25,123 research outputs found
Mammographic image restoration using maximum entropy deconvolution
An image restoration approach based on a Bayesian maximum entropy method
(MEM) has been applied to a radiological image deconvolution problem, that of
reduction of geometric blurring in magnification mammography. The aim of the
work is to demonstrate an improvement in image spatial resolution in realistic
noisy radiological images with no associated penalty in terms of reduction in
the signal-to-noise ratio perceived by the observer. Images of the TORMAM
mammographic image quality phantom were recorded using the standard
magnification settings of 1.8 magnification/fine focus and also at 1.8
magnification/broad focus and 3.0 magnification/fine focus; the latter two
arrangements would normally give rise to unacceptable geometric blurring.
Measured point-spread functions were used in conjunction with the MEM image
processing to de-blur these images. The results are presented as comparative
images of phantom test features and as observer scores for the raw and
processed images. Visualization of high resolution features and the total image
scores for the test phantom were improved by the application of the MEM
processing. It is argued that this successful demonstration of image
de-blurring in noisy radiological images offers the possibility of weakening
the link between focal spot size and geometric blurring in radiology, thus
opening up new approaches to system optimization.Comment: 18 pages, 10 figure
Automatic detection and extraction of artificial text in video
A significant challenge in large multimedia databases is the
provision of efficient means for semantic indexing and retrieval of visual information. Artificial text in video is normally generated in order to supplement or summarise the visual content and thus is an important carrier of information that is highly relevant to the content of the video. As such, it is a potential ready-to-use source of semantic information. In this paper we present an algorithm for detection and localisation of artificial text in video using a horizontal difference magnitude measure and morphological processing. The result of character segmentation, based on a modified version of the Wolf-Jolion
algorithm [1][2] is enhanced using smoothing and multiple
binarisation. The output text is input to an “off-the-shelf” noncommercial OCR. Detection, localisation and recognition results for a 20min long MPEG-1 encoded television programme are presented
Sequential Adaptive Detection for In-Situ Transmission Electron Microscopy (TEM)
We develop new efficient online algorithms for detecting transient sparse
signals in TEM video sequences, by adopting the recently developed framework
for sequential detection jointly with online convex optimization [1]. We cast
the problem as detecting an unknown sparse mean shift of Gaussian observations,
and develop adaptive CUSUM and adaptive SSRS procedures, which are based on
likelihood ratio statistics with post-change mean vector being online maximum
likelihood estimators with . We demonstrate the meritorious performance
of our algorithms for TEM imaging using real data
Multiscale Astronomical Image Processing Based on Nonlinear Partial Differential Equations
Astronomical applications of recent advances in the field of nonastronomical image processing are presented. These innovative methods, applied to multiscale astronomical images, increase signal-to-noise ratio, do not smear point sources or extended diffuse structures, and are thus a highly useful preliminary step for detection of different features including point sources, smoothing of clumpy data, and removal of contaminants from background maps. We show how the new methods, combined with other algorithms of image processing, unveil fine diffuse structures while at the same time enhance detection of localized objects, thus facilitating interactive morphology studies and paving the way for the automated recognition and classification of different features. We have also developed a new application framework for astronomical image processing that implements some recent advances made in computer vision and modern image processing, along with original algorithms based on nonlinear partial differential equations. The framework enables the user to easily set up and customize an image-processing pipeline interactively; it has various common and new visualization features and provides access to many astronomy data archives. Altogether, the results presented here demonstrate the first implementation of a novel synergistic approach based on integration of image processing, image visualization, and image quality assessment
Modeling and Estimation for Real-Time Microarrays
Microarrays are used for collecting information about a large number of different genomic particles simultaneously. Conventional fluorescent-based microarrays acquire data after the hybridization phase. During this phase, the target analytes (e.g., DNA fragments) bind to the capturing probes on the array and, by the end of it, supposedly reach a steady state. Therefore, conventional microarrays attempt to detect and quantify the targets with a single data point taken in the steady state. On the other hand, a novel technique, the so-called real-time microarray, capable of recording the kinetics of hybridization in fluorescent-based microarrays has recently been proposed. The richness of the information obtained therein promises higher signal-to-noise ratio, smaller estimation error, and broader assay detection dynamic range compared to conventional microarrays. In this paper, we study the signal processing aspects of the real-time microarray system design. In particular, we develop a probabilistic model for real-time microarrays and describe a procedure for the estimation of target amounts therein. Moreover, leveraging on system identification ideas, we propose a novel technique for the elimination of cross hybridization. These are important steps toward developing optimal detection algorithms for real-time microarrays, and to understanding their fundamental limitations
Real-time DNA microarray analysis
We present a quantification method for affinity-based
DNA microarrays which is based on the
real-time measurements of hybridization kinetics.
This method, i.e. real-time DNA microarrays,
enhances the detection dynamic range of conventional
systems by being impervious to probe
saturation in the capturing spots, washing
artifacts, microarray spot-to-spot variations, and
other signal amplitude-affecting non-idealities. We
demonstrate in both theory and practice that the
time-constant of target capturing in microarrays,
similar to all affinity-based biosensors, is inversely
proportional to the concentration of the target
analyte, which we subsequently use as the fundamental
parameter to estimate the concentration
of the analytes. Furthermore, to empirically
validate the capabilities of this method in practical
applications, we present a FRET-based assay which
enables the real-time detection in gene expression
DNA microarrays
- …