Estimating the 3D skeletons and transverse areas of coronary arteries from biplane angiograms

Kitamura, K.; Tobis, Jonathan M.; Sklansky, Jack

doi:10.1109/42.7779

Cited by 113 publications

(33 citation statements)

References 27 publications

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“…Filtering and segmentation of vascular structures (generally from MR and CT angiography) has been an active research field since the end of the 80's (see, e.g., [20,31] for pioneering works). These intensive efforts were motivated by the possible use of such segmentation results, e.g., for pathology detection and quantification, or for surgical planning.…”

Section: Filtering and Segmentation Of 3d Angiographic Datamentioning

confidence: 99%

Filtering and segmentation of 3D angiographic data: Advances based on mathematical morphology

Dufour

Tankyevych

Naegel

et al. 2013

Medical Image Analysis

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In the last 20 years, 3D angiographic imaging proved its usefulness in the context of various clinical applications. However, angiographic images are generally difficult to analyse due to their size and the fact that useful information is easily hidden in noise and artifacts. Therefore, there is an ongoing necessity to provide tools facilitating their visualization and analysis, while vessel segmentation from such images remains a challenging task. This article presents new vessel segmentation and filtering techniques, relying on recent advances in mathematical morphology. In particular, methodological results related to variant mathematical morphology and connected filtering are stated, and involved in an angiographic data processing framework. These filtering and segmentation methods are validated on real and synthetic 3D angiographic data.

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Section: Filtering and Segmentation Of 3d Angiographic Datamentioning

confidence: 99%

Filtering and segmentation of 3D angiographic data: Advances based on mathematical morphology

Dufour

Tankyevych

Naegel

et al. 2013

Medical Image Analysis

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“…Similar to previously published algorithms [17][18][19], the present analysis is based on the estimation of object cross-sections from a line-based structure of an initial image followed by smoothing of 3D shape. The present study is based on the object model demonstrated in Fig.…”

Section: D Reconstruction Algorithmmentioning

confidence: 99%

Quantification of diesel injector dribble using 3D reconstruction from x-ray and DBI imaging

Sechenyh

Turner

Sykes

et al. 2017

Proceedings ILASS–Europe 2017. 28th Conference on Liquid Atomization and Spray Systems

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Post-injection dribble is known to lead to incomplete atomisation and combustion due to the release of slow moving, and often surface-bound, liquid fuel after the end of the injection event. This can have a negative effect on engine emissions, performance, and injector durability. To better quantify this phenomenon we present a new image processing approach to quantify the volume and surface area of ligaments produced during the end of injection, for an ECN 'Spray B' 3-hole injector. Circular approximation for cross-sections was used to estimate three-dimensional parameters of droplets and ligaments. The image processing consisted in three stages: edge detection, morphological reconstruction, and 3D reconstruction. For the last stage of 3D reconstruction, smooth surfaces were obtained by computation of the alpha shape which represents a bounding volume enveloping a set of 3D points. The object model was verified by calculation of surface area and volume from 2D images of figures with well-known shapes. We show that the object model fits non-spherical droplets and pseudo-cylindrical ligaments reasonably well. We applied our processing approach to datasets generated by different research groups to decouple the effect of gas temperature and pressure on the fuel dribble process. High-speed X-ray phase-contrast images obtained at room temperature conditions (297 K) at the 7-ID beamline of the Advanced Photon Source at Argonne National Laboratory, together with diffused back-illumination (DBI) images captured at a wide range of temperature conditions (293-900 K) by CMT Motores Térmicos, were analysed and compared quantitatively. KeywordsDiesel injector; dribble; ligament; droplet shape; atomisation. IntroductionThe end-of-injection (EOI) fuel dribble causes a formation of unburned hydrocarbons and decreases the performance of internal combustion engines in a variety of ways. For example, deposits lead to an increase in air pollutant emissions [1, 2], a decrease in quality of injection [2,3,4] and further coking of the nozzle [5]. Understanding of the fuel dribbling process is particularly important for the development of a strategy for optimal use of fuels. However, observing the transient end-of-injection processes is particularly challenging due to the extreme operating conditions and the microscopic scales involved. Consequently, there is a lack of quantitative information on the fuel dribble events and the parameters that affect them. Recently published studies [6][7][8][9][10] demonstrate different aspects of the EOI fuel dribble based on a qualitative and quantitative analysis of experimental images of the injection process. The following important factors affecting the mechanism of the fuel dribble were studied: peak injection velocity [7], needle closing speed [7], in-cylinder pressure [6,7], injection pressure [6], fuel mass expulsion [9], bubble ingestion at the EOI [10], liquid length recession at the EOI [8] and different flow characteristics at the EOI [11]. The present study is dedicated to a qua...

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“…Skeletons can be used in computer-aided diagnostic tools for coarctation and aneurism, by evaluating the transverse areas of any vessel structure, see for example [23].…”

Section: 1mentioning

confidence: 99%

“…Furthermore, many computer-aided diagnostic tools rely on skeletons. For example, skeletons have been used to identify blood vessels stenoses [23]- [25], tracheal stenoses [26], polyps and cancer in colon [27] and left atrium fibrosis [28].…”

Section: Introductionmentioning

confidence: 99%

Topology Preserving Thinning of Cell Complexes

Dłotko

Specogna²

2014

IEEE Trans. on Image Process.

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Abstract. A topology preserving skeleton is a synthetic representation of an object that retains its topology and many of its significant morphological properties. The process of obtaining the skeleton, referred to as skeletonization or thinning, is a very active research area. It plays a central role in reducing the amount of information to be processed during image analysis and visualization, computer-aided diagnosis or by pattern recognition algorithms.This paper introduces a novel topology preserving thinning algorithm which removes simple cells-a generalization of simple points-of a given cell complex. The test for simple cells is based on acyclicity tables automatically produced in advance with homology computations. Using acyclicity tables render the implementation of thinning algorithms straightforward. Moreover, the fact that tables are automatically filled for all possible configurations allows to rigorously prove the generality of the algorithm and to obtain fool-proof implementations. The novel approach enables, for the first time, according to our knowledge, to thin a general unstructured simplicial complex. Acyclicity tables for cubical and simplicial complexes and an open source implementation of the thinning algorithm are provided as additional material to allow their immediate use in the vast number of practical applications arising in medical imaging and beyond.

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Estimating the 3D skeletons and transverse areas of coronary arteries from biplane angiograms

Cited by 113 publications

References 27 publications

Filtering and segmentation of 3D angiographic data: Advances based on mathematical morphology

Filtering and segmentation of 3D angiographic data: Advances based on mathematical morphology

Quantification of diesel injector dribble using 3D reconstruction from x-ray and DBI imaging

Topology Preserving Thinning of Cell Complexes

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