Denis Laurendeau

An algorithm for efficient metal artifact reductions in permanent seed implants

PURPOSEnIn permanent seed implants, 60 to more than 100 small metal capsules are inserted in the prostate, creating artifacts in x-ray computed tomography (CT) imaging. The goal of this work is to develop an automatic method for metal artifact reduction (MAR) from small objects such as brachytherapy seeds for clinical applications.nnnMETHODSnThe approach for MAR is based on the interpolation of missing projections by directly using raw helical CT data (sinogram). First, an initial image is reconstructed from the raw CT data. Then, the metal objects segmented from the reconstructed image are reprojected back into the sinogram space to produce a metal-only sinogram. The Steger method is used to determine precisely the position and edges of the seed traces in the raw CT data. By combining the use of Steger detection and reprojections, the missing projections are detected and replaced by interpolation of non-missing neighboring projections.nnnRESULTSnIn both phantom experiments and patient studies, the missing projections have been detected successfully and the artifacts caused by metallic objects have been substantially reduced. The performance of the algorithm has been quantified by comparing the uniformity between the uncorrected and the corrected phantom images. The results of the artifact reduction algorithm are indistinguishable from the true background value.nnnCONCLUSIONSnAn efficient algorithm for MAR in seed brachytherapy was developed. The test results obtained using raw helical CT data for both phantom and clinical cases have demonstrated that the proposed MAR method is capable of accurately detecting and correcting artifacts caused by a large number of very small metal objects (seeds) in sinogram space. This should enable a more accurate use of advanced brachytherapy dose calculations, such as Monte Carlo simulations.

Active training and driving-specific feedback improve older drivers' visual search prior to lane changes

BackgroundDriving retraining classes may offer an opportunity to attenuate some effects of aging that may alter driving skills. Unfortunately, there is evidence that classroom programs (driving refresher courses) do not improve the driving performance of older drivers. The aim of the current study was to evaluate if simulator training sessions with video-based feedback can modify visual search behaviors of older drivers while changing lanes in urban driving.MethodsIn order to evaluate the effectiveness of the video-based feedback training, 10 older drivers who received a driving refresher course and feedback about their driving performance were tested with an on-road standardized evaluation before and after participating to a simulator training program (Feedback group). Their results were compared to a Control group (12 older drivers) who received the same refresher course and in-simulator active practice as the Feedback group without receiving driving-specific feedback.ResultsAfter attending the training program, the Control group showed no increase in the frequency of the visual inspection of three regions of interests (rear view and left side mirrors, and blind spot). In contrast, for the Feedback group, combining active training and driving-specific feedbacks increased the frequency of blind spot inspection by 100% (32.3 to 64.9% of verification before changing lanes).ConclusionsThese results suggest that simulator training combined with driving-specific feedbacks helped older drivers to improve their visual inspection strategies, and that in-simulator training transferred positively to on-road driving. In order to be effective, it is claimed that driving programs should include active practice sessions with driving-specific feedbacks. Simulators offer a unique environment for developing such programs adapted to older drivers needs.

Changing Lanes in a Simulator: Effects of Aging on the Control of the Vehicle and Visual Inspection of Mirrors and Blind Spot

Objective: The aim of this study was to examine lane change strategies in active younger and older drivers. Visual inspection of mirrors and the blind spot and the control of the vehicle were documented in a simulator environment. Methods: Younger (n = 10, 21–31 years) and older (n = 11, 65–75 years) active drivers drove through a continuous simulated environment including urban and rural sections. The scenario included events where, to negotiate a secure lane change, the driver needed to look at 3 regions of interest (ROI): (1) the rearview mirror, (2) the left side mirror, and (3) the left blind spot. The lane change maneuvers were necessary to avoid a vehicle parked halfway in the rightmost lane that was partially or completely blocking the lane or for overtaking a slower moving vehicle. Results: Compared with younger drivers, older drivers showed a reduced frequency of visual inspection toward the rearview mirror and the blind spot. Also, though the older drivers showed a constant frequency of visual inspection across the 2 types of driving maneuvers, the younger drivers increased their frequency of inspection when overtaking a slower vehicle. Control of the car was mostly similar for both groups. Conclusion: A better knowledge of the drivers’ visual search strategies when changing lanes could help in identifying suboptimal strategies at-risk of causing crashes and also serves to develop retraining programs.

Evolutionary optimization of low-discrepancy sequences

Low-discrepancy sequences provide a way to generate quasi-random numbers of high dimensionality with a very high level of uniformity. The nearly orthogonal Latin hypercube and the generalized Halton sequence are two popular methods when it comes to generate low-discrepancy sequences. In this article, we propose to use evolutionary algorithms in order to find optimized solutions to the combinatorial problem of configuring generators of these sequences. Experimental results show that the optimized sequence generators behave at least as well as generators from the literature for the Halton sequence and significantly better for the nearly orthogonal Latin hypercube.

