A strain energy filter for 3D vessel enhancement with application to pulmonary CT images

Xiao, Changyan; Staring, Marius; Shamonin, Denis P; Reiber, J. H. C.; Stolk, Jan; Stoel, Berend C.

doi:10.1016/j.media.2010.08.003

Cited by 64 publications

(45 citation statements)

References 44 publications

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“…TM nano 12000 (Miltenyi Biotec) was injected into the lateral tail vein and segmentations were performed on the subtraction of preand post-contrast agent scans with a vesselness measure derived from [14]. Our primary segmentation was performed using a Markov Random Field approach which leads to a largely contiguous segmentation.…”

Section: Contrast Agent Exitronmentioning

confidence: 99%

Filling Large Discontinuities in 3D Vascular Networks Using Skeleton- and Intensity-Based Information

Bates

Risser

Irving

et al. 2015

Lecture Notes in Computer Science

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Abstract. Segmentation of vasculature is a common task in many areas of medical imaging, but complex morphology and weak signal often lead to incomplete segmentations. In this paper, we present a new gap filling strategy for 3D vascular networks. The novelty of our approach is to combine both skeleton-and intensity-based information to fill large discontinuities. Our approach also does not make any hypothesis on the network topology, which is particularly important for tumour vasculature due to the chaotic arrangement of vessels within tumours. Synthetic results show that using intensity-based information, in addition to skeleton-based information, can make the detection of large discontinuities more robust. Our strategy is also shown to outperform a classic gap filling strategy on 3D Micro-CT images of preclinical tumour models.

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Section: Contrast Agent Exitronmentioning

confidence: 99%

Filling Large Discontinuities in 3D Vascular Networks Using Skeleton- and Intensity-Based Information

Bates

Risser

Irving

et al. 2015

Lecture Notes in Computer Science

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“…2, a common property of the sheet profiles is that they generally take a convex Gaussian shape, which is much like the cross section of blood vessels in medical images. Motivated by the well-established theoretical analysis in vessel filters [26], [28], we propose to detect the Gaussianlike sheet profiles with an 1-D ridge strength measure. To be simplified, a γ -normalized second-order Gaussian kernel…”

Section: A Multiscale Sheet Likelihood Measurementmentioning

confidence: 99%

An Apparatus and Method for Real-Time Stacked Sheets Counting With Line-Scan Cameras

Chen

Wang

Xiao

2015

IEEE Trans. Instrum. Meas.

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To satisfy the requirement of quality control in printing and packaging industry, a sheet counting apparatus is developed, which adopts a line-scan camera to image the fringes of sheet stack and is able to provide a real-time and noncontact measurement of their quantity. With a brief introduction of the system architecture, our main work focuses on the sheet counting algorithms. The basic principle is to identify each sheet profile from the 1-D image with a robust ridge strength measurement. First, a multiscale bi-Gaussian ridge likelihood measurement and a ridge-valley descriptor are utilized to improve adjacent objects detection by increasing local contrast around sheet fringes. Then, a sheet recognition scheme, which integrates a peak detection algorithm and the ridge region criteria for verification, is proposed to discriminate true sheets from the obtained ridgeness measure. According to experiments and tests in real production lines, our apparatus can reach a very high measuring accuracy for printing papers or cards with a thickness not <0.2 mm.

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“…Some of them also used the eigenvectors obtained from the Hessian matrix [18,8], for intance for guidance of a diffusion framework [23]. In [2] the second derivatives were associated to first derivatives and a Canny filter, while in [40], a strain energy function used a stress tensor computed from the Hessian tensor.…”

Section: Differential Filtersmentioning

confidence: 99%

Tubular Structure Filtering by Ranking Orientation Responses of Path Operators

Merveille

Talbot

Najman

et al. 2014

Computer Vision – ECCV 2014

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International audienceThin objects in 3D volumes, for instance vascular networks in medical imaging or various kinds of fibres in materials science, have been of interest for some time to computer vision. Particularly, tubular objects are everywhere elongated in one principal direction – which varies spatially – and are thin in the other two perpendicular directions. Filters for detecting such structures use for instance an analysis of the three principal directions of the Hessian, which is a local feature. In this article, we present a low-level tubular structure detection filter. This filter relies on paths, which are semi-global features that avoid any blurring effect induced by scale-space convolution. More precisely, our filter is based on recently developed morphological path operators. These require sampling only in a few principal directions, are robust to noise and do not assume feature regularity. We show that by ranking the directional response of this operator, we are further able to efficiently distinguish between blob, thin planar and tubular structures. We validate this approach on several applications, both from a qualitative and a quantitative point of view, demonstrating noise robustness and an efficient response on tubular structures

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A strain energy filter for 3D vessel enhancement with application to pulmonary CT images

Cited by 64 publications

References 44 publications

Filling Large Discontinuities in 3D Vascular Networks Using Skeleton- and Intensity-Based Information

Filling Large Discontinuities in 3D Vascular Networks Using Skeleton- and Intensity-Based Information

An Apparatus and Method for Real-Time Stacked Sheets Counting With Line-Scan Cameras

Tubular Structure Filtering by Ranking Orientation Responses of Path Operators

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