A variational model for thin structure segmentation based on a directional regularization

Merveille, Odyssée; Miraucourt, Olivia; Salmon, Stéphanie; Passat, Nicolas; Talbot, Hugues

doi:10.1109/icip.2016.7533176

Cited by 9 publications

(8 citation statements)

References 22 publications

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“…As a low level operator for the characterization of curvilinear structures, RORPO can be used as prior information in segmentation frameworks in the same way as the Hessian [62]. As future work, we plan on embedding RORPO features in a learning-based framework for segmentation, and we are working on accelerating the computations.…”

Section: Resultsmentioning

confidence: 99%

Curvilinear Structure Analysis by Ranking the Orientation Responses of Path Operators

Merveille

Talbot

Najman

et al. 2018

IEEE Trans. Pattern Anal. Mach. Intell.

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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.

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Section: Resultsmentioning

confidence: 99%

Curvilinear Structure Analysis by Ranking the Orientation Responses of Path Operators

Merveille

Talbot

Najman

et al. 2018

IEEE Trans. Pattern Anal. Mach. Intell.

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“…This corresponds to the relaxation of the isotropic hypothesis often introduced when formulating the problem as a MRF. As an alternative to the weighted Total Variation (TV) [19], the authors of [17] introduce a directional TV approach, based on a "vesselness feature" which aims to detect thin structures. Finally, since we believe that structure orientation estimation is a key aspect of anisotropic regularization approaches, let us mention different ways to estimate it: tensor voting approaches [5,16], vesselness operators like RORPO [18], the Frangi vesselness [8], or structure aware regression filters [23] to perform structure-dependent image smoothing.…”

Section: Introductionmentioning

confidence: 99%

Thin Structures Segmentation Using Anisotropic Neighborhoods

Ribal¹,

Lermé²,

Hégarat‐Mascle³

2020

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Bayesian and probabilistic models are widely used in image processing to handle noise due to various alteration phenomena. To benefit from the spatial information in a tractable way, Markov Random Fields (MRF) are often assumed with isotropic neighborhoods, that is however at the detriment of the preservation of thin structures. In this study, we aim at relaxing this assumption on stationarity and isotropy of the neighborhood shape in order to get a prior probability term that is relevant not only within the homogeneous areas but also close to object borders and within thin structures. To tackle the issue of neighborhood shape estimation, we propose to use tensor voting, that allows for the estimation of structure direction and saliency at various scales. We propose three main ways to derive anisotropic neighborhoods, namely shape-based, target-based and cardinal-based neighborhood. Then, having defined the neighborhood field, we introduce an energy that will be minimized using graph cuts, and illustrate the benefits of our approach against the use of isotropic neighborhoods in the applicative context of crack detection. First results on such a challenging problem are very encouraging.

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“…This article is an extended and improved version of the conference paper [3]. It is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

$n$ D Variational Restoration of Curvilinear Structures With Prior-Based Directional Regularization

Merveille

Naegel

Talbot

et al. 2019

IEEE Trans. on Image Process.

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Curvilinear structure restoration in image processing procedures is a difficult task, which can be compounded when these structures are thin, i.e. when their smallest dimension is close to the resolution of the sensor. Many recent restoration methods involve considering a local gradient-based regularization term as prior, assuming gradient sparsity. An isotropic gradient operator is typically not suitable for thin curvilinear structures, since gradients are not sparse for these. In this article, we propose a mixed gradient operator that combines a standard gradient in the isotropic image regions, and a directional gradient in the regions where specific orientations are likely. In particular, such information can be provided by curvilinear structure detectors (e.g. RORPO or Frangi filters). Our proposed mixed gradient operator, that can be viewed as a companion tool of such detectors, is proposed in a discrete framework and its formulation / computation holds in any dimension; in other words, it is valid in Z n , n ≥ 1. We show how this mixed gradient can be used to construct image priors that take edge orientation as well as intensity into account, and then involved in various image processing tasks while preserving curvilinear structures. Experiments carried out on 2D, 3D, real and synthetic images illustrate the relevance of the proposed gradient and its use in variational frameworks for both denoising and segmentation tasks.

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A variational model for thin structure segmentation based on a directional regularization

Cited by 9 publications

References 22 publications

Curvilinear Structure Analysis by Ranking the Orientation Responses of Path Operators

Curvilinear Structure Analysis by Ranking the Orientation Responses of Path Operators

Thin Structures Segmentation Using Anisotropic Neighborhoods

$n$ D Variational Restoration of Curvilinear Structures With Prior-Based Directional Regularization

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