Tubular Structure Filtering by Ranking Orientation Responses of Path Operators

Abstract

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

Efficient and Robust Path Openings Using the Scale-Invariant Rank Operator

“…1). Further constraints on the ability of paths to "zig-zag" might be beneficial [11,16] [17,18]. The contrast has been inverted (and in the original the values are scaled so that maximum fractional anisotropy corresponds to full scale)…”

“…The "Ranking Orientation Responses of Path Operators" (RORPO) modification [17,18] was incorporated into our implementation of generalized path openings to further reduce the background response, since this modification responds only to long and thin structures. This is accomplished by applying the path opening separately to the different graphs corresponding to different directions (see Fig.…”

From Real MRA to Virtual MRA: Towards an Open-Source Framework

“…In this context, we investigated two kinds of vesselness measures, namely Frangi Vesselness [6], and an alternative morphological feature, namely RORPO 3 [7], proved to present complementary properties in terms of accuracy and precision. In addition, such an approach can be enhanced with directional information in the regularization term.…”

“…Specific software packages have been designed for MRI simulation, including JEM-RIS 7 , which is an advanced MRI simulator software written in C++, open-source and freely modifiable. Natively, Bloch equation solving in JEMRIS is only dedicated to simulate static tissues; indeed, JEMRIS only allows to specify one trajectory for all spins.…”

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

“…From this second derivative-based filter, we can extract two features: (i) a vesselness feature which gives, for each image pixel, the probability to belong to a tubular structure, and (ii) a direction feature that gives, for each image pixel, the direction (as a vector) of the associated tubular structure. Recently, we introduced the RORPO operator [8,9] which is a robust alternative to the Frangi filter, which also provides both vesselness and direction features.…”