This paper presents an ingestion procedure into an interoperable repository called ALPACS (Access to Local Picture Archiving and Communication Systems). ALPACS serves clinical and hospital users who can access the repository data through an Artificial Intelligence (AI) application called PROXIMITY 1.0. This paper shows the automated procedure for data ingestion from the medical imaging provider into the ALPACS repository. The data ingestion procedure was successfully applied from the data provider (Hospital Clínico de la Universidad de Chile, HCUCH) by applying a pseudo-anonymization algorithm at the source and respecting the privacy of sensitive patient data. The transfer is done using international communication standards for health systems, allowing the replication of the procedure for other medical imaging provider institutions.

This paper describes a novel method to acquire depth images using a pair of ToF (Time-of-Flight) cameras. As opposed to approaches that filter, calibrate or do 3D reconstructions posterior to the image acquisition, we combine the measurements of the two cameras within a modified acquisition procedure. The new proposed stereo-ToF acquisition is composed of three stages during which we actively modify the infrared lighting of the scene: first, the two cameras emit an infrared signal one after the other (stages 1 and 2), and then, simultaneously (stage 3). Assuming the scene is static during the three stages, we gather the depth measurements obtained with both cameras and define a cost function to optimize the two depth images. A qualitative and quantitative evaluation of the performance of the proposed stereo-ToF acquisition is provided both for simulated and real ToF cameras. In both cases, the stereo-ToF acquisition produces more accurate depth measurements. Moreover, an extension to the multi-view ToF case and a detailed study on the interference specifications of the system are included.

This paper describes a novel method to acquire depth images using a pair of ToF (Time of Flight) cameras. As opposed to approaches that filter, calibrate or do 3D reconstructions posterior to the image acquisition, we propose to combine the measurements of the two cameras at the acquisition level. To do so, we define a three-stages procedure, during which we actively modify the infrared lighting of the scene: first, the two cameras emit an infrared signal one after the other (stages 1 and 2), and then, simultaneously (stage 3). Assuming the scene is static during the three stages, we gather the depth measurements obtained with both cameras and define a cost function to optimize the two depth images. A quantitative evaluation of the performance of the proposed method for different objects and stereo configurations is provided based on a simulation of the ToF cameras. Results on real images are also presented. Both in simulation and real images the stereo-ToF acquisition produces more accurate depth measurements.

This paper describes an extension to the Monocular Simultaneous Localization and Mapping (MonoSLAM) method that relies on the images provided by a combined high resolution Time of Flight (HR-ToF) sensor. In its standard formulation MonoSLAM estimates the depth of each tracked feature as the camera moves. This depth estimation depends both on the quality of the feature tracking and the previous camera position estimates. Additionally, MonoSLAM requires a set of known features to initialize the scale of the map and the world coordinate system. We propose to use the combined high resolution ToF sensor to incorporate depth measures into the MonoSLAM framework while keeping the accuracy of the feature detection. In practice, we use a ToF (Time of Flight) and a high-resolution (HR) camera in a calibrated and synchronized set-up and modify the measurement model and observation updates of MonoSLAM. The proposed method does not require known features to initialize a map. Experiments show first, that the depth measurements in our method improve the results of camera localization when compared to the MonoSLAM approach using HR images alone; and second, that HR images are required for reliable tracking.

International audience