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Many biomedical applications require detection of curvilinear networks in images, and would benefit from automatic or semiautomatic segmentation to allow high-throughput measurements. Here we discuss a contrast independent approach to identify curvilinear structures based on oriented phase congruency, the Phase Congruency Tensor. We show that the proposed approach is largely insensitive to intensity...
Better utilizing the vast amount of valuable information stored in the medical imaging databases is always an interesting research area, and one way is to retrieve similar images as a reference dataset to assist the diagnosis. Distance metric is a core component in image retrieval; and in this paper, we propose a new learning-based distance metric design, based on regression and classification techniques...
Primary ciliary dyskinesia (PCD) implies cilia with dysmotility or total absence of motility, which may result in sinusitis, chronicbronchitis, bronchiectasis and male infertility. A large number of deficiencies detectable on the ultrastructural level give rise to PCD, but patients with normal cilia ultrastructure are common. An early diagnosis is very important since PCD can cause permanent lung...
Statistical shape models are commonly used in various applications of computer vision. Nevertheless, these models are not well adapted to hierarchical structures. This paper proposes a solution to this problem by presenting a general framework to build multilevel statistical shape models. Based on multilevel component analysis, the idea is to decompose the data into a within-individual and a between-individual...
Magnetic resonance angiography (MRA) provides a noninvasive means to detect the presence, location and severity of atherosclerosis throughout the vascular system. In such studies, and especially those in the coronary arteries, the vessel luminal area is typically measured at multiple cross-sectional locations along the course of the artery. The advent of fast volumetric imaging techniques covering...
We propose a new multivariate method, partial sparse canonical correlation analysis (PSCCA), for computing the statistical comparisons needed by population studies in medical imaging. PSCCA is a multivariate generalization of linear regression that allows one to statistically parameterize imaging studies in terms of multiple views of the population (e.g., the full collection of measurements taken...
The distributed source inverse problem for Magnetoencephalography (MEG) requires regularisation in order to calculate a stable and accurate solution. Recently there has been interest in source reconstruction via the l1 penalised least squares problem. Although this gives some sparseness the ideal measure for sparsity is the l0 norm. Here we develop a cyclic descent algorithm to solve the grouped-variable...
We investigate saddle points in 3D cardiac images. We do so by improving a critical point detection algorithm, the 3D winding number, or Poincaré index. We consider two different applications. We estimate cardiac motion from 3D tagged MRI data, based on tracking of saddle points. We also employ our method for saddle point extraction in blood flow data, acquired by phase contrast MRI.
We propose a biquaternion formalism to model diffusion tensor magnetic resonance imaging (DT-MRI) data. Unlike previous methods that use dimensionality reduction, we are able to process the full tensor in a holistic manner while respecting the underlying manifold of the data. Using this approach, we introduce the Fourier transform and convolution for DT-MRI for the first time, which can be applied...
In phosphorescence lifetime imaging methods oxygen tension in retinal vessels has traditionally been indirectly determined from the estimates of intermediate variables whose noise-contaminated linear combinations are observed as phosphorescence intensity images. The classical least squares (LS) and regularized least squares (RLS) methods were used to obtain estimates of the intermediate variables...
This paper describes a method for medical images annotation based on the SURF descriptor and the SVM classifier. For the features extraction a Fast-Hessian detector was used. The feature matching was performed with a SVM with a quadratic kernel. The testing of the developed system was performed using a subset of the IRMA radiographic images. The results provided with the SURF descriptor are compared...
Recently, the in vivo imaging of pulmonary alveoli was made possible thanks to confocal microscopy. For these new images, we wish to aid the clinician by developing a computer-aided diagnosis system, able to detect a pathological state in these images. An original approach that combines a texture-based characterization of the images and uses a boosted cascade of classifiers to detect a pathological...
Recent advances in vector-field imaging have brought to the forefront the need for efficient denoising and reconstruction algorithms that take the physical properties of vector fields into account and can be applied to large volumes of data. With these requirements in mind, we propose a computationally efficient algorithm for variational de-noising and reconstruction of vector fields. Our variational...
We present a new fast active contour for images in 3D microscopy. We introduce a fully parametric design that relies on exponential B-spline bases and allows us to impose a sphere-like topology. The proposed 3D snake can approximate blob-like objects with good accuracy. The optimization process is remarkably fast. Our technique yields successful segmentation results even for a challenging data set...
A weakly supervised image classification framework is presented in this paper. Given reference images marked by clinicians as relevant or irrelevant, we learn to automatically detect relevant patterns, i.e. patterns that only appear in relevant images. After training, relevant patterns are sought in unseen images in order to classify each image as relevant or irrelevant. No manual segmentations are...
We investigate a non quadratic regularizer that is based on the Hessian operator for dealing with the restoration of 3-D images in a variational framework. We show that the regularizer under study is a valid extension of the total-variation (TV) functional, in the sense that it retains its favorable properties while following a similar underlying principle. We argue that the new functional is well...
In this paper, a segmentation method of the retinal images exudates is proposed. First, pixels that belong to exudates are located using the scale-space extrinsic curvature. These candidate points, are used together with the mean curvature to select possible exudates patches. True exudates are selected using the local maxima blob response through dynamical threshold, which will represent the final...
In this paper we propose a fast method to compute the longitudinal extension of surfaces using the extrema of the first eigenfunction of Laplace-Beltrami Operator and the hot spots conjecture. We also propose an original definition of the surface width based on the distance to the longest geodesic. We show that the implementation of our new definition of length is consistent with the one computed...
In this paper a novel articulated atlas for the fully automated segmentation of the skeleton from head & neck CT datasets is presented. An individual atlas describing the shape and appearance is created for each individual bone. Principal Component Analysis is used to learn spatial relations between those atlases resulting in a unified articulated atlas. Transformations are parameterized using...
Over decades, segmentation has remained a salient task in most medical imaging applications confronting multi-faced challenges including limited image quality. In this paper, we present a new anisotropic region growing segmentation approach for vascular or other elongated structures. A fundamental challenge during tracing vascular structures is broken continuity of structures by noise and other imaging...
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