4,996 research outputs found
A Weighted K-means Algorithm applied to Brain Tissue Classification
Tissue classification in Magnetic Resonance (MR) brain images is an important issue in the analysis of several brain dementias. This paper presents a modification of the classical K-means algorithm taking into account the number of times specific features appear in an image, employing, for that purpose, a weighted mean to calculate the centroid of every cluster. Pattern Recognition techniques allow grouping pixels based on features similarity. In this paper, multispectral gray-level intensity MR brain images are used. T1, T2 and PD-weighted images provide different and complementary information about the tissues. Segmentation is performed in order to classify each pixel of the resulting image according to four possible classes: cerebro-spinal fluid (CSF), white matter (WM), gray matter (GM) and background. T1, T2 and PD-weighted images are used as patterns. The proposed algorithm weighs the number of pixels corresponding to each set of gray levels in the feature vector. As a consequence, an automatic segmentation of the brain tissue is obtained. The algorithm provides faster results if compared with the traditional K-means, thereby retrieving complementary information from the images.Facultad de Informátic
Deep grey matter volumetry as a function of age using a semi-automatic qMRI algorithm
Quantitative Magnetic Resonance has become more and more accepted for clinical trial in many fields. This technique not only can generate qMRI maps (such as T1/T2/PD) but also can be used for further postprocessing including segmentation of brain and characterization of different brain tissue. Another main application of qMRI is to measure the volume of the brain tissue such as the deep Grey Matter (dGM). The deep grey matter serves as the brain's "relay station" which receives and sends inputs between the cortical brain regions. An abnormal volume of the dGM is associated with certain diseases such as Fetal Alcohol Spectrum Disorders (FASD). The goal of this study is to investigate the effect of age on the volume change of the dGM using qMRI.
Thirteen patients (mean age= 26.7 years old and age range from 0.5 to 72.5 years old) underwent imaging at a 1.5T MR scanner. Axial images of the entire brain were acquired with the mixed Turbo Spin-echo (mixed -TSE) pulse sequence. The acquired mixed-TSE images were transferred in DICOM format image for further analysis using the MathCAD 2001i software (Mathsoft, Cambridge, MA). Quantitative T1 and T2-weighted MR images were generated. The image data sets were further segmented using the dual-space clustering segmentation. Then volume of the dGM matter was calculated using a pixel counting algorithm and the spectrum of the T1/T2/PD distribution were also generated. Afterwards, the dGM volume of each patient was calculated and plotted on scatter plot. The mean volume of the dGM, standard deviation, and range were also calculated.
The result shows that volume of the dGM is 47.5 ±5.3ml (N=13) which is consistent with former studies. The polynomial tendency line generated based on scatter plot shows that the volume of the dGM gradually increases with age at early age and reaches the maximum volume around the age of 20, and then it starts to decrease gradually in adulthood and drops much faster in elderly age. This result may help scientists to understand more about the aging of the brain and it can also be used to compare with the results from former studies using different techniques
Recommended from our members
A survey of fuzzy rule-based image segmentation techniques
This paper describes the various fuzzy rule based techniques for image segmentation. Fuzzy rule based segmentation techniques can incorporate domain expert knowledge and manipulate numerical as well as linguistic data. They are also capable of drawing partial inference using fuzzy IF-THEN rules. For these reasons they have been extensively applied in medical imaging. But these rules are application domain specific and it is very difficult to define the rules either manually or automatically so that the segementation can be achieved successfully
MRI Super-Resolution using Multi-Channel Total Variation
This paper presents a generative model for super-resolution in routine
clinical magnetic resonance images (MRI), of arbitrary orientation and
contrast. The model recasts the recovery of high resolution images as an
inverse problem, in which a forward model simulates the slice-select profile of
the MR scanner. The paper introduces a prior based on multi-channel total
variation for MRI super-resolution. Bias-variance trade-off is handled by
estimating hyper-parameters from the low resolution input scans. The model was
validated on a large database of brain images. The validation showed that the
model can improve brain segmentation, that it can recover anatomical
information between images of different MR contrasts, and that it generalises
well to the large variability present in MR images of different subjects. The
implementation is freely available at https://github.com/brudfors/spm_superre
Atherosclerotic carotid plaque composition: a 3T and 7T MRI-histology correlation study
Background and Purpose
Carotid artery atherosclerotic plaque composition may influence plaque stability and risk of
thromboembolic events, and non-invasive plaque imaging may therefore permit risk
stratification for clinical management. Plaque composition was compared using non-invasive
in-vivo (3T) and ex-vivo (7T) MRI and histopathological examination.
Methods
Thirty three endarterectomy cross sections, from 13 patients, were studied. The datasets
consisted of in-vivo 3T MRI, ex-vivo 7T MRI and histopathology. Semi-automated
segmentation methods were used to measure areas of different plaque components. Bland-
Altman plots and mean difference with 95% confidence interval were carried out.
Results
There was general quantitative agreement between areas derived from semi-automated
segmentation of MRI data and histology measurements. The mean differences and 95%
confidence bounds in the relative to total plaque area between 3T versus Histology were:
fibrous tissue 4.99 % (-4.56 to 14.56), lipid-rich/necrotic core (LR/NC) with haemorrhage -
1.81% (-14.11 to 10.48), LR/NC without haemorrhage -2.43% (-13.04 to 8.17), and
calcification -3.18% (-11.55 to 5.18). The mean differences and 95% confidence bounds in
the relative to total plaque area between 7T and histology were: fibrous tissue 3.17 % (-3.17
to 9.52), LR/NC with haemorrhage -0.55% (-9.06 to 7.95), LR/NC without haemorrhage -
12.62% (-19.8 to -5.45), and calcification -2.43% (-9.97 to 4.73).
Conclusions
This study provides evidence that semi-automated segmentation of 3T/7T MRI techniques
can help to determine atherosclerotic plaque composition. In particular, the high resolution of
ex-vivo 7T data was able to highlight greater detail in the atherosclerotic plaque composition.
High field MRI may therefore have advantages for in vivo carotid plaque MR imaging
PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation
With the advent of convolutional neural networks~(CNN), supervised learning
methods are increasingly being used for whole brain segmentation. However, a
large, manually annotated training dataset of labeled brain images required to
train such supervised methods is frequently difficult to obtain or create. In
addition, existing training datasets are generally acquired with a homogeneous
magnetic resonance imaging~(MRI) acquisition protocol. CNNs trained on such
datasets are unable to generalize on test data with different acquisition
protocols. Modern neuroimaging studies and clinical trials are necessarily
multi-center initiatives with a wide variety of acquisition protocols. Despite
stringent protocol harmonization practices, it is very difficult to standardize
the gamut of MRI imaging parameters across scanners, field strengths, receive
coils etc., that affect image contrast. In this paper we propose a CNN-based
segmentation algorithm that, in addition to being highly accurate and fast, is
also resilient to variation in the input acquisition. Our approach relies on
building approximate forward models of pulse sequences that produce a typical
test image. For a given pulse sequence, we use its forward model to generate
plausible, synthetic training examples that appear as if they were acquired in
a scanner with that pulse sequence. Sampling over a wide variety of pulse
sequences results in a wide variety of augmented training examples that help
build an image contrast invariant model. Our method trains a single CNN that
can segment input MRI images with acquisition parameters as disparate as
-weighted and -weighted contrasts with only -weighted training
data. The segmentations generated are highly accurate with state-of-the-art
results~(overall Dice overlap), with a fast run time~( 45
seconds), and consistent across a wide range of acquisition protocols.Comment: Typo in author name corrected. Greves -> Grev
- …