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Segmentation Methods (segmentation + methods)

Distribution by Scientific Domains

Medical Sciences	57%

Selected Abstracts

Evaluation of automated brain MR image segmentation and volumetry methods

HUMAN BRAIN MAPPING, Issue 4 2009
Frederick Klauschen
Abstract We compare three widely used brain volumetry methods available in the software packages FSL, SPM5, and FreeSurfer and evaluate their performance using simulated and real MR brain data sets. We analyze the accuracy of gray and white matter volume measurements and their robustness against changes of image quality using the BrainWeb MRI database. These images are based on "gold-standard" reference brain templates. This allows us to assess between- (same data set, different method) and also within-segmenter (same method, variation of image quality) comparability, for both of which we find pronounced variations in segmentation results for gray and white matter volumes. The calculated volumes deviate up to >10% from the reference values for gray and white matter depending on method and image quality. Sensitivity is best for SPM5, volumetric accuracy for gray and white matter was similar in SPM5 and FSL and better than in FreeSurfer. FSL showed the highest stability for white (<5%), FreeSurfer (6.2%) for gray matter for constant image quality BrainWeb data. Between-segmenter comparisons show discrepancies of up to >20% for the simulated data and 24% on average for the real data sets, whereas within-method performance analysis uncovered volume differences of up to >15%. Since the discrepancies between results reach the same order of magnitude as volume changes observed in disease, these effects limit the usability of the segmentation methods for following volume changes in individual patients over time and should be taken into account during the planning and analysis of brain volume studies. Hum Brain Mapp, 2009. © 2008 Wiley-Liss, Inc. [source]

Assessment of the increase in variability when combining volumetric data from different scanners

HUMAN BRAIN MAPPING, Issue 2 2009
Santiago Reig
Abstract In multicenter MRI studies, pooling of volumetric data requires a prior evaluation of compatibility between the different machines used. We tested the compatibility of five different scanners (2 General Electric Signa, 2 Siemens Symphony, and a Philips Gyroscan) at five different sites by repeating the scans of five volunteers at each of the sites. Using a semiautomatic method based on the Talairach atlas, and SPM algorithms for tissue segmentation (multimodal T1 and T2, or T1-only), we obtained volume measurements of the main brain lobes (frontal, parietal, occipital, temporal) and for each tissue type. Our results suggest that pooling of multisite data adds small error for whole brain measurements, intersite coefficient of variation (CV) ranging from 1.8 to 5.2%, respectively, for GM and CSF. However, in the occipital lobe, intersite CV can be as high as 11.7% for WM and 17.3% for CSF. Compared with the intersite, intrasite CV values were always much lower. Whenever possible, T1 and T2 tissue segmentation methods should be used because they yield more consistent volume measurements between sites than T1-only, especially when some of the scans were obtained with different sequence parameters and pixel size from those of the other sites. Our study shows that highest compatibility among scanners would be obtained using equipments of the same manufacturer and also image acquisition parameters as similar as possible. After validation, data from a specific ROI or scanner showing values markedly different from the other sites might be excluded from the analysis. Hum Brain Mapp, 2009. © 2007 Wiley-Liss, Inc. [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]

Satellite image segmentation using hybrid variable genetic algorithm

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 3 2009
Mohamad M. Awad
Abstract Image segmentation is an important task in image processing and analysis. Many segmentation methods have been used to segment satellite images. The success of each method depends on the characteristics of the acquired image such as resolution limitations and on the percentage of imperfections in the process of image acquisition due to noise. Many of these methods require a priori knowledge which is difficult to obtain. Some of them are parametric statistical methods that use many parameters which are dependent on image property. In this article, a new unsupervised nonparametric method is developed to segment satellite images into homogeneous regions without any a priori knowledge. The new method is called hybrid variable genetic algorithm (HVGA). The variability is found in the variable number of cluster centers and in the changeable mutation rate. In addition, this new method uses different heuristic processes to increase the efficiency of genetic algorithm in avoiding local optimal solutions. Experiments performed on two different satellite images (Landsat and Spot) proved the high accuracy and efficiency of HVGA compared with another two unsupervised and nonparametric segmentation methods genetic algorithm (GA) and self-organizing map (SOM). The verification of the results included stability and accuracy measurements using an evaluation method implemented from the functional model (FM) and field surveys. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 199,207, 2009 [source]

Comparison of segmentation methods for MRI measurement of cardiac function in rats

JOURNAL OF MAGNETIC RESONANCE IMAGING, Issue 4 2010
Johannes Riegler MSc
Abstract Purpose To establish the accuracy, intra- and inter-observer variabilities of four different segmentation methods for measuring cardiac functional parameters in healthy and infarcted rat hearts. Materials and Methods Six Wistar rats were imaged before and after myocardial infarction using an electrocardiogram and respiratory-gated spoiled gradient echo sequence. Blinded and randomized datasets were analyzed by various semi-automatic and manual segmentation methods to compare their measurement bias and variability. In addition, the accuracy of these methods was assessed by comparison with reference measurements acquired from high-resolution three-dimensional (3D) datasets of a heart phantom. Results Relative inter- and intra-observer variability were found to be similar for all four methods. Semi-automatic segmentation methods reduced analysis time by up to 70%, while yielding similar measurement bias and variability compared with manual segmentation. Semi-automatic methods were found to underestimate the ejection fraction for healthy hearts compared with manual segmentation while overestimating them in infarcted hearts. However, semi-automatic segmentation of short axis slices agreed better with 3D reference scans of a heart phantom compared with manual segmentation. Conclusion Semi-automatic segmentation methods are faster than manual segmentation, while offering a similar intra- and inter-observer variability. However, a potential bias has been observed between healthy and infarcted hearts for different methods, which should also be considered when selecting the most appropriate analysis technique. J. Magn. Reson. Imaging 2010;32:869,877. © 2010 Wiley-Liss, Inc. [source]

Segmentation of 3D microtomographic images of granular materials with the stochastic watershed

JOURNAL OF MICROSCOPY, Issue 1 2010
M. FAESSEL
Summary Segmentation of 3D images of granular materials obtained by microtomography is not an easy task. Because of the conditions of acquisition and the nature of the media, the available images are not exploitable without a reliable method of extraction of the grains. The high connectivity in the medium, the disparity of the object's shape and the presence of image imperfections make classical segmentation methods (using image gradient and watershed constrained by markers) extremely difficult to perform efficiently. In this paper, we propose a non-parametric method using the stochastic watershed, allowing to estimate a 3D probability map of contours. Procedures allowing to extract final segmentation from this function are then presented. [source]