Abstract：
:We present an algorithm estimating the width of retinal vessels in fundus camera images. The algorithm uses a novel parametric surface model of the cross-sectional intensities of vessels, and ensembles of bagged decision trees to estimate the local width from the parameters of the best-fit surface. We report comparative tests with REVIEW, currently the public database of reference for retinal width estimation, containing 16 images with 193 annotated vessel segments and 5066 profile points annotated manually by three independent experts. Comparative tests are reported also with our own set of 378 vessel widths selected sparsely in 38 images from the Tayside Scotland diabetic retinopathy screening programme and annotated manually by two clinicians. We obtain considerably better accuracies compared to leading methods in REVIEW tests and in Tayside tests. An important advantage of our method is its stability (success rate, i.e., meaningful measurement returned, of 100% on all REVIEW data sets and on the Tayside data set) compared to a variety of methods from the literature. We also find that results depend crucially on testing data and conditions, and discuss criteria for selecting a training set yielding optimal accuracy.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Lupaşcu CA,Tegolo D,Trucco Edoi
10.1016/j.media.2013.07.006subject
Has Abstractpub_date
2013-12-01 00:00:00pages
1164-80issue
8eissn
1361-8415issn
1361-8423pii
S1361-8415(13)00113-8journal_volume
17pub_type
杂志文章abstract：:We propose in this article a content-based image retrieval (CBIR) method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2009.11.004
更新日期：2010-04-01 00:00:00
abstract：:We propose two information theoretic similarity measures that allow to incorporate tissue class information in non-rigid image registration. The first measure assumes that tissue class probabilities have been assigned to each of the images to be registered by prior segmentation of both of them. One image is then non-r...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2005.03.004
更新日期：2006-06-01 00:00:00
abstract：:Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Currently, many multi-task learning approaches have been proposed to predict the disease progression at the early stage using longitudinal data, with each task corresponding to a pa...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2019.01.007
更新日期：2019-04-01 00:00:00
abstract：:A novel method for estimating a field of fiber orientation distribution (FOD) based on signal de-convolution from a given set of diffusion weighted magnetic resonance (DW-MR) images is presented. We model the FOD by higher order Cartesian tensor basis using a parametrization that explicitly enforces the positive semi-...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2012.07.002
更新日期：2012-08-01 00:00:00
abstract：:A stochastic finite element framework is presented for the simultaneous estimation of the cardiac kinematic functions and material model parameters from periodic medical image sequences. While existing biomechanics studies of the myocardial material constitutive laws have assumed known tissue kinematic measurements, a...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/s1361-8415(03)00066-5
更新日期：2003-12-01 00:00:00
abstract：:Diffusion MRI magnitude data, typically Rician or noncentral χ distributed, is affected by the noise floor, which falsely elevates signal, reduces image contrast, and biases estimation of diffusion parameters. Noise floor can be avoided by extracting real-valued Gaussian-distributed data from complex diffusion-weighte...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2020.101828
更新日期：2021-02-01 00:00:00
abstract：:In this paper, we present a framework to estimate local ventricular myocardium contractility using clinical MRI, a heart model and data assimilation. First, we build a generic anatomical model of the ventricles including muscle fibre orientations and anatomical subdivisions. Then, this model is deformed to fit a clini...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2006.04.002
更新日期：2006-08-01 00:00:00
abstract：:Biophysical models of the atrium provide a physically constrained framework for describing the current state of an atrium and allow predictions of how that atrium will respond to therapy. We propose a work flow to simulate patient specific electrophysiological heterogeneity from clinical data and validate the resultin...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2018.04.005
更新日期：2018-07-01 00:00:00
abstract：:We present a machine learning approach called shape regression machine (SRM) for efficient segmentation of an anatomic structure that exhibits a deformable shape in a medical image, e.g., left ventricle endocardial wall in an echocardiogram. The SRM achieves efficient segmentation via statistical learning of the inter...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2010.04.002
更新日期：2010-08-01 00:00:00
abstract：:An automated vendor-independent system for dose monitoring in computed tomography (CT) medical examinations involving ionizing radiation is presented in this paper. The system provides precise size-specific dose estimates (SSDE) following the American Association of Physicists in Medicine regulations. Our dose managem...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2020.101898
更新日期：2021-02-01 00:00:00
abstract：:An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and clas...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2015.04.015
更新日期：2015-07-01 00:00:00
abstract：:For many orthopaedic, neurological, and oncological applications, an exact segmentation of the vertebral column including an identification of each vertebra is essential. However, although bony structures show high contrast in CT images, the segmentation and labelling of individual vertebrae is challenging. In this pa...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2009.02.004
更新日期：2009-06-01 00:00:00
abstract：:This paper presents a symbolic visualization environment known as the Corner Cube environment, which was developed to facilitate rapid examination and comparison of activated foci defined by analyses of functional neuroimaging datasets. We have performed a comparative evaluation of this environment against maximum-int...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/s1361-8415(98)80020-0
更新日期：1998-09-01 00:00:00
abstract：:Diffusion magnetic resonance imaging (dMRI) provides a noninvasive method for measuring brain tissue microstructure. q-Space deep learning(q-DL) methods have been developed to accurately estimate tissue microstructure from dMRI scans acquired with a reduced number of diffusion gradients. In these methods, deep network...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2020.101885
更新日期：2021-01-01 00:00:00
abstract：:We evaluate the accuracy of whole-body bone extraction from whole-body MR images using a number of atlas-based segmentation methods. The motivation behind this work is to find the most promising approach for the purpose of MRI-guided derivation of PET attenuation maps in whole-body PET/MRI. To this end, a variety of a...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2016.11.003
更新日期：2017-02-01 00:00:00
abstract：:Accurate digital representation of major white matter bundles in the brain is an important goal in neuroscience image computing since the representations can be used for surgical planning, intra-patient longitudinal analysis and inter-subject population connectivity studies. Reconstructing desired fiber bundles genera...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2018.02.008
更新日期：2018-05-01 00:00:00
abstract：:Neural cell instance segmentation, which aims at joint detection and segmentation of every neural cell in a microscopic image, is essential to many neuroscience applications. The challenge of this task involves cell adhesion, cell distortion, unclear cell contours, low-contrast cell protrusion structures, and backgrou...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2019.05.004
更新日期：2019-07-01 00:00:00
abstract：:Deep learning based methods have improved the estimation of tissue microstructure from diffusion magnetic resonance imaging (dMRI) scans acquired with a reduced number of diffusion gradients. These methods learn the mapping from diffusion signals in a voxel or patch to tissue microstructure measures. In particular, it...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2020.101650
更新日期：2020-04-01 00:00:00
abstract：:The Cartesian parallel magnetic imaging problem is formulated variationally using a high-order penalty for coil sensitivities and a total variation like penalty for the reconstructed image. Then the optimality system is derived and numerically discretized. The objective function used is non-convex, but it possesses a ...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2011.07.002
更新日期：2012-01-01 00:00:00
abstract：:A novel image segmentation algorithm was developed to allow the automatic segmentation of both normal and abnormal anatomy from medical images. The new algorithm is a form of spatially varying statistical classification, in which an explicit anatomical template is used to moderate the segmentation obtained by statisti...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/s1361-8415(00)00003-7
更新日期：2000-03-01 00:00:00
abstract：:Classification of digital pathology images is imperative in cancer diagnosis and prognosis. Recent advancements in deep learning and computer vision have greatly benefited the pathology workflow by developing automated solutions for classification tasks. However, the cost and time for acquiring high quality task-speci...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2020.101859
更新日期：2021-01-01 00:00:00
abstract：:In minimally invasive surgery, deployment of motion compensation, dynamic active constraints and adaptive intra-operative guidance require accurate estimation of deforming tissue in 3D. To this end, the use of vision-based techniques is advantageous in that it does not require the integration of additional hardware to...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2011.02.010
更新日期：2012-04-01 00:00:00
abstract：:This paper describes a new robust and fully automatic method for calibration of three-dimensional (3D) freehand ultrasound called Confhusius (CalibratiON for FreeHand UltraSound Imaging USage). 3D Freehand ultrasound consists in mounting a position sensor on a standard probe. The echographic B-scans can be localized i...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2004.06.021
更新日期：2005-02-01 00:00:00
abstract：:Highly relevant for both clinical and legal medicine applications, the established radiological methods for estimating unknown age in children and adolescents are based on visual examination of bone ossification in X-ray images of the hand. Our group has initiated the development of fully automatic age estimation meth...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2019.101538
更新日期：2019-12-01 00:00:00
abstract：:In this paper, we exploit the ability of Compressed Sensing (CS) to recover the whole 3D Diffusion MRI (dMRI) signal from a limited number of samples while efficiently recovering important diffusion features such as the Ensemble Average Propagator (EAP) and the Orientation Distribution Function (ODF). Some attempts to...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2013.02.010
更新日期：2013-07-01 00:00:00
abstract：:Atlas-based methods for automated preoperative planning of the femoral stem implant in total hip arthroplasty are described. Statistical atlases are constructed from a number of past preoperative plans prepared by experienced surgeons in order to represent the surgeon's expertise of the planning. Two types of atlases ...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2011.10.005
更新日期：2012-02-01 00:00:00
abstract：:Diffusion tensor imaging can provide the fundamental information required for viewing structural connectivity. However, robust and accurate acquisition and processing algorithms are needed to accurately map the nerve connectivity. In this paper, we present a novel algorithm for extracting and visualizing the fiber tra...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2003.12.001
更新日期：2004-06-01 00:00:00
abstract：:The incorporation of intensity, spatial, and topological information into large-scale multi-region segmentation has been a topic of ongoing research in medical image analysis. Multi-region segmentation problems, such as segmentation of brain structures, pose unique challenges in image segmentation in which regions may...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2015.05.005
更新日期：2016-01-01 00:00:00
abstract：:Automated delineation of anatomical structures in chest radiographs is difficult due to superimposition of multiple structures. In this work an automated technique to segment the clavicles in posterior-anterior chest radiographs is presented in which three methods are combined. Pixel classification is applied in two s...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2012.06.009
更新日期：2012-12-01 00:00:00
abstract：:The spinal column is one of the most distinguishable structures in CT scans of the superior part of the human body. It is not necessary to segment the spinal column in order to use it as a frame of reference. It is sufficient to place landmarks and the appropriate anatomical labels at intervertebral disks and vertebra...
journal_title：Medical image analysis
pub_type： 杂志文章
doi：10.1016/j.media.2013.07.005
更新日期：2013-12-01 00:00:00
