Model-based quantitation of 3-D magnetic resonance angiographic images

Frangi, Alejandro F.; Niessen, Wiro J.; Hoogeveen, Romhild M.; Walsum, Theo van; Viergever, Max A.

doi:10.1109/42.811279

Cited by 338 publications

(308 citation statements)

References 44 publications

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“…When representing images as gray-level landscapes, such structures manifest themselves as ridges. It is known (15)(16)(17)(18) that ridge-like image structures are well detected by means of second-order differential operators. Specifically, the local principal ridge directions at any point in an image are given by the eigenvectors of the second-derivative matrix computed from the intensity values around that point.…”

Section: Materials and Methods Tracing Techniquementioning

confidence: 99%

Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images

et al. 2004

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Background: For the investigation of the molecular mechanisms involved in neurite outgrowth and differentiation, accurate and reproducible segmentation and quantification of neuronal processes are a prerequisite. To facilitate this task, we developed a semiautomatic neurite tracing technique. This article describes the design and validation of the technique. Methods:The technique was compared to fully manual delineation. Four observers repeatedly traced selected neurites in 20 fluorescence microscopy images of cells in culture, using both methods. Accuracy and reproducibility were determined by comparing the tracings to highresolution reference tracings, using two error measures. Labor intensiveness was measured in numbers of mouse clicks required. The significance of the results was determined by a Student t-test and by analysis of variance. Results: Both methods slightly underestimated the true neurite length, but the differences were not unanimously

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Section: Materials and Methods Tracing Techniquementioning

confidence: 99%

Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images

et al. 2004

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“…Some of the approaches aim at coronary tree analysis [5][6][7], late enhancement detection [8,9], and analysis of time intensity curves [10,11]. These methods use low-level information from the data without incorporating prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Mahapatra

2013

J Digit Imaging

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In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information and large within-patient shape variation of the different parts necessitates the inclusion of prior shape information. But large interpatient shape variability makes it difficult to have a generalized shape model. Therefore, for every dataset the shape prior is chosen as a single image clearly showing the different parts. Prior shape information is obtained from a combination of distance functions and orientation angle histograms of each pixel relative to the prior shape. To account for shape changes, pixels near the boundary are allowed to change their labels by appropriate formulation of the penalty and smoothness costs. Our method consists of two stages. In the first stage, segmentation is performed using only intensity information which is the starting point for the second stage combining intensity and shape information to get the final segmentation. Experimental results on different subsets of 30 real patient datasets show higher segmentation accuracy in using shape information and our method's superior performance over other competing methods.

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“…Let the eigenvalues of ∇ 2 f (x; σ) be λ 1 , λ 2 , λ 3 (λ 1 ≥ λ 2 ≥ λ 3 ) and their corresponding eigenvectors be e 1 , e 2 , e 3 (|e 1 | = |e 2 | = |e 3 | = 1), respectively. e 1 is expected to give the tangential direction of the line and the line filter response is obtained as the combination of λ 2 and λ 3 [9], directional second derivatives orthogonal to e 1 .…”

Section: Extracting a Sequence Of Multiscale Line Filter Responses Onmentioning

confidence: 99%

“…A typical method is to extract medial axes of the structures and then quantitate the width of the contour of cross-section orthogonal to the axis [1], [2], [3]. The diameters of small-diameter structures are known to be overestimated when they are measured based on the cross-sectional contours [3].…”

Section: Introductionmentioning

confidence: 99%

Accurate Quantification of Small-Diameter Tubular Structures in Isotropic CT Volume Data Based on Multiscale Line Filter Responses

Sato

Yamamoto

Tamura

2004

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004

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Abstract.A method fully utilizing multiscale line filter responses is presented to estimate the point spread function (PSF) of a CT scanner and diameters of small tubular structures based on the PSF. The estimation problem is formulated as a least square fitting of a sequence of multiscale responses obtained at each medial axis point to the precomputed multiscale response curve for the ideal line model. The method was validated through phantom experiments and demonstrated to accurately measure small-diameter structures which are significantly overestimated by conventional methods based on the full width half maximum (FWHM) and zero-crossing edge detection.

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Model-based quantitation of 3-D magnetic resonance angiographic images

Cited by 338 publications

References 44 publications

Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images

Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Accurate Quantification of Small-Diameter Tubular Structures in Isotropic CT Volume Data Based on Multiscale Line Filter Responses

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