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IEEE PROJECTS 201011-12
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AN ALGORITHM FOR INTELLIGIBILITY PREDICTION OF TIME-FREQUENCY WEIGHTED NOISY SPEECH
Audio, Speech, and Language Processing, IEEE Transactions on
ABSTRACT
In the development process of noise-reduction algorithms, an objective machine-driven intelligibility
measure which shows high correlation with speech intelligibility is of great interest. Besides reducing
time and costs compared to real listening experiments, an objective intelligibility measure could also
help provide answers on how to improve the intelligibility of noisy unprocessed speech.
In this paper, a short-time objective intelligibility measure (STOI) is presented, which shows high
correlation with the intelligibility of noisy and time–frequency weighted noisy speech (e.g., resulting
from noise reduction) of three different listening experiments.
In general, STOI showed better correlation with speech intelligibility compared to five other reference
objective intelligibility models. In contrast to other conventional intelligibility models which tend to rely
on global statistics across entire sentences, STOI is based on shorter time segments (386 ms).
Experiments indeed show that it is beneficial to take segment lengths of this order into account. In
addition, a free Matlab implementation is provided.
ADAPTIVE MULTISCALE COMPLEXITY ANALYSIS OF FETAL HEART RATE
Biomedical Engineering, IEEE Transactions on
ABSTRACT
Per partum fetal asphyxia is a major cause of neonatal morbidity and mortality. Fetal heart rate
monitoring plays an important role in early detection of acidosis, an indicator for asphyxia.
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This problem is addressed in this paper by introducing a novel complexity analysis of fetal heart rate
data, based on producing a collection of piecewise linear approximations of varying dimensions from
which a measure of complexity is extracted.
This procedure specifically accounts for the highly non-stationary context of labor by being adaptive and
multiscale. Using a reference dataset, made of real per partum fetal heart rate data, collected in situ and
carefully constituted by obstetricians, the behavior of the proposed approach is analyzed and illustrated.
Its performance is evaluated in terms of the rate of correct acidosis detection versus the rate of false
detection, as well as how early the detection is made. Computational cost is also discussed. The results
are shown to be extremely promising and further potential uses of the tool are discussed
TISSUE-SPECIFIC COMPARTMENTAL ANALYSIS FOR DYNAMIC CONTRAST-ENHANCED MR IMAGING OF
COMPLEX TUMORS
Medical Imaging, IEEE Transactions on
ABSTRACT
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides a noninvasive method for
evaluating tumor vasculature patterns based on contrast accumulation and washout. However, due to
limited imaging resolution and tumor tissue heterogeneity, tracer concentrations at many pixels often
represent a mixture of more than one distinct compartment.
This pixel-wise partial volume effect (PVE) would have profound impact on the accuracy of
pharmacokinetics studies using existing compartmental modeling (CM) methods. We therefore propose
a convex analysis of mixtures (CAM) algorithm to explicitly mitigate PVE by expressing the kinetics in
each pixel as a nonnegative combination of underlying compartments and subsequently identifying pure
volume pixels at the corners of the clustered pixel time series scatter plot simplex.
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The algorithm is supported theoretically by a well-grounded mathematical framework and practically by
plug-in noise filtering and normalization preprocessing. We demonstrate the principle and feasibility of
the CAM-CM approach on realistic synthetic data involving two functional tissue compartments, and
compare the accuracy of parameter estimates obtained with and without PVE elimination using CAM or
other relevant techniques.
Experimental results show that CAM-CM achieves a significant improvement in the accuracy of kinetic
parameter estimation.
We apply the algorithm to real DCE-MRI breast cancer data and observe improved pharmacokinetics
parameter estimation, separating tumor tissue into regions with differential tracer kinetics on a pixel-bypixel basis and revealing biologically plausible tumor tissue heterogeneity patterns.
This method combines the advantages of multivariate clustering, convex geometry analysis, and
compartmental modeling approaches. The open-source MATLAB software of CAM-CM is publicly
available from the Web.
CELLULAR NEURAL
RECONSTRUCTION
NETWORKS,
NAVIER-STOKES
EQUATION
AND
MICROARRAY
IMAGE
Image Processing, IEEE Transactions on
ABSTRACT
Despite the latest improvements in the microarray technology, many developments are needed
particularly in the image processing stage. Some hardware implementations of microarray image
processing have been proposed and proved to be a promising alternative to the currently available
software systems. However, the main drawback is the unsuitable addressing of the quantification of the
gene spots which depend on many assumptions.
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It is our aim in this paper to present a new Image Reconstruction algorithm using Cellular Neural
Network, which solves the Navier-Stokes equation. This algorithm offers a robust method to estimate
the background signal within the gene spot region.
