Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called Dual-Bootstrap Iterative Closest Point (Dual-Bootstrap ICP). The approach is to start from one or more initial, low-order estimates that are only accurate in small image regions, called bootstrap regions. In each bootstrap region, the algorithm iteratively: 1) refines the transformation estimate using constraints only from within the bootstrap region; 2) expands the bootstrap region; and 3) tests to see if a higher order transformation model can be used, stopping when the region expands to cover the overlap between images. Steps 1): and 3), the bootstrap steps, are governed by the covariance matrix of the estimated transformation. Estimation refinement [Step 2)] uses a novel robust version of the ICP algorithm. In registering retinal image pairs, Dual-Bootstrap ICP is initialized by automatically matching individual vascular landmarks, and it aligns images based on detected blood vessel centerlines. The resulting quadratic transformations are accurate to less than a pixel. On tests involving approximately 6000 image pairs, it successfully registered 99.5% of the pairs containing at least one common landmark, and 100% of the pairs containing at least one common landmark and at least 35% image overlap.

Abstract-Our goal is an automated 2D-image-pair registration algorithm capable of aligning images taken of a wide variety of natural and man-made scenes as well as many medical images. The algorithm should handle low overlap, substantial orientation and scale differences, large illumination variations, and physical changes in the scene. An important component of this is the ability to automatically reject pairs that have no overlap or have too many differences to be aligned well. We propose a complete algorithm including techniques for initialization, for estimating transformation parameters, and for automatically deciding if an estimate is correct. Keypoints extracted and matched between images are used to generate initial similarity transform estimates, each accurate over a small region. These initial estimates are rank-ordered and tested individually in succession. Each estimate is refined using the Dual-Bootstrap ICP algorithm, driven by matching of multiscale features. A three-part decision criteria, combining measurements of alignment accuracy, stability in the estimate, and consistency in the constraints, determines whether the refined transformation estimate is accepted as correct. Experimental results on a data set of 22 challenging image pairs show that the algorithm effectively aligns 19 of the 22 pairs and rejects 99.8 percent of the misalignments that occur when all possible pairs are tried. The algorithm substantially out-performs algorithms based on keypoint matching alone.

This paper presents a broadly applicable algorithm and a comprehensive open-source software implementation for automated tracing of neuronal structures in 3-D microscopy images. The core 3-D neuron tracing algorithm is based on three-dimensional (3-D) open-curve active Contour (Snake). It is initiated from a set of automatically detected seed points. Its evolution is driven by a combination of deforming forces based on the Gradient Vector Flow (GVF), stretching forces based on estimation of the fiber orientations, and a set of control rules. In this tracing model, bifurcation points are detected implicitly as points where multiple snakes collide. A boundariness measure is employed to allow local radius estimation. A suite of pre-processing algorithms enable the system to accommodate diverse neuronal image datasets by reducing them to a common image format. The above algorithms form the basis for a comprehensive, scalable, and efficient software system developed for confocal or brightfield images. It provides multiple automated tracing modes. The user can optionally interact with the tracing system using multiple view visualization, and exercise full control to ensure a high quality reconstruction. We illustrate the utility of this tracing system by presenting results from a synthetic dataset, a brightfield dataset and two confocal datasets from the DIADEM challenge.

Concentric eyewall formation can be idealized as the interaction of a tropical cyclone core with nearby weaker vorticity of various spatial scales. This paper considers barotropic aspects of concentric eyewall formation from modified Rankine vortices. In this framework, the following parameters are found to be important in concentric eyewall formation: vorticity strength ratio, separation distance, companion vortex size, and core vortex skirt parameter. A vorticity skirt on the core vortex affects the filamentation dynamics in two important ways. First, the vorticity skirt lengthens the filamentation time, and therefore slows moat formation in the region just outside the radius of maximum wind. Second, at large radii, a skirted core vortex induces higher strain rates than a corresponding Rankine vortex and is thus more capable of straining out the vorticity field far from the core. Calculations suggest that concentric structures result from binary interactions when the small vortex is at least 4-6 times as strong as the larger companion vortex. An additional requirement is that the separation distance between the edges of the two vortices be less than 6-7 times the smaller vortex radius. Broad moats form when the initial companion vortex is small, the vorticity skirt outside the radius of maximum wind is small, and the strength ratio is large. In concentric cases, an outer vorticity ring develops when the initial companion vortex is large, the vorticity skirt outside the radius of maximum wind is small, and the strength ratio is not too large. In general, when the companion vortex is 3 times as strong as the core vortex and the separation distance is 4-6 times the radius of the smaller vortex, a core vortex with a vorticity skirt produces concentric structures. In contrast, a Rankine vortex produces elastic interaction in this region of parameter space. Thus, a Rankine vortex of sufficient strength favors the formation of a concentric structure closer to the core vortex, while a skirted vortex of sufficient strength favors the formation of concentric structures farther from the core vortex. This may explain satellite microwave observations that suggest a wide range of radii for concentric eyewalls.

