Curvilinear Structure Enhancement with the Polygonal Path Image - Application to Guide-Wire Segmentation in X-Ray Fluoroscopy

Computer Vision – ECCV 2014

et al. 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

Section: Non-linear Filtersmentioning

confidence: 98%

Section: Path Operatorsmentioning

confidence: 99%

Tubular Structure Filtering by Ranking Orientation Responses of Path Operators

Computer Vision – ECCV 2014

et al. 2014

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“…The use of PO and RPO, and a few other path-based paradigms (e.g., polygonal path image [28]), have been proposed in medical imaging and remote sensing [29,30,31], but for 2D applications only. In the sequel, we recall the basics of PO, and we discuss the challenges related to the 2D-3D transition, which constitute the genesis of RORPO.…”

Section: Semi-global (Path-based) Approachesmentioning

confidence: 99%

Ranking Orientation Responses of Path Operators: Motivations, Choices and Algorithmics

Lecture Notes in Computer Science

et al. 2015

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International audienceA new morphological operator, namely RORPO (Ranking Orientation Responses of Path Operators), was recently introduced as a semi-global, morphological alternative to the local, Hessian-based operators for thin structure filtering in 3D images. In this context, a previous study has already provided experimental proof of its relevance by comparison to such differential operators. In this article, we present a methodological study of RORPO, which completes the presentation of this new morphological filter. In particular, we expose the motivations of RORPO with respect to previous morphological strategies; we present algo-rithmic developments of this filter and the underlying robust path operator; and we discuss computational issues related to parametricity and time efficiency. We conclude this study by a discussion on the methodological and applicative potentiality of RORPO in various fields of image processing and analysis

“…A curvilinear object detector was proposed in [48] using geodesic voting, similar to path density. Polygonal path images [49] extended this idea allowing for better regularization and fewer artefacts. A recent formal discussion on optimal global paths was also proposed in [50].…”

Section: Path-based Approachesmentioning

confidence: 99%

Curvilinear Structure Analysis by Ranking the Orientation Responses of Path Operators

IEEE Trans. Pattern Anal. Mach. Intell.

et al. 2018

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The analysis of thin curvilinear objects in 3D images is a complex and challenging task. In this article, we introduce a new, non-linear operator, called RORPO (Ranking the Orientation Responses of Path Operators). Inspired by the multidirectional paradigm currently used in linear filtering for thin structure analysis, RORPO is built upon the notion of path operator from mathematical morphology. This operator, unlike most operators commonly used for 3D curvilinear structure analysis, is discrete, non-linear and non-local. From this new operator, two main curvilinear structure characteristics can be estimated: an intensity feature, that can be assimilated to a quantitative measure of curvilinearity; and a directional feature, providing a quantitative measure of the structure's orientation. We provide a full description of the structural and algorithmic details for computing these two features from RORPO, and we discuss computational issues. We experimentally assess RORPO by comparison with three of the most popular curvilinear structure analysis filters, namely Frangi Vesselness, Optimally Oriented Flux, and Hybrid Diffusion with Continuous Switch. In particular, we show that our method provides up to 8 percent more true positive and 50 percent less false positives than the next best method, on synthetic and real 3D images.