Quantitative comparisons are carried out, between our approach and some available methods in terms
of objective standpoint. It is shown that the proposed algorithm gives highly accurate and realistic
measurements in a fully automated manner, and also, in a remarkably efficient time.
MEMORY-EFFICIENT ARCHITECTURE FOR HYSTERESIS THRESHOLDING AND OBJECT FEATURE
EXTRACTION
Image Processing, IEEE Transactions on
ABSTRACT
Hysteresis thresholding is a method that offers enhanced object detection. Due to its recursive nature, it
is time consuming and requires a lot of memory resources. This makes it avoided in streaming
processors with limited memory.
We propose two versions of a memory-efficient and fast architecture for hysteresis thresholding: a highaccuracy pixel-based architecture and a faster block-based one at the expense of some loss in the
accuracy. Both designs couple thresholding with connected component analysis and feature extraction
in a single pass over the image.
Unlike queue-based techniques, the proposed scheme treats candidate pixels almost as foreground until
objects complete; a decision is then made to keep or discard these pixels. This allows processing on the
fly, thus avoiding additional passes for handling candidate pixels and extracting object features.
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Moreover, labels are reused so only one row of compact labels is buffered. Both architectures are
implemented in MATLAB and VHDL. Simulation results on a set of real and synthetic images show that
the execution speed can attain an average increase up to 24× for the pixel-based and 52× for the blockbased when compared to s
A CLOSED-FORM APPROXIMATION OF THE EXACT UNBIASED INVERSE OF THE ANSCOMBE VARIANCESTABILIZING TRANSFORMATION
Image Processing, IEEE Transactions on
ABSTRACT
We presented an exact unbiased inverse of the Anscombe variance-stabilizing transformation and
showed that when applied to Poisson image denoising, the combination of variance stabilization and
state-of-the-art Gaussian denoising algorithms is competitive with some of the best Poisson denoising
algorithms.
We also provided a Matlab implementation of our method, where the exact unbiased inverse
transformation appears in non-analytical form. Here we propose a closed-form approximation of the
exact unbiased inverse, in order to facilitate the use of this inverse.
The proposed approximation produces results equivalent to those obtained with the accurate (nonanalytical) exact unbiased inverse, and thus notably better than one would get with the asymptotically
unbiased inverse transformation, which is commonly used in applications.
----
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IMPLEMENTATION OF NEURAL NETWORK CONTROLLED THREE-LEG VSC AND A TRANSFORMER AS
THREE-PHASE FOUR-WIRE DSTATCOM
Industry Applications, IEEE Transactions on
ABSTRACT
In this paper, a neural-network (NN)-controlled distribution static compensator (DSTATCOM) using a
dSPACE processor is implemented for power quality improvement in a three-phase four-wire
distribution system.
A three-leg voltage-source-converter (VSC)-based DSTATCOM with a zig-zag transformer is used for the
compensation of reactive power for voltage regulation or for power factor correction along with load
balancing, elimination of harmonic currents, and neutral current compensation at the point of common
coupling.
The Adaline (adaptive linear element)-based NN is used to implement the control scheme of the VSC.
This technique gives similar performance as that of other control techniques, but it is simple to
implement and has a fast response and gives nearly zero phase shift.
The zig-zag transformer is used for providing a path to the zero-sequence current in a three-phase fourwire distribution system. This reduces the complexity and also the cost of the DSTATCOM system.
The performance of the proposed DSTATCOM system is validated through simulations using MATLAB
software with its Simulink and Power System Blockset toolboxes and hardware implementation.
POSTURE CONTROL OF ELECTROMECHANICAL ACTUATOR-BASED THRUST VECTOR SYSTEM FOR
AIRCRAFT ENGINE
Industrial Electronics, IEEE Transactions on
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ABSTRACT
This paper deals with the dynamical modeling and posture control of the electromechanical actuator
(EMA)-based thrust vector control (TVC) system for aircraft engine. Addressing the issues of the large
inertia and low stiffness existed in the TVC system driven by EMA, this paper established a 2-DOF
mathematical model to describe EMA dynamic characteristics.
In order to overcome the influence of the motion coupling of the TVC-EMA existed in the pitching and
yawing channels, we presented a kind of dual-channel coordinated-control method which realizes the
trust vector control for the swung aircraft engine based on the inverse kinematics.
This control strategy uses the command Eulers angles transformation to solve the desired actuator
linear lengths, and tracks the desired lengths via the compound control law composed of robust PID
with the lead compensation and Bang-Bang control in the two actuators.
The hybrid experimental simulation system based on dSPACE was set up, the control parameters of the
compound control methods were confirmed by off-line simulation based on Matlab, and the load
experiments of circular motion and step response were implemented on the test system. The simulation
and test results show that the designed thrust vector controller can achieve the satisfactory control
performances.
