Conference Paper

Simultaneous correspondence and non-rigid 3D reconstruction of the coronary tree from single X-ray images

DOI: 10.1109/ICCV.2011.6126325 Conference: IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, November 6-13, 2011
Source: DBLP


We present a novel approach to simultaneously reconstruct the D structure of a non-rigid coronary tree and estimate point correspondences between an input X-ray image and a reference 3D shape. At the core of our approach lies an optimization scheme that iteratively fits a generative D model of increasing complexity and guides the matching process. As a result, and in contrast to existing approaches that assume rigidity or quasi-rigidity of the structure, our method is able to retrieve large non-linear deformations even when the input data is corrupted by the presence of noise and partial occlusions. We extensively evaluate our approach under synthetic and real data and demonstrate a remarkable improvement compared to state-of-the-art.

  • Source
    • "The final category involves those methods that simultaneously search for correspondences and estimate the transformation parameters using a Kalman filter-like approach [7], [11], [22], [29], [33], [34]. As soon as a few initial correspondences have been established, the set of remaining potential matches is rapidly reduced, making the search complexity manageable. "
    [Show abstract] [Hide abstract]
    ABSTRACT: We present a new approach for matching sets of branching curvilinear structures that form graphs embedded in ${mathbb {R}}^2$ or ${mathbb {R}}^3$ and may be subject to deformations. Unlike earlier methods, ours does not rely on local appearance similarity nor does require a good initial alignment. Furthermore, it can cope with non-linear deformations, topological differences, and partial graphs. To handle arbitrary non-linear deformations, we use Gaussian process regressions to represent the geometrical mapping relating the two graphs. In the absence of appearance information, we iteratively establish correspondences between points, update the mapping accordingly, and use it to estimate where to find the most likely correspondences that will be used in the next step. To make the computation tractable for large graphs, the set of new potential matches considered at each iteration is not selected at random as with many RANSAC-based algorithms. Instead, we introduce a so-called Active Testing Search strategy that performs a priority search to favor the most likely matches and speed-up the process. We demonstrate the effectiveness of our approach first on synthetic cases and then on angiography data, retinal fundus images, and microscopy image stacks acquired at very different resolutions.
    Full-text · Article · Mar 2015 · IEEE Transactions on Pattern Analysis and Machine Intelligence
  • [Show abstract] [Hide abstract]
    ABSTRACT: Segmentation of coronary arteries in X-Ray angiography is a fundamental tool to evaluate arterial diseases and choose proper coronary treatment. The accurate segmentation of coronary arteries has become an important topic for the registration of different modalities which allows physicians rapid access to different medical imaging information from Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI). In this paper, we propose an accurate fully automatic algorithm based on Graph-cuts for vessel centerline extraction, caliber estimation, and catheter detection. Vesselness, geodesic paths, and a new multi-scale edgeness map are combined to customize the Graph-cuts approach to the segmentation of tubular structures, by means of a global optimization of the Graph-cuts energy function. Moreover, a novel supervised learning methodology that integrates local and contextual information is proposed for automatic catheter detection. We evaluate the method performance on three datasets coming from different imaging systems. The method performs as good as the expert observer w.r.t. centerline detection and caliber estimation. Moreover, the method discriminates between arteries and catheter with an accuracy of 96.5%, sensitivity of 72%, and precision of 97.4%.
    No preview · Article · Sep 2012 · IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society
  • [Show abstract] [Hide abstract]
    ABSTRACT: Simultaneously recovering the camera pose and correspondences between a set of 2D-image and 3D-model points is a difficult problem, especially when the 2D-3D matches cannot be established based on appearance only. The problem becomes even more challenging when input images are acquired with an uncalibrated camera with varying zoom, which yields strong ambiguities between translation and focal length. We present a solution to this problem using only geometrical information. Our approach owes its robustness to an initial stage in which the joint pose and focal length solution space is split into several Gaussian regions. At runtime, each of these regions is explored using an hypothesize-and-test approach, in which the potential number of 2D-3D matches is progressively reduced using informed search through Kalman updates, iteratively refining the pose and focal length parameters. The technique is exhaustive but efficient, significantly improving previous methods in terms of robustness to outliers and noise.
    No preview · Conference Paper · Jan 2013
Show more