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Medical Images (medical + image)

Distribution by Scientific Domains

Medical Sciences	33%

Selected Abstracts

Nonrigid registration of multiple sclerosis brain images using lesion inpainting for morphometry or lesion mapping

HUMAN BRAIN MAPPING, Issue 4 2009
Michaël Sdika
Abstract Morphometric studies of medical images often include a nonrigid registration step from a subject to a common reference. The presence of white matter multiple sclerosis lesions will distort and bias the output of the registration. In this article, we present a method to remove this bias by filling such lesions to make the brain look like a healthy brain before the registration. We finally propose a dedicated method to fill the lesions and present numerical results showing that our method outperforms current state of the art method. Hum Brain Mapp, 2009. © 2008 Wiley-Liss, Inc. [source]

Radiation exposure and the justification of computed tomography scanning in an Australian hospital emergency department

INTERNAL MEDICINE JOURNAL, Issue 11 2009
M. Street
Abstract In an emergency department (ED), computed tomography (CT) is particularly beneficial in the investigation of high-speed trauma patients. With the advent of multidetector CT (MDCT) scanners, it is becoming faster and easier to conduct scans. In recent years, this has become evident with an increasing number of CT requests. Patients who have multiple CT scans during their hospital stay can receive radiation doses that have an increased theoretical risk of induction of cancer. It is essential that the clinical justification for each CT scan be considered on an individual basis and that due consideration is given to the radiation risk and possible diagnostic benefit. The current lack of a central State or Commonwealth data repository for medical images is a contributing factor to excessive radiation dosage to the population. The principles of justification and radiation risks are discussed in this study. [source]

On the segmentation of vascular geometries from medical images

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, Issue 1 2010
A. G. Radaelli
Abstract A 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 state-of-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. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Target image enhancement using representative line in MR cholangiography images

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 3 2004
Syoji Kobashi
Abstract MR cholangiography (MRC) is a commonly used imaging method for diagnosing the pancreatic duct. This article proposes a novel method for enhancing medical images called target image enhancement using representative line (TIER), and its application to MRC images. Our method first finds the representative line (RL) of the pancreatic duct using a fuzzy if-then rule. TIER presumes the approximate region of the pancreatic duct using the obtained RL, and then emphasizes the intensity in the approximate region. Therefore, our method does not require any segmentation procedures, which often may be hard work. We evaluated the ability to find the RL and to estimate the approximate region of the pancreatic duct by applying the proposed method to computer-simulated data and MRI phantom data. We also show the image-enhanced images for normal and abnormal patients. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 122,130, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20015 [source]