MODELING, CONTROL AND MONITORING OF S3RS BASED HYDROGEN COOLING SYSTEM IN THERMAL
POWER PLANT
Industrial Electronics, IEEE Transactions on
ABSTRACT
The faster heat dissipation of generators in power plant call for hydrogen cooling, and water is used as
coolant to cool down the hot hydrogen which comes out from the hydrogen cooling system (HCS) at
generating end. Therefore, in large generating plants the process of cooling and coolant becomes an
integral part of the Heat Exchangers. Hence, requirement of a reliable hydrogen cooling system is a
must.
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This paper presents development and implementation of supervisory control and data acquisition
(SCADA) based process control and monitoring system. A novel method of Six Stage Standby Redundant
Structured (S3RS) HCS is proposed for the cooling of large generators in thermal power plant(s).
This proposed system is equally reliable for steam turbine based generating plants and Integrated
Gasification Combined Cycle (IGCC) plants. The entire process control and monitoring, popularly known
as human machine interface (HMI) of HCS has been developed and simulated on RSViewSE, a real-time
automation platform by Rockwell Automation. And, the system reliability of the proposed S3RS process
model is implemented using MATLAB
POWER LOSS COMPARISON OF SINGLE- AND TWO-STAGE GRID-CONNECTED PHOTOVOLTAIC SYSTEMS
Energy Conversion, IEEE Transactions on
ABSTRACT
This paper presents power loss comparison of single- and two-stage grid-connected photovoltaic (PV)
systems based on the loss factors of double line-frequency voltage ripple (DLFVR), fast irradiance
variation + DLFVR, fast dc load variation + DLFVR, limited operating voltage range + DLFVR, and overall
loss factor combination.
These loss factors will result in power deviation from the maximum power points. In this paper, both
single-stage and two-stage grid-connected PV systems are considered. All of the effects on a two-stage
system are insignificant due to an additional maximum power point tracker, but the tracker will reduce
the system efficiency typically about 2.5%.
The power loss caused by these loss factors in a single-stage grid-connected PV system is also around
2.5%; that is, a single-stage system has the merits of saving components and reducing cost, and does not
penalize overall system efficiency under certain operating voltage ranges. Simulation results with the
MATLAB software package and experimental results have confirmed the analysis.
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SIMPLE ANALYTICAL METHOD FOR DETERMINING PARAMETERS OF DISCHARGING BATTERIES
Energy Conversion, IEEE Transactions on
ABSTRACT
This paper derives simple and explicit formulas for computing the parameters of Thevenin's equivalent
circuit model for a discharging battery. The general Thevenin's equivalent circuit model has $n$ pairs of
parallel resistors and capacitors (nth-order model).
The main idea behind the new method is to transform the problem of solving a system of high-order
polynomial equations into one of solving several linear equations and a single-variable $n$th-order
polynomial equation, via some change of variables. The computation can be implemented with a simple
MATLAB code less than half-page long.
Experimental and computational results are obtained for three types of batteries: Li-polymer, lead--acid,
and nickel metal hydride. For all the tested batteries, the first-order models are not able to generate
voltage responses that closely match the measured responses, while second-order models can generate
well-matched responses. For some of the batteries, a third-order model can do a better job matching
the voltage responses.
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BOOSTING COLOR FEATURE SELECTION FOR COLOR FACE RECOGNITION
Image Processing, IEEE Transactions on
ABSTRACT
This paper introduces the new color face recognition (FR) method that makes effective use of boosting
learning as color-component feature selection framework. The proposed boosting color-component
feature selection framework is designed for finding the best set of color-component features from
various color spaces (or models), aiming to achieve the best FR performance for a given FR task.
In addition, to facilitate the complementary effect of the selected color-component features for the
purpose of color FR, they are combined using the proposed weighted feature fusion scheme.
The effectiveness of our color FR method has been successfully evaluated on the following five public
face databases (DBs): CMU-PIE, Color FERET, XM2VTSDB, SCface, and FRGC 2.0.
Experimental results show that the results of the proposed method are impressively better than the
results of other state-of-the-art color FR methods over different FR challenges including highly
uncontrolled illumination, moderate pose variation, and small resolution face images.
AUTOMATIC EXACT HISTOGRAM SPECIFICATION FOR CONTRAST ENHANCEMENT AND VISUAL SYSTEM
BASED QUANTITATIVE EVALUATION
Image Processing, IEEE Transactions on
ABSTRACT
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Histogram equalization, which aims at information maximization, is widely used in different ways to
perform contrast enhancement in images. In this paper, an automatic exact histogram specification
technique is proposed and used for global and local contrast enhancement of images.
