Accurate and Efficient Linear Structure Segmentation by Leveraging Ad Hoc Features with Learned Filters

Extracting linear structures, such as blood vessels or dendrites, from images is crucial in many medical imagery applications, and many handcrafted features have been proposed to solve this problem. However, such features rely on assumptions that are never entirely true. Learned features, on the other hand, can capture image characteristics difficult to define analytically, but tend to be much slower to compute than handcrafted features. We propose to complement handcrafted methods with features found using very recent Machine Learning techniques, and we show that even few filters are sufficient to efficiently leverage handcrafted features. We demonstrate our approach on the STARE, DRIVE, and BF2D datasets, and on 2D projections of neural images from the DIADEM challenge. Our proposal outperforms handcrafted methods, and pairs up with learning-only approaches at a fraction of their computational cost

Non-rigid image registration using electric current flow

In this paper, a novel electric current flow (ECF) based model is proposed to perform feature based non-rigid brain image registration. The ECF features simultaneously capture both voxel intensity and inter-voxel distance information. In the proposed ECF framework, each voxel is regarded as exhibiting electric potential proportional to voxel intensity. Voxels are connected by conductive wires in a pairwise manner. Each conductive wire has resistance, in which the resistance value is proportional to the length of the wire. The electric potential difference among connected pixels induces electric current passing through their connected wire. The amount of the electric current is the ratio between the voxel potential difference and the wire resistance. The potential difference and resistance are respectively proportional to the voxel intensity difference and the inter-voxel distance. By analyzing the electric current induced by the connection between a reference voxel and its counterparts in a given range, the ECF algorithm searches for the most salient connection to construct the ECF features. The ECF features are incorporated in the Markov random field labeling framework for non-rigid image registration. The registration quality of the proposed method has been evaluated intensively on both BrainWeb and IBSR databases. It is compared with four related approaches. Experimental results illustrate that the proposed method consistently achieves the highest registration accuracy among all the compared methods on both databases. © 2012 IEEE

Supervised Feature Learning for Curvilinear Structure Segmentation

Abstract. We present a novel, fully-discriminative method for curvilinear structure segmentation that simultaneously learns a classifier and the features it relies on. Our approach requires almost no parameter tuning and, in the case of 2D images, removes the requirement for handdesigned features, thus freeing the practitioner from the time-consuming tasks of parameter and feature selection. Our approach relies on the Gradient Boosting framework to learn discriminative convolutional filters in closed form at each stage, and can operate on raw image pixels as well as additional data sources, such as the output of other methods like the Optimally Oriented Flux. We will show that it outperforms state-of-theart curvilinear segmentation methods on both 2D images and 3D image stacks.

3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification

In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks

Marked Point Process Model for Curvilinear Structures Extraction

International audienceIn this paper, we propose a new marked point process (MPP) model and the associated optimization technique to extract curvilinear structures. Given an image, we compute the intensity variance and rotated gradient magnitude along the line segment. We constrain high level shape priors of the line segments to obtain smoothly connected line configuration. The optimization technique consists of two steps to reduce the significance of the parameter selection in our MPP model. We employ Monte Carlo sampler with delayed rejection to collect line hypotheses over different parameter spaces. Then, we maximize the consensus among line detection results to reconstruct the most plausible curvilinear structures without parameter estimation process. Experimental results show that the algorithm effectively localizes curvilinear structures on a wide range of datasets

Tubular structure filtering by ranking orientation responses of path operators

National 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 di- rections. 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 an efficient response on tubular structures