240 research outputs found
PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI
In this paper we present a novel method for the correction of motion
artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of
the whole uterus. Contrary to current slice-to-volume registration (SVR)
methods, requiring an inflexible anatomical enclosure of a single investigated
organ, the proposed patch-to-volume reconstruction (PVR) approach is able to
reconstruct a large field of view of non-rigidly deforming structures. It
relaxes rigid motion assumptions by introducing a specific amount of redundant
information that is exploited with parallelized patch-wise optimization,
super-resolution, and automatic outlier rejection. We further describe and
provide an efficient parallel implementation of PVR allowing its execution
within reasonable time on commercially available graphics processing units
(GPU), enabling its use in the clinical practice. We evaluate PVR's
computational overhead compared to standard methods and observe improved
reconstruction accuracy in presence of affine motion artifacts of approximately
30% compared to conventional SVR in synthetic experiments. Furthermore, we have
evaluated our method qualitatively and quantitatively on real fetal MRI data
subject to maternal breathing and sudden fetal movements. We evaluate
peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and
cross correlation (CC) with respect to the originally acquired data and provide
a method for visual inspection of reconstruction uncertainty. With these
experiments we demonstrate successful application of PVR motion compensation to
the whole uterus, the human fetus, and the human placenta.Comment: 10 pages, 13 figures, submitted to IEEE Transactions on Medical
Imaging. v2: wadded funders acknowledgements to preprin
Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI
PURPOSE: We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). METHODS: The method is based on superpixel technique and classification of each superpixel. A number of novel image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. RESULTS: The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48Â %, 6Â % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09Â %, 6Â % and 0.88, respectively. CONCLUSIONS: This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management
BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis
Emergency events involving fire are potentially harmful, demanding a fast and
precise decision making. The use of crowdsourcing image and videos on crisis
management systems can aid in these situations by providing more information
than verbal/textual descriptions. Due to the usual high volume of data,
automatic solutions need to discard non-relevant content without losing
relevant information. There are several methods for fire detection on video
using color-based models. However, they are not adequate for still image
processing, because they can suffer on high false-positive results. These
methods also suffer from parameters with little physical meaning, which makes
fine tuning a difficult task. In this context, we propose a novel fire
detection method for still images that uses classification based on color
features combined with texture classification on superpixel regions. Our method
uses a reduced number of parameters if compared to previous works, easing the
process of fine tuning the method. Results show the effectiveness of our method
of reducing false-positives while its precision remains compatible with the
state-of-the-art methods.Comment: 8 pages, Proceedings of the 28th SIBGRAPI Conference on Graphics,
Patterns and Images, IEEE Pres
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