The desired histogram is obtained by first subjecting the image histogram to a modification process and
then by maximizing a measure that represents increase in information and decrease in ambiguity. A new
method of measuring image contrast based upon local band-limited approach and center-surround
retinal receptive field model is also devised in this paper.
This method works at multiple scales (frequency bands) and combines the contrast measures obtained
at different scales using Lp-norm. In comparison to a few existing methods, the effectiveness of the
proposed automatic exact histogram specification technique in enhancing contrasts of images is
demonstrated through qualitative analysis and the proposed image contrast measure based quantitative
analysis.
HIGH DYNAMIC RANGE IMAGE DISPLAY WITH HALO AND CLIPPING PREVENTION
Image Processing, IEEE Transactions on
ABSTRACT
The dynamic range of an image is defined as the ratio between the highest and the lowest luminance
level. In a high dynamic range (HDR) image, this value exceeds the capabilities of conventional display
devices; as a consequence, dedicated visualization techniques are required.
In particular, it is possible to process an HDR image in order to reduce its dynamic range without
producing a significant change in the visual sensation experienced by the observer. In this paper, we
propose a dynamic range reduction algorithm that produces high-quality results with a low
computational cost and a limited number of parameters.
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The algorithm belongs to the category of methods based upon the Retinex theory of vision and was
specifically designed in order to prevent the formation of common artifacts, such as halos around the
sharp edges and clipping of the highlights, that often affect methods of this kind.
After a detailed analysis of the state of the art, we shall describe the method and compare the results
and performance with those of two techniques recently proposed in the literature and one commercial
software.
GRADIENT PROFILE PRIOR AND ITS APPLICATIONS IN IMAGE SUPER-RESOLUTION AND ENHANCEMENT
Image Processing, IEEE Transactions on
ABSTRACT
In this paper, we propose a novel generic image prior-gradient profile prior, which implies the prior
knowledge of natural image gradients. In this prior, the image gradients are represented by gradient
profiles, which are 1-D profiles of gradient magnitudes perpendicular to image structures.
We model the gradient profiles by a parametric gradient profile model. Using this model, the prior
knowledge of the gradient profiles are learned from a large collection of natural images, which are
called gradient profile prior.
Based on this prior, we propose a gradient field transformation to constrain the gradient fields of the
high resolution image and the enhanced image when performing single image super-resolution and
sharpness enhancement. With this simple but very effective approach, we are able to produce state-ofthe-art results.
The reconstructed high resolution images or the enhanced images are sharp while have rare ringing or
jaggy artifacts
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EXPLORING DUPLICATED
REGIONS IN NATURAL IMAGES
Image Processing, IEEE Transactions on
ABSTRACT
Duplication of image regions is a common method for manipulating original images, using typical
software like Adobe Photoshop, 3DS MAX, etc. In this study, we propose a duplication detection
approach that can adopt two robust features based on discrete wavelet transform (DWT) and kernel
principal component analysis (KPCA). Both schemes provide excellent representations of the image data
for robust block matching.
Multiresolution wavelet coefficients and KPCA-based projected vectors corresponding to image-blocks
are arranged into a matrix for lexicographic sorting. Sorted blocks are used for making a list of similar
point-pairs and for computing their offset frequencies. Duplicated regions are then segmented by an
automatic technique that refines the list of corresponding point-pairs and eliminates the minimum
offset-frequency threshold parameter in the usual detection method.
A new technique that extends the basic algorithm for detecting Flip and Rotation types of forgeries is
also proposed. This method uses global geometric transformation and the labeling technique to
indentify the mentioned forgeries.
Experiments with a good number of natural images show very promising results, when compared with
the conventional PCA-based approach. A quantitative analysis indicate that the wavelet-based feature
outperforms PCA- or KPCA-based features in terms of average precision and recall in the noiseless, or
uncompressed domain, while KPCA-based feature obtains excellent performance in the additive noise
and lossy JPEG compression environments.
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SCALABLE FACE IMAGE RETRIEVAL WITH IDENTITY-BASED QUANTIZATION AND MULTI-REFERENCE RERANKING
ABSTRACT:
In this paper we aim to build a scalable face image retrieval system. For this purpose, we develop a new
scalable face representation using both local and global features. In the indexing stage, we exploit
special properties of faces to design new component based local features, which are subsequently
quantized into visual words using a novel identity-based quantization scheme.
We also use a very small Hamming signature (40 bytes) to encode the discriminative global feature for
each face. In the retrieval stage, candidate images are firstly retrieved from the inverted index of visual
words.
We then use a new multi-reference distance to re-rank the candidate images using the Hamming
signature. On a one million face database