kernels) are used to enhance salient image features. ANNs of the feedforward type are employed, which allow an efficient use of a priori knowledge about the shape of nodules, and the background structure. The images from the public JSRT database, including 247 radiograms, were used to build and test the system. We performed a further test by using a second private database with 65 radiograms collected and annotated at the Radiology Department of the University of Florence. Both data sets include nodule and nonnodule radiographs. The use of a public data set along with independent testing with a different image set makes the comparison with other systems easier and allows a deeper understanding of system behavior. Experimental results are described by ROC/FROC analysis. For the JSRT database, we observed that by varying sensitivity from 60 to 75% the number of false alarms per image lies in the range 4-10, while accuracy is in the range 95.7-98.0%. When the second data set was used comparable results were obtained. The observed system performances support the undertaking of system validation in clinical settings.

A computational method is reported which allows the fully automated recovery of the three-dimensional shape of the cardiac left ventricle from a reduced set of apical echo views. Two typically ill-posed problems have been faced: 1) the detection of the left ventricle contours in each view, and 2) the integration of the detected contour points (which form a sparse and partially inconsistent data set) into a single surface representation. The authors' solution to these problems is based on a careful integration of standard computer vision algorithms with neural networks. Boundary detection comprises three steps: edge detection, edge grouping, and edge classification. The first and second steps (which are typical early-vision tasks not involving specific domain-knowledge) have been performed through fast, well-established algorithms of computer vision. The higher level task of left ventricle-edge discrimination, which involves the exploitation of specific knowledge about the left ventricle silhouette, has been performed by feedforward neural networks. Following the most recent results in the field of computer vision, the first step in solving the problem of recovering the ventricle surface has been the adoption of a physically inspired model of it. Basically, the authors have modeled the left ventricle surface as a closed, thin, elastic surface and the data as a set of radial springs acting on it. The recovery process is equivalent to the settling of the surface-plus-springs system into a stable configuration of minimum potential energy. The finite element discretization of this model leads directly to an analog neural-network implementation. The efficiency of such an implementation has been remarkably enhanced through a learning algorithm which embeds specific knowledge about the shape of the left ventricle in the network. Experiments using clinical echographic sequences are described. Four apical views (each with a different rotation of the probe) have been acquired during a heartbeat from a set of seven normal subjects. These images have been utilized to set the various processing modules and test their capabilities.

The coronary tree expert (CORTEX) analyzer, which is a vision system for the description of the bidimensional shape and position of coronary vessels using standard nonsubtracted radiographic images, is described. A bottom-up approach was used to deal with the typical characteristics of medical images, such as structural and nonstructural noise and complexity and variability of biological shapes. On these grounds, grouping criteria were utilized to produce intermediate image representations with an increasing complexity in a hierarchical manner (from edge points to curves, segments, bars, and finally to vessels and their mutual relations). In this way, uncertain, inconsistent, and deficient information was efficiently processed. The evaluation of CORTEX segmentation is also performed according to a signal-detection-theory-like approach.>