48,844 research outputs found
Shape-Based Models for Interactive Segmentation of Medical Images
Accurate image segmentation is one of the key problems in computer vision. In domains such as radiation treatment planning, dosimetrists must manually trace the outlines of a few critical structures on large numbers of images. Considerable similarity can be seen in the shape of these regions, both between adjacent slices in a particular patient and across the spectrum of patients. Consequently we should be able to model this similarity and use it to assist in the process of segmentation. Previous work has demonstrated that a constraint-based 2D radial model can capture generic shape information for certain shape classes, and can reduce user interaction by a factor of three over purely manual segmentation. Additional simulation studies have shown that a probabilistic version of the model has the potential to further reduce user interaction. This paper describes an implementation of both models in a general-purpose imaging and graphics framework and compares the usefulness of the models on several shape classes
Performance of the OPAL Si-W luminometer at LEP I-II
A pair of compact Silicon-Tungsten calorimeters was operated in the OPAL
experiment at LEP to measure the integrated luminosity from detection of Bhabha
electrons scattered at angles between 25 and 58 mrad from the beam line. In the
eight years from 1993 to 2000 the detector worked first at the Z mass peak and
then at center of mass energies up to 209 GeV. The fine radial and longitudinal
segmentation (2.5mm x 1X0) allowed the radial position of electron and photon
showers to be measured with a resolution of 130-170 microns and a residual
radial bias as small as 7 microns. Reducing the bias in the definition of the
inner acceptance radius was the key element in obtaining an experimental
systematic error on the integrated luminosity of only 3.4 10^-4. The
performance of the detector at both LEP-I and LEP-II is reviewed. Energy
resolution, sensitivity to overlapping electromagnetic showers and sensitivity
to minimum ionizing particles are discussed.Comment: 9 pages, 7 figures, 10th International Conference on Calorimetry in
High Energy Physics. http://3w.hep.caltech.edu/calor02
User-initialized active contour segmentation and golden-angle real-time cardiovascular magnetic resonance enable accurate assessment of LV function in patients with sinus rhythm and arrhythmias.
BackgroundData obtained during arrhythmia is retained in real-time cardiovascular magnetic resonance (rt-CMR), but there is limited and inconsistent evidence to show that rt-CMR can accurately assess beat-to-beat variation in left ventricular (LV) function or during an arrhythmia.MethodsMulti-slice, short axis cine and real-time golden-angle radial CMR data was collected in 22 clinical patients (18 in sinus rhythm and 4 patients with arrhythmia). A user-initialized active contour segmentation (ACS) software was validated via comparison to manual segmentation on clinically accepted software. For each image in the 2D acquisitions, slice volume was calculated and global LV volumes were estimated via summation across the LV using multiple slices. Real-time imaging data was reconstructed using different image exposure times and frame rates to evaluate the effect of temporal resolution on measured function in each slice via ACS. Finally, global volumetric function of ectopic and non-ectopic beats was measured using ACS in patients with arrhythmias.ResultsACS provides global LV volume measurements that are not significantly different from manual quantification of retrospectively gated cine images in sinus rhythm patients. With an exposure time of 95.2 ms and a frame rate of > 89 frames per second, golden-angle real-time imaging accurately captures hemodynamic function over a range of patient heart rates. In four patients with frequent ectopic contractions, initial quantification of the impact of ectopic beats on hemodynamic function was demonstrated.ConclusionUser-initialized active contours and golden-angle real-time radial CMR can be used to determine time-varying LV function in patients. These methods will be very useful for the assessment of LV function in patients with frequent arrhythmias
Deep Structured Features for Semantic Segmentation
We propose a highly structured neural network architecture for semantic
segmentation with an extremely small model size, suitable for low-power
embedded and mobile platforms. Specifically, our architecture combines i) a
Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random
layer realizing a radial basis function kernel approximation, and iii) a linear
classifier. While stages i) and ii) are completely pre-specified, only the
linear classifier is learned from data. We apply the proposed architecture to
outdoor scene and aerial image semantic segmentation and show that the accuracy
of our architecture is competitive with conventional pixel classification CNNs.
Furthermore, we demonstrate that the proposed architecture is data efficient in
the sense of matching the accuracy of pixel classification CNNs when trained on
a much smaller data set.Comment: EUSIPCO 2017, 5 pages, 2 figure
Straight to Shapes: Real-time Detection of Encoded Shapes
Current object detection approaches predict bounding boxes, but these provide
little instance-specific information beyond location, scale and aspect ratio.
In this work, we propose to directly regress to objects' shapes in addition to
their bounding boxes and categories. It is crucial to find an appropriate shape
representation that is compact and decodable, and in which objects can be
compared for higher-order concepts such as view similarity, pose variation and
occlusion. To achieve this, we use a denoising convolutional auto-encoder to
establish an embedding space, and place the decoder after a fast end-to-end
network trained to regress directly to the encoded shape vectors. This yields
what to the best of our knowledge is the first real-time shape prediction
network, running at ~35 FPS on a high-end desktop. With higher-order shape
reasoning well-integrated into the network pipeline, the network shows the
useful practical quality of generalising to unseen categories similar to the
ones in the training set, something that most existing approaches fail to
handle.Comment: 16 pages including appendix; Published at CVPR 201
Optic nerve head segmentation
Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 /spl mu//pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent-Fair performance in 83% of cases, and performs well even on blurred image
Pulse shape analysis in segmented detectors as a technique for background reduction in Ge double-beta decay experiments
The need to understand and reject backgrounds in Ge-diode detector
double-beta decay experiments has given rise to the development of pulse shape
analysis in such detectors to discern single-site energy deposits from
multiple-site deposits. Here, we extend this analysis to segmented Ge detectors
to study the effectiveness of combining segmentation with pulse shape analysis
to identify the multiplicity of the energy deposits.Comment: 12 pages, 13 figures, will be submitted to NI
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