The purposes of this study were to test the effectiveness of laser treatment (pulsed CO₂ and pulsed Nd-YAG) on in vitro acid resistance of human enamel. Thirty enamel surfaces were prepared from 10 extracted permanent premolars (3 surfaces per tooth). Two experimental surfaces on each tooth were irradiated with either CO₂ or Nd-YAG lasers. All specimens were demineralized in 10 ml lactate buffer for 24 or 72 h after laser treatment. After 24-hour acid treatment the mean concentration of calcium that dissolved into the lactate buffer in the CO₂ laser group was significantly less than in the control group, while the dissolved calcium concentration in the Nd-YAG laser group did not differ from the control group (p > 0.05). The erosion depth in the CO₂ laser group was significantly shallower than in the Nd-YAG laser group (p < 0.001). After 72-hour acid treatment, the acid resistances of neither group of laser-treated surfaces differed significantly from the controls. By scanning electron microscopy, the acid-eroded laser-treated enamel surfaces had type I and type II etching patterns, fissures, and disordered rough surfaces compared to control enamel, with a regular type II etching pattern. CO₂ laser-treated tooth enamel was more resistant to acid challenge than was Nd-YAG laser-treated enamel, given the same fluence, but neither type of laser increased acid resistance of subsurface enamel.

A model-based algorithm, termed exclusion region and position refinement (ERPR), is presented for improving the accuracy and repeatability of estimating the locations where vascular structures branch and cross over, in the context of human retinal images. The goal is two fold. First, accurate morphometry of branching and crossover points (landmarks) in neuronal/vascular structure is important to several areas of biology and medicine. Second, these points are valuable as landmarks for image registration, so improved accuracy and repeatability in estimating their locations and signatures leads to more reliable image registration for applications such as change detection and mosaicing. The ERPR algorithm is shown to reduce the median location error from 2.04 pixels down to 1.1 pixels, while improving the median spread (a measure of repeatability) from 2.09 pixels down to 1.05 pixels. Errors in estimating vessel orientations were similarly reduced from 7.2 degrees down to 3.8 degrees. These improvements are especially significant for real-time image registration applications for which computationally expensive refinement approaches such as sum of squared difference (SSD) registration can be avoided.

Motivated by the need for multimodal image registration in ophthalmology, this paper introduces an algorithm which is tailored to jointly align in a common reference space all the images in a complete fluorescein angiogram (FA) sequence, which contains both red-free (RF) and FA images. Our work is inspired by Generalized Dual-Bootstrap Iterative Closest Point (GDB-ICP), which rank-orders Lowe keypoint matches and refines the transformation, going from local and low-order to global and higher-order model, computed from each keypoint match in succession. Albeit GDB-ICP has been shown to be robust in registering images taken under different lighting conditions, the performance is not satisfactory for image pairs with substantial, nonlinear intensity differences. Our algorithm, named Edge-Driven DB-ICP, targeting the least reliable component of GDB-ICP, modifies generation of keypoint matches for initialization by extracting the Lowe keypoints from the gradient magnitude image and enriching the keypoint descriptor with global-shape context using the edge points. Our dataset consists of 60 randomly-selected pathological sequences, each on average having up to two RF and 13 FA images. Edge-Driven DB-ICP successfully registered 92.4% of all pairs, and 81.1% multimodal pairs, whereas GDB-ICP registered 80.1% and 40.1%, respectively. For the joint registration of all images in a sequence, Edge-Driven DB-ICP succeeded in 59 sequences, which is a 23% improvement over GDB-ICP.

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