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Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction.

Purpose: In 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., Methods: The 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., Results: In 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., Conclusions: An 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.

This paper proposes a new morphology-based approach for the interslice interpolation of current transformer (CT) and MRI datasets composed of parallel slices. Our approach is object based and accepts as input data binary slices belonging to the same anatomical structure. Such slices may contain one or more regions, since topological changes between two adjacent slices may occur. Our approach handles explicitly interslice topology changes by decomposing a many-to-many correspondence into three fundamental cases: one-to-one, one-to-many, and zero-to-one correspondences. The proposed interpolation process is iterative. One iteration of this process computes a transition sequence between a pair of corresponding input slices, and selects the element located at equal distance from the input slices. This algorithmic design yields a gradual, smooth change of shape between the input slices. Therefore, the main contribution of our approach is its ability to interpolate between two anatomic shapes by creating a smooth, gradual change of shape, and without generating over-smoothed interpolated shapes.

This paper presents a newly developed method to estimate, in magnetic resonance (MR) images, the temperatures reached within the volume of an iceball produced by a cryogenic probe. Building on the direct measurements of the MR signal intensity and its correlation with independent temperature variations at the phase transition from liquid to solid, the thermal information embedded in the images was accessed. The volume and diameter of the growing iceball were estimated from a time series of MR images. Using regressions over the volume in the time and thermal domains, this method predicted the cryogenic temperatures beyond the range of sensitivity of the MR signal itself. We present a validation of this method in samples of gelatin and ex vivo pig liver. Temperature predictions are shown to agree with independent thermosensor readings over a range extending from 20 degrees C down to -65 degrees C, with an average error of less than 6 degrees C.

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. [ABSTRACT FROM AUTHOR]

Traditional approaches for surface reconstruction from range data require that the input data be either range images or unorganized sets of points. With these methods, range data acquired along curvilinear patterns cannot be used for surface reconstruction unless constraints are imposed on the shape of the patterns or on sensor displacement. This paper presents a novel approach for reconstructing a surface from a set of arbitrary, unorganized and intersecting curves. A strategy for updating the reconstructed surface during data acquisition is described as well. Curves are accumulated in a volumetric structure in which a vector field is built and updated. The information that is needed for efficient curve registration is also directly available in the vector field. The proposed modeling approach combines surface reconstruction and curve registration into a unified procedure. The algorithm implementing the approach is of linear complexity with respect to the number of input curves and makes it suitable for interactive modeling. Simulated data based on a set of six curvilinear patterns as well as data acquired with a range sensor are used to illustrate the various steps of the algorithm. [Copyright &y& Elsevier]