An algorithm for efficient metal artifact reductions in permanent seed.

PURPOSEnIn permanent seed implants, 60 to more than 100 small metal capsules are inserted in the prostate, creating artifacts in x-ray computed tomography (CT) imaging. The goal of this work is to develop an automatic method for metal artifact reduction (MAR) from small objects such as brachytherapy seeds for clinical applications.nnnMETHODSnThe approach for MAR is based on the interpolation of missing projections by directly using raw helical CT data (sinogram). First, an initial image is reconstructed from the raw CT data. Then, the metal objects segmented from the reconstructed image are reprojected back into the sinogram space to produce a metal-only sinogram. The Steger method is used to determine precisely the position and edges of the seed traces in the raw CT data. By combining the use of Steger detection and reprojections, the missing projections are detected and replaced by interpolation of non-missing neighboring projections.nnnRESULTSnIn both phantom experiments and patient studies, the missing projections have been detected successfully and the artifacts caused by metallic objects have been substantially reduced. The performance of the algorithm has been quantified by comparing the uniformity between the uncorrected and the corrected phantom images. The results of the artifact reduction algorithm are indistinguishable from the true background value.nnnCONCLUSIONSnAn efficient algorithm for MAR in seed brachytherapy was developed. The test results obtained using raw helical CT data for both phantom and clinical cases have demonstrated that the proposed MAR method is capable of accurately detecting and correcting artifacts caused by a large number of very small metal objects (seeds) in sinogram space. This should enable a more accurate use of advanced brachytherapy dose calculations, such as Monte Carlo simulations.

A Global Registration Method Based on the Vector Field Representation

In this paper, we propose a new approach for point cloud registration based on a volumetric representation called the vector field. A surface classification method is first integrated in the vector field represention to implement rough registration estimation in a pair-wise manner. A pose refinement process is then applied to the rough-estimate. The global registration process is supported entirely by the vector field representation. It does not require a priori information on relative position of views and reduces the computational complexity for searching correspondences. Experiments demonstrate the efficiency of the method on a wide variety of objects.

A computer vision framework for the analysis and interpretation of the cephalo-ocular behavior of drivers

In this paper, we introduce a computer vision system specially designed for the analysis and interpretation of the cephalo-ocular behavior of drivers. The system is composed of both hardware and software components and is described in three steps. The first step is devoted to the description of the driving simulator and the developed software. The second step deals with the identification of the driver’s visual search actions using computer vision. The latter are related to specific driving events such as blind spot checking and rear-view/lateral mirror verification. Based on the simulator’s open module, the third step is concerned with the identification of car/road events (overtaking, crossing an intersection) and the mapping of these events with the driver’s behavior. The proposed system will be used by a kinesiology research group for the evaluation and improvement of driver performances in a safe environment (driving simulator). In addition to the controlled environment, a modified version of the system also deals with real driving contexts (i.e. driving in a real car). Experimental results confirm both the robustness and the effectiveness of the proposed cephalo-ocular analysis framework.

IMAGE — Complex situation understanding: An immersive concept development

This paper presents an immersive Human-centric/built virtual work cell for analyzing complex situations dynamically. This environment is supported by a custom open architecture, is composed of objects of complementary nature reflecting the level of Human understanding. Furthermore, it is controlled by an intuitive 3D bimanual gestural interface using data gloves.

Co-adapting mobile sensor networks to maximize coverage in dynamic environments

With recent advances in mobile computing, swarm robotics has demonstrated its utility in countless situations like recognition, surveillance, and search and rescue. This paper presents a novel approach to optimize the position of a swarm of robots to accomplish sensing tasks based on cooperative co-evolution. Results show that the introduced cooperative method simultaneously finds the right number of sensors while also optimizing their positions in static and dynamic environments.

A Computer Vision System for Analyzing and Interpreting the Cephalo-ocular Behavior of Drivers in a Simulated Driving Context

In this paper we introduce a new computer vision framework for the analysis and interpretation of the cephalo-ocular behavior of drivers. We start by detecting the most important facial features, namely the nose tip and the eyes. For that, we introduce a new algorithm for eyes detection and we call upon the cascade of boosted classifiers technique based on Haar-like features for detecting the nose tip. Once those facial features are well identified, we apply the pyramidal Lucas-Kanade method for tracking purposes. Events resulting from those two approaches are combined in order to identify, analyze and interpret the cephalo-ocular behavior of drivers. Experimental results confirm both the robustness and the effectiveness of the proposed framework.