Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.

Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at non-vascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matchedfilter responses, confidence measures and vessel boundary measures. Matched filter responses are derived in scale-space to extract vessels of widely varying widths. A vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile. Vessel boundary measures and associated confidences are computed at potential vessel boundaries. Combined, these responses form a 6-dimensional measurement vector at each pixel. A training technique is used to develop a mapping of this vector to a likelihood ratio that measures the "vesselness" at each pixel. Results comparing this vesselness measure to matched filters alone and to measures based on the Hessian of intensities show substantial improvements both qualitatively and quantitatively. The Hessian can be used in place of the matched filter to obtain similar but less-substantial improvements or to steer the matched filter by preselecting kernel orientations. Finally, the new vesselness likelihood ratio is embedded into a vessel tracing framework, resulting in an e#cient and e#ective vessel centerline extraction algorithm.

Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at non-vascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matched filter responses, confidence measures and vessel boundary measures. Matched filter responses are derived in scale-space to extract vessels of widely varying widths. A vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile. Vessel boundary measures and associated confidences are computed at potential vessel boundaries. Combined, these responses form a 6-dimensional measurement vector at each pixel. A learning technique is applied to map this vector to a likelihood ratio that measures the "vesselness" at each pixel. Results comparing this vesselness measure to matched filters alone and to measures based on the intensity Hessian show substantial improvements both qualitatively and quantitatively. When the Hessian is used in place of the matched filter, similar but less-substantial improvements are obtained. Finally, the new vesselness likelihood ratio is embedded into a vessel tracing framework, resulting in an e#cient and e#ective vessel extraction algorithm.

This paper presents a correspondence-based toolkit for image registration. Written in C++, the toolkit complements the capabilities of the Insight Toolkit (ITK). Major components include features, feature sets, match generators, error scale estimators, robust transformation estimators, and convergence testers, all combined and controlled by several different registration engines. Correspondence-based algorithms which can be implemented using the toolkit extend from ICP to hybrids of intensity-based and feature-based registration. The toolkit is being used both as an education tool and the foundation for developing new algorithms.

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The tools used in many scientific fields for the quantization of three-dimensional objects yield volumetric data sets. These data sets are often stored as scalar fields along a three-dimensional rectilinear grid. Non-rectilinear grids are often resampled to a rectilinear grid to allow efficient processing. Each cubic, face-adjacent cell composed by the grid is

Many image registration algorithms are formulated as optimization problems with a gradient descent based solver, One difficulty with designing and implementing such methods is in the implementation of the gradient computation. This process can be time-consuming and error-prone. In addition some functions do not have gradients that can be expressed in symbolic form. Automatic differentiation is useful for computing gradients of complicated objective functions. It moves the burden of computing gradients from the programmer to the computer. So far, AD has not been exploited for use in image registration. This paper describes a software library the authors have developed to automate the process of computing gradients of registration objective functions. This can alleviate the job of registration designers somewhat and potentially make it easier to design better registration algorithms.

This paper presents an ongoing research on the behavior and performance of LISP with respect to C in the context of scientific numerical computing. Several simple image processing algorithms are used to evaluate the performance of pixel access and arithmetic operations in both languages. We demonstrate that the behavior of equivalent LISP and C code is similar with respect to the choice of data structures and types, and also to external parameters such as hardware optimization. We further demonstrate that properly typed and optimized LISP code runs as fast as the equivalent C code, or even faster in some cases.

journal homepage: www.elsevier.com/locate/media Location registration and recognition (LRR) for serial analysis of nodules

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