Biomedical imaging modalities: a tutorial

Acharya, Raj; Wasserman, Richard M.; Stevens, Jeffrey; Hinojosa, Carlos

doi:10.1016/0895-6111(94)00043-3

Cited by 66 publications

(36 citation statements)

References 10 publications

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“…In the following we provide a short description of the most common angiographic imaging techniques currently used in clinical practice with an emphasis on those aspects of the technique relevant to segmentation. A more detailed description can be found in [1].…”

Section: Introductionmentioning

confidence: 99%

On the segmentation of vascular geometries from medical images

Radaelli

Peiró

2009

Numer Methods Biomed Eng

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SUMMARYA comprehensive analysis of vascular morphology and the application of generic models of vascular biomechanics to specific patients require the ability of extracting a geometrical representation of the vascular anatomy from medical images. Owing to the wide range of clinical manifestations of vascular disease and associated imaging modalities and protocols, several segmentation methods have been proposed over the last 20 years and are available in the literature. In this paper, we review the methods of segmentation of angiographic medical images and identify major advantages and disadvantages of stateof-the-art techniques. We further discuss the performance of some of the most popular intensity-based and gradient-based methods using a set of images of peripheral by-pass grafts acquired with magnetic resonance angiography (MRA). We then propose a threshold front method for the segmentation of MRA images and assess its performance using two anatomic scale replica models, reproducing a normal and a stenotic peripheral artery. The threshold front algorithm is a simple, fast and parameter-free (still adaptive) method achieving segmentation errors below pixel resolution.

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Section: Introductionmentioning

confidence: 99%

On the segmentation of vascular geometries from medical images

Radaelli

Peiró

2009

Numer Methods Biomed Eng

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“…Images were captured in accordance to the standard protocol [33] with patients lying in the supine position and with the neck rotated to the left side while the right CCA was examined. The database can be downloaded from the SPLab web page 1 . As far as we know, this database is the only one freely available database of ultrasound images of common carotid artery.…”

Section: Image Databasementioning

confidence: 99%

“…Nowadays, a standard examination is often accompanied by additional examinations that use some of the modern medical imaging techniques [1]. These techniques rely on signals that are beyond the perception of human beings.…”

Section: Introduction Mmentioning

confidence: 99%

Towards an Optimal Interest Point Detector for Measurements in Ultrasound Images

Zukal¹,

Beneš²,

Číka³

et al. 2013

Measurement Science Review

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This paper focuses on the comparison of different interest point detectors and their utilization for measurements in ultrasound (US) images. Certain medical examinations are based on speckle tracking which strongly relies on features that can be reliably tracked frame to frame. Only significant features (interest points) resistant to noise and brightness changes within US images are suitable for accurate long-lasting tracking. We compare three interest point detectors -Harris-Laplace, Difference of Gaussian (DoG) and Fast Hessian -and identify the most suitable one for use in US images on the basis of an objective criterion. Repeatability rate is assumed to be an objective quality measure for comparison. We have measured repeatability in images corrupted by different types of noise (speckle noise, Gaussian noise) and for changes in brightness. The Harris-Laplace detector outperformed its competitors and seems to be a sound option when choosing a suitable interest point detector for US images. However, it has to be noted that Fast Hessian and DoG detectors achieved better results in terms of processing speed.

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“…A DICOM image usually contains one slice (a 2D image) acquired using any medical imaging modality (MRI, CT-scan, PET, SPECT, ultrasound, X-ray... [1]). A DICOM image may contain a multi-slice data set but this is rarely encountered.…”

Section: Dicom Formatmentioning

confidence: 99%

A Secure Grid Medical Data Manager Interfaced to the gLite Middleware

Montagnat¹,

Frohner

Jouvenot³

et al. 2007

J Grid Computing

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Abstract. The medical community is producing and manipulating a tremendous volume of digital data for which computerized archiving, processing and analysis is needed. Grid infrastructures are promising for dealing with challenges arising in computerized medicine but the manipulation of medical data on such infrastructures faces both the problem of interconnecting medical information systems to grid middlewares and of preserving patients' privacy in a wide and distributed multiuser system. These constraints are often limiting the use of grids for manipulating sensitive medical data.This paper describes our design of a medical data management system taking advantage of the advanced gLite data management services, developed in the context of the EGEE project, to fulfill the stringent needs of the medical community. It ensures medical data protection through strict data access control, anonymization and encryption. The multi-level access control provides the flexibility needed for c 2007 Kluwer Academic Publishers. Printed in the Netherlands.GRID172.tex; 1 2 J. Montagnat, A. Frohner, D. Jouvenot, C. Pera et al implementing complex medical use-cases. Data anonymization prevents the exposure of most sensitive data to unauthorized users, and data encryption guarantees data protection even when it is stored at remote sites. Moreover, the developed prototype provides a grid Storage Resource Manager (SRM) interface to standard medical DICOM servers thereby enabling transparent access to medical data without interfering with medical practice.Keywords: Secure grid storage, gLite middleware, medical data management 1. Context ObjectivesMany scientific areas benefit from large and distributed storage capabilities provided by grid infrastructures. On top of physical storage resources, the EGEE [17] grid data management system eases the manipulation of large data volumes and provides high level functionality such as data distribution, replication and optimized access. To build a data management system that can adapt to the heterogeneous data storage resources (disk, tapes, silos...), the grid community has adopted standard interfaces to virtualize the underlying resources. In particular, gLite [23], the next generation EGEE middleware, has adopted the Storage Resource Manager (SRM) interface [29] standardized in the context of the Open Grid Forum [28]. The SRM's primary concern is to provide efficient access to large volumes of data. It provides, among other services, prefetching of data files recorded on secondary storage, management of storage space and reservation of storage resources. However, it does not provide any access control nor protection of data which severely limits its usability for applications manipulating sensitive data.In this paper, we address the problem of sensitive data management on the EGEE grid infrastructure and we introduce a data management service designed to handle medical records on grids. We first motivate our approach through an in-depth requirement analysis of data management in the medical ar...

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Biomedical imaging modalities: a tutorial

Cited by 66 publications

References 10 publications

On the segmentation of vascular geometries from medical images

On the segmentation of vascular geometries from medical images

Towards an Optimal Interest Point Detector for Measurements in Ultrasound Images

A Secure Grid Medical Data Manager Interfaced to the gLite Middleware

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