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Variational Curve Skeletons Using Gradient Vector Flow
, 2008
"... Representing a 3D shape by a set of one-dimensional curves that are locally symmetric with respect to its boundary (i.e., curve skeletons) is of importance in several machine intelligence tasks. This paper presents a fast, automatic, and robust variational framework for computing continuous, sub-vox ..."
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Representing a 3D shape by a set of one-dimensional curves that are locally symmetric with respect to its boundary (i.e., curve skeletons) is of importance in several machine intelligence tasks. This paper presents a fast, automatic, and robust variational framework for computing continuous, sub-voxel accurate curve skeletons from volumetric objects. A reference point inside the object is considered a point source that transmits two wave fronts of different energies. The first front (β-front) converts the object into a graph, from which the object salient topological nodes are determined. Curve skeletons are tracked from those nodes along the cost field constructed by the second front (α-front) until the point source is reached. The accuracy and robustness of the proposed work are validated against competing techniques as well as a database of 3D objects. Unlike other state-of-the-art techniques, the proposed framework is highly robust because it avoids locating and classifying skeletal junction nodes, employs a new energy that does not form medial surfaces, and finally extracts curve skeletons that correspond to the most prominent parts of the shape, and are hence less sensitive to noise.
Principal Curves to Extract Vessels in 3D Angiograms
"... Segmentation of blood vessels and extraction of their centerlines in 3D angiography are essential to diagnosis and prognosis of vascular diseases, and advanced image processing and analysis. In this paper, we propose a semiautomatic method to perform those two tasks simultaneously. A user supplies t ..."
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Segmentation of blood vessels and extraction of their centerlines in 3D angiography are essential to diagnosis and prognosis of vascular diseases, and advanced image processing and analysis. In this paper, we propose a semiautomatic method to perform those two tasks simultaneously. A user supplies two end points to the algorithm and a vessel centerline between the two given points is extracted automatically. Local vessel widths are estimated as byproducts. Additional anchor points can be added in between to handle difficult situation. Our method is based upon a polygonal line algorithm. This algorithm is used to find principal curves, nonlinear generalization of principal components, from point clouds. We discuss an application of principal curve to vessel extraction from a theoretical view point. A novel algorithm is then proposed for the application. No data interpolation is needed in the algorithm and centerlines extracted are adaptive to the vasculature complexity on account of their nonparametric representation. We have tested the method on two synthetic data sets and two clinical data sets. Results show that it has high robustness to variation in image resolution, voxel anisotropy and noise. Moreover, centerlines obtained are in subvoxel precision and local widths estimated are accurate under limit of image resolution. 1.
Principal Curves for Lumen Center Extraction and Flow Channel Width Estimation in 3-D Arterial Networks: Theory, Algorithm, and Validation
"... Abstract—We present an energy-minimization-based framework for locating the centerline and estimating the width of tubelike objects from their structural network with a nonparametric model. The nonparametric representation promotes simple modeling of nested branches and-way furcations, i.e., structu ..."
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Abstract—We present an energy-minimization-based framework for locating the centerline and estimating the width of tubelike objects from their structural network with a nonparametric model. The nonparametric representation promotes simple modeling of nested branches and-way furcations, i.e., structures that abound in an arterial network, e.g., a cerebrovascular circulation. Our method is capable of extracting the entire vascular tree from an angiogram in a single execution with a proper initialization. A succinct initial model from the user with arterial network inlets, outlets, and branching points is sufficient for complex vasculature. The novel method is based upon the theory of principal curves. In this paper, theoretical extension to grayscale angiography is discussed, and an algorithm to find an arterial network as principal curves is also described. Quantitative validation on a number of simulated data sets, synthetic volumes of 19 BrainWeb vascular models, and 32 Rotterdam Coronary Artery volumes was conducted. We compared the algorithm to a state-of-the-art method and further tested it on two clinical data sets. Our algorithmic outputs—lumen centers and flow channel widths—are important to various medical and clinical applications, e.g., vasculature segmentation, registration and visualization, virtual angioscopy, and vascular atlas formation and population study. Index Terms—Angiography, arterial networks, blood vessels, centerlines, principal curves. I.
Segmentation of 3D Tubular Tree Structures in Medical Images
, 2010
"... The segmentation of tubular tree structures like vessel systems in volumetric medical images is of vital interest for many medical applications. However, a diverse set of challenging objectives and problems is related to this task in different application domains. In this work, we develop and evalua ..."
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The segmentation of tubular tree structures like vessel systems in volumetric medical images is of vital interest for many medical applications. However, a diverse set of challenging objectives and problems is related to this task in different application domains. In this work, we develop and evaluate methods to address these issues. To accomplish the segmentation of heavily branched structures in a robust manner, we propose a generally applicable three-step approach consisting of: (i) a bottom-up identification of tubular structures followed by (ii) a grouping and linkage of these tubular structures into tree structures that are (iii) used as a prior for the actual segmentation. This approach incorporates additional prior knowledge compared to conventional approaches: the individual tubular structures have to be connected with each other and – from a biological perspective – to be supplied. In this way, we achieve a high robustness regarding the structural correctness of the segmentation results. We develop and investigate novel methods for each of these processing steps addressing the needs of different applications. In particular, we present a novel approach for detection
Automatic Morphological Reconstruction of Neurons from Optical Imaging
"... Abstract — In this paper, we present a computational and experimental framework towards real-time functional imaging of neuronal cells. Our proposed system consists of a number of steps that produce a 3D geometrical model used for functional simulations of the neuron cell. The key steps in our curre ..."
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Abstract — In this paper, we present a computational and experimental framework towards real-time functional imaging of neuronal cells. Our proposed system consists of a number of steps that produce a 3D geometrical model used for functional simulations of the neuron cell. The key steps in our current approach are: i) restoration of the original data by deconvolution, ii) robust frames-based denoising, iii) registration of the data, iv) dendrite detection by learning and predicting generalized 3D tubular models, and v) a novel skeletonization algorithm. Centerline extraction is performed using a morphology-guided deformable model in which we do not assume any particular tubular shape. The challenges of analyzing images from optical imaging include the typically low signal-to-noise ratio and the multiscale nature of the tubular structures under consideration, ranging from hundreds of microns to tens of nanometers. We present extensive results of neuron reconstructions performed on both real image volumes and synthetic data demonstrating the accuracy and robustness of our method. I.
A New Color Coding Scheme for Easy Polyp Visualization in CT-Based Virtual Colonoscopy
"... In this paper, we first introduce three different geometric features including shape index, curvedness and sphericity ratio, for colonic polyp detection. A new color coding scheme is designed to highlight the detected polyps, and help radiologists to distinguish them from other tissues more easily. ..."
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In this paper, we first introduce three different geometric features including shape index, curvedness and sphericity ratio, for colonic polyp detection. A new color coding scheme is designed to highlight the detected polyps, and help radiologists to distinguish them from other tissues more easily. The key idea is to place the detected polyp candidates at the same locations in a newly created polygonal dataset with exactly the same topological and geometrical properties as the triangulated mesh surface of real colon dataset, and assign different colors to the two separated datasets to highlight the polyps. Finally, we validate the proposed polyp detection framework and color coding scheme by computer simulated and real colon datasets. For sixteen synthetic polyps with different shapes and different sizes, the sensitivity is 100%, and false positive is 0. Keywords: Colorectal cancer, Curvature-based geometric features, Virtual colonoscopy, Color coding, Topological and geometrical property
