Curvature of oriented patterns: 2-D and 3-D estimation from differential geometry

Donias, Marc; Baylou, Pierre; Keskes, N.

doi:10.1109/icip.1998.723464

Cited by 12 publications

(11 citation statements)

References 7 publications

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“…The principal curvatures are normally obtained as the eigenvalues of the Hessian matrix of the intensity function [38]. The estimation of the Hessian of the intensity function involves second-order partial derivatives and so is highly sensitive to noise.…”

Section: Correlation-based Vessel Junction and Nodule Enhancementmentioning

confidence: 99%

Vessel tree reconstruction in thoracic CT scans with application to nodule detection

Agam

Armato

2005

IEEE Trans. Med. Imaging

160

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Vessel tree reconstruction in volumetric data is a necessary prerequisite in various medical imaging applications. Specifically, when considering the application of automated lung nodule detection in thoracic computed tomography (CT) scans, vessel trees can be used to resolve local ambiguities based on global considerations and so improve the performance of nodule detection algorithms. In this study, a novel approach to vessel tree reconstruction and its application to nodule detection in thoracic CT scans was developed by using correlation-based enhancement filters and a fuzzy shape representation of the data. The proposed correlation-based enhancement filters depend on first-order partial derivatives and so are less sensitive to noise compared with Hessian-based filters. Additionally, multiple sets of eigenvalues are used so that a distinction between nodules and vessel junctions becomes possible. The proposed fuzzy shape representation is based on regulated morphological operations that are less sensitive to noise. Consequently, the vessel tree reconstruction algorithm can accommodate vessel bifurcation and discontinuities. A quantitative performance evaluation of the enhancement filters and of the vessel tree reconstruction algorithm was performed. Moreover, the proposed vessel tree reconstruction algorithm reduced the number of false positives generated by an existing nodule detection algorithm by 38%.

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Section: Correlation-based Vessel Junction and Nodule Enhancementmentioning

confidence: 99%

Vessel tree reconstruction in thoracic CT scans with application to nodule detection

Agam

Armato

2005

IEEE Trans. Med. Imaging

160

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“…The local curvature of the smoothed image, denoted as I σ , is then computed in scale space as shown in [20]:…”

Section: Adaptive Regularization Via Measure Of Image Reliabilitymentioning

confidence: 99%

Reliability-Driven, Spatially-Adaptive Regularization for Deformable Registration

Tang

Hamarneh

Abugharbieh

2010

Biomedical Image Registration

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Abstract. We propose a reliability measure that identifies informative image cues useful for registration, and present a novel, data-driven approach to spatially adapt regularization to the local image content via use of the proposed measure. We illustrate the generality of this adaptive regularization approach within a powerful discrete optimization framework and present various ways to construct a spatially varying regularization weight based on the proposed measure. We evaluate our approach within the registration process using synthetic experiments and demonstrate its utility in real applications. As our results demonstrate, our approach yielded higher registration accuracy than non-adaptive approaches and the proposed reliability measure performed robustly even in the presences of noise and intensity inhomogenity.

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“…where I x,σ and I y,σ are the image derivatives along x and y, respectively, at scale σ. Denoting the Hessian matrix of I(x, y; σ) by H σ (x, y), the local image curvature K(x, y; σ) can be calculated as [25,26]:…”

Section: Local Image Curvature Cuementioning

confidence: 99%

Adaptive Regularization for Image Segmentation Using Local Image Curvature Cues

Rao

Abugharbieh

Hamarneh

2010

Computer Vision – ECCV 2010

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Abstract. Image segmentation techniques typically require proper weighting of competing data fidelity and regularization terms. Conventionally, the associated parameters are set through tedious trial and error procedures and kept constant over the image. However, spatially varying structural characteristics, such as object curvature, combined with varying noise and imaging artifacts, significantly complicate the selection process of segmentation parameters. In this work, we propose a novel approach for automating the parameter selection by employing a robust structural cue to prevent excessive regularization of trusted (i.e. low noise) high curvature image regions. Our approach autonomously adapts local regularization weights by combining local measures of image curvature and edge evidence that are gated by a signal reliability measure. We demonstrate the utility and favorable performance of our approach within two major segmentation frameworks, graph cuts and active contours, and present quantitative and qualitative results on a variety of natural and medical images.

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Curvature of oriented patterns: 2-D and 3-D estimation from differential geometry

Cited by 12 publications

References 7 publications

Vessel tree reconstruction in thoracic CT scans with application to nodule detection

Vessel tree reconstruction in thoracic CT scans with application to nodule detection

Reliability-Driven, Spatially-Adaptive Regularization for Deformable Registration

Adaptive Regularization for Image Segmentation Using Local Image Curvature Cues

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