Showing papers in "Medical Image Analysis in 2009"

TL;DR: Statistical shape models (SSMs) have by now been firmly established as a robust tool for segmentation of medical images as discussed by the authors, primarily made possible by breakthroughs in automatic detection of shape correspondences.

TL;DR: This paper reviews state-of-the-art literature on vascular segmentation with a particular focus on 3D contrast-enhanced imaging modalities (MRA and CTA) and discusses the theoretical and practical properties of recent approaches and highlight the most advanced and promising ones.

TL;DR: The aim of this review is to explain and to categorize the various algorithms into groups and their application in the field of medical signal analysis.

TL;DR: A standardized evaluation methodology and reference database for the quantitative evaluation of coronary artery centerline extraction algorithms and a database containing 32 cardiac CTA datasets with corresponding reference standard is described and made available.

TL;DR: A comprehensive solution for automatically detecting, identifying, and segmenting vertebrae in CT images by designing a framework that takes an arbitrary CT image as input and provides a segmentation in form of labelled triangulated vertebra surface models.

TL;DR: The detection results presented are a realistic measure of a CAD system performance in a low-dose screening study which includes a diverse array of nodules of many varying sizes, types and textures.

TL;DR: A fully automated, fast method to detect the fovea and the optic disc in digital color photographs of the retina is presented and combines cues measured directly in the image with cues derived from a segmentation of the retinal vasculature.

TL;DR: An automatic method based on mixture models to dynamically threshold the images in order to separate exudates from background and image-based classification accuracy is evaluated, obtaining a sensitivity of 100% and a specificity of 90%.

TL;DR: A Matching Pursuit Algorithm for currents is proposed, which allows us to approximate, at any desired accuracy, the mean and modes of a population of geometrical primitives modeled as currents, which offers a sparse representation of the currents, and offers a way to visualize, and hence to interpret, such statistics.

TL;DR: Simulation of diastolic LV mechanics allowed us to estimate the stiffness of the passive LV myocardium based on kinematic data obtained from tagged MRI, thereby improving the understanding of the underlying structural basis of mechanical dysfunction under pathological conditions.

TL;DR: This paper presents a 2D/3D correspondence building method based on a non-rigid 2D point matching process, which iteratively uses a symmetric injective nearest-neighbor mapping operator and 2D thin-plate splines based deformations to find a fraction of best matched2D point pairs between features extracted from the X-ray images and those extracts from the 3D model.

TL;DR: The ability to simultaneously track cell cycle phase and cell motion at the single cell level is demonstrated in a model-based approach to characterize the four phases of the cell cycle G1, S, G2, and M.

TL;DR: This paper presents a system that combines physical and statistical modeling to generate synthetic multi-modal 3D brain MRI with tumor and edema, along with the underlying anatomical ground truth, with main emphasis on simulation of the major effects known for tumor MRI.

TL;DR: An efficient solution procedure is developed for the accompanying viscoelastic hereditary integrals which allows use of such models in explicit dynamic finite element algorithms, and the implementation of a new GPU-based finite element scheme for soft tissue simulation using the CUDA API is described.

TL;DR: A carefully engineered algorithm using Kalman-snakes and optical flow computation is presented and results indicate that this algorithm can significantly speed up the task of manual axon tracking.

TL;DR: It turns out that the probabilistic approaches for multiple virus tracking in multi-channel fluorescence microscopy images are superior to the deterministic schemes as well as to the approaches based on a mixture of particle filters.

TL;DR: A set of algorithms for real time computation of soft tissue deformation, starting with the finite element formulation and the integration scheme used and addressing common problems such as hourglass control and locking are presented.

TL;DR: A method that automatically segments the liver by combining more phases of the contrast-enhanced CT examination is presented, which demonstrates that automatic segmentation is more reliable when the information of more phases is combined.

TL;DR: A novel voxel-based method based upon the Laplacian definition of thickness that is both accurate and computationally efficient is proposed, and the number of samples is reduced by 25% to find significant differences between the two groups.

TL;DR: A fully automatic, multiscale fuzzy C-means (MsFCM) classification method for MR images is presented and can provide a quantitative tool for neuroimaging and other applications.

TL;DR: An augmented reality guidance system for liver thermal ablation in interventional radiology is presented and developed and validated algorithms that automatically process and extract feature points and the overall targeting accuracy on an abdominal phantom is validated.

TL;DR: This work presents a method for the estimation of various features of the tissue micro-architecture using the diffusion magnetic resonance imaging and demonstrates the effectiveness of the method with results on both synthetic phantom and real MR datasets acquired in a clinical time-frame.

TL;DR: The problem of shape matching and analysis of folding patterns is studied by extending the notion of depth maps when no natural projection plane exists by solving a time independent Poisson equation.

TL;DR: Phi UN, however, was significantly faster at low signal to noise ratio (SNR) and data with a more complex phase topography, making it particularly suitable for applications with low SNR and high spatial resolution.

TL;DR: A selective method of measurement for computing image similarities based on characteristic structure extraction is proposed and applied to flexible endoscope navigation and revealed that bronchoscope tracking using the proposed method could track up to 1600 consecutive bronchoscopic images without external position sensors.

TL;DR: This paper introduces a novel displacement tracking algorithm, with a search strategy guided by a data quality indicator, and shows that the proposed algorithm is more robust when the displacement distribution is challenging.

TL;DR: In this paper, a model-driven compensation for the brain deformation during neurosurgery is proposed, where the deformation of the aspirated tissue is imaged via a mirror using an external camera.

TL;DR: Motion-guided segmentation employs the displacement-encoded phase shifts intrinsic to DENSE MRI to accurately propagate a single set of pre-defined contours throughout the remaining cardiac phases.

TL;DR: A system for respiratory motion correction of MRI-derived roadmaps for use in X-ray guided cardiac catheterisation procedures using a subject-specific affine motion model that is quickly constructed from a short pre-procedure MRI scan is described.

TL;DR: A fast and novel probabilistic method for white matter fiber tracking in diffusion weighted MRI (DWI), which takes advantage of the weighting and resampling mechanism of particle filtering.