Multiple model estimation for the detection of curvilinear segments in medical X-ray images using sparse-plus-dense-RANSAC

In this paper, we build on the RANSAC method to detect multiple instances of objects in an image, where the objects are modeled as curvilinear segments with distinct endpoints. Our approach differs from previously presented work in that it incorporates soft constraints, based on a dense image representation, that guide the estimation process in every step. This enables (1) better correspondence with image content, (2) explicit endpoint detection and (3) a reduction in the number of iterations required for accurate estimation. In the case of curvilinear objects examined in this paper, these constraints are formulated as binary image labels, where the estimation proved to be robust to mislabeling, e.g. in case of intersections. Results for both synthetic and real data from medical X-ray images show the improvement from incorporating soft image-based constraints

Towards a full-reference, information-theoretic quality assessment method for X-ray images

This work aims at defining an information-theoretic quality assessment technique for cardiovascular X-ray images, using a full-reference scheme (relying on averaging a sequence to obtain a noiseless reference). With the growth of advanced signal processing in medical imaging, such an approach will enable objective comparisons of the quality of processed images. A concept for describing the quality of an image is to express it in terms of its information capacity. Shannon has derived this capacity for noisy channel coding. However, for X-ray images, the noise is signal-dependent and non-additive, so that Shannon's theorem is not directly applicable. To overcome this complication, we exploit the fact that any invertible mapping on a signal does not change its information content. We show that it is possible to transform the images in such a way that the Shannon theorem can be applied. A general method for calculating such a transformation is used, given a known relation between signal mean and noise standard deviation. After making the noise signal-independent, it is possible to assess the information content of an image and to calculate an overall quality metric (e.g. information capacity) which includes the effects of sharpness, contrast and noise. We have applied this method on phantom images under different acquisition conditions and computed the information capacity for those images. We aim to show that the results of this assessment are consistent with variations in noise, contrast and sharpness, introduced by system settings and image processing

Cardiac left atrium CT image segmentation for ablation guidance

Catheter ablation is an increasingly important curative procedure for atrial fibrillation. Knowledge of the local wall thickness is essential to determine the proper ablation energy. This paper presents the first semi-automatic atrial wall thickness measurement method for ablation guidance. It includes both endocardial and epicardial atrial wall segmentation on CT image data. Segmentation is based on active contours, Otsu's multiple threshold method and hysteresis thresholding. Segmentation results were compared to contours manually drawn by two experts, using repeated measures analysis of variance. The root mean square differences between the semi-automatic and the manually drawn contours were comparable to intra-observer variation (endocardium: p = 0.23, epicardium: p = 0.18). Mean wall thickness difference is significant between one of the experts on one side, and the presented method and the other expert on the other side (

Multiple model estimation for the detection of curvilinear segments in medical X-ray images using sparse-plus-dense-RANSAC

In this paper, we build on the RANSAC method to detect multiple instances of objects in an image, where the objects are modeled as curvilinear segments with distinct endpoints. Our approach differs from previously presented work in that it incorporates soft constraints, based on a dense image representation, that guide the estimation process in every step. This enables (1) better correspondence with image content, (2) explicit endpoint detection and (3) a reduction in the number of iterations required for accurate estimation. In the case of curvilinear objects examined in this paper, these constraints are formulated as binary image labels, where the estimation proved to be robust to mislabeling, e.g. in case of intersections. Results for both synthetic and real data from medical X-ray images show the improvement from incorporating soft image-based constraints

Feature-based depth estimation for monoplane X-ray imaging with limited C-arm motion

During interventional procedures, surgical guidance is provided through live X-ray imaging. Depth cues from 2D imaging can enhance the insight to the true position of important structures. In this paper, we examine a novel application where a small motion of the C- arm is used to create disparity between views for defining a sparse depth map of generic interest points. Feature points are detected and then matched across multiple views, and the depth of the points is estimated using multi-view geometry. Specific adaptations for our case are (1) the matching using geometric constraints for locally dense searching and (2) outlier rejection for robust feature tracks in multiple views. Evaluation using phantom images gave an accuracy in the mm-range for up to 20 views (or _ 300), and a sufficiently rich set of robustly tracked features. This creates a valid option for depth estimation of static points of clinical interest

Surgical needle reconstruction using small-angle multi-view X-ray

In biopsies, drainages, vertebroplasty, and other needle-based procedures, insight on the 3D position of a needle is crucial for correct navigation by the clinician. In this paper, we present a method for the reconstruction of surgical needles using multi-view X-ray imaging with a small motion of the C-arm. It is required that the extent of the motion is limited (smaller than 30 degrees) to allow use of this method during an intervention. This small motion provides sufficient multi-view information, which is used in combination with a needle model for the 3D reconstruction of the needle. To this end, we describe a system comprising the steps of (a) needle detection in a novel, RANSAC-based framework, (b) tracking of needles in subsequent views using geometric constraints and (c) needle reconstruction. Results are presented in comparison to a volume reconstruction using a full rotation of the C-arm (about 207 degrees), showing good accuracy of the proposed method

3D catheter reconstruction using nonrigid structure-from-mortion and a robotics model

Surgical guidance during minimally invasive intervention could be greatly enhanced if the 3D location and orientation of instruments, especially catheters, is available. In this paper, we present a new method for the 3D reconstruction of deforming curvilinear objects such as catheters, using the framework of Non-Rigid Structurefrom- Motion (NRSfM). We combine NRSfM with a kinematics model from the field of Robotics, which provides a low-dimensional parametrization of the object deformation. This is used in the context of an X-ray imaging system where multiple views are acquired with a small view separation. We show that using such a kinematics model, a non-linear optimization scheme succeeds in retrieving the deformable 3D pose from the 2D projections. Experiments on synthetic and real X-ray data show promising results of the proposed method as compared to state-of-the-art NRSfM