Range imaging plays an essential role in many fields: 3D modeling, robotics, heritage, agriculture, forestry, reverse engineering. One of the most popular range-measuring technologies is laser scanner due to its several advantages: long range, high precision, real-time measurement capabilities, and no dependence on lighting conditions. However, laser scanners are very costly. Their high cost prevents widespread use in applications. Due to the latest developments in technology, now, low-cost, reliable, faster, and light-weight 1D laser range finders (LRFs) are available. A low-cost 1D LRF with a scanning mechanism, providing the ability of laser beam steering for additional dimensions, enables to capture a depth map. In this work, we present an unsynchronized scanning with a low-cost LRF to decrease scanning period and reduce vibrations caused by stop-scan in synchronized scanning. Moreover, we developed an algorithm for alignment of unsynchronized raw data and proposed range image post-processing framework. The proposed technique enables to have a range imaging system for a fraction of the price of its counterparts. The results prove that the proposed method can fulfill the need for a low-cost laser scanning for range imaging for static environments because the most significant limitation of the method is the scanning period which is about 2 minutes for 55,000 range points (resolution of 250x220 image). In contrast, scanning the same image takes around 4 minutes in synchronized scanning. Once faster, longer range, and narrow beam LRFs are available, the methods proposed in this work can produce better results.
The active contour model has good performance in boundary extraction for medical images; particularly, Gradient Vector Flow (GVF) active contour model shows good performance at concavity convergence and insensitivity to initialization, yet it is susceptible to edge leaking, deep and narrow concavities, and has some issues handling noisy images. This paper proposes a novel external force, called Iterative Weighted Average Diffusion (IWAD), which used in tandem with parametric active contours, provides superior performance in images with high values of concavity. The image gradient is first turned into an edge image, smoothed, and modified with enhanced corner detection, then the IWAD algorithm diffuses the force at a given pixel based on its 3x3 pixel neighborhood. A forgetting factor, φ, is employed to ensure that forces being spread away from the boundary of the image will attenuate. The experimental results show better behavior in high curvature regions, faster convergence, and less edge leaking than GVF when both are compared to expert manual segmentation of the images.
Techniques that provide a non-invasive method for evaluation of intraoperative skin flap perfusion are currently available but underutilized. We hypothesize that intraoperative vascular imaging can be used to reliably assess skin flap perfusion and elucidate areas of future necrosis by means of a standardized critical perfusion threshold. Five animal groups (negative controls, n=4; positive controls, n=5; chemotherapy group, n=5; radiation group, n=5; chemoradiation group, n=5) underwent pre-flap treatments two weeks prior to undergoing random pattern dorsal fasciocutaneous flaps with a length to width ratio of 2:1 (3 x 1.5 cm). Flap perfusion was assessed via laser-assisted indocyanine green dye angiography and compared to standard clinical assessment for predictive accuracy of flap necrosis. For estimating flap-failure, clinical prediction achieved a sensitivity of 79.3% and a specificity of 90.5%. When average flap perfusion was more than three standard deviations below the average flap perfusion for the negative control group at the time of the flap procedure (144.3±17.05 absolute perfusion units), laser-assisted indocyanine green dye angiography achieved a sensitivity of 81.1% and a specificity of 97.3%. When absolute perfusion units were seven standard deviations below the average flap perfusion for the negative control group, specificity of necrosis prediction was 100%. Quantitative absolute perfusion units can improve specificity for intraoperative prediction of viable tissue. Using this strategy, a positive predictive threshold of flap failure can be standardized for clinical use.
Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at di erent points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D di usion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation.
Visual tracking is an important task that has received a lot of attention in recent years. Robust generic tracking tools are
of major interest for applications ranging from surveillance and security to image guided surgery. In these applications,
the objects of interest may be translated and scaled. We present here an algorithm that uses scaled normalized cross-correlation
matching as the likelihood within the particle filtering framework. We do not need color and contour cues in
our algorithm. Experimental results with constant rectangular templates show that the method is reliable for noisy and
cluttered scenarios, and provides accurate and smooth trajectories in cases of target translation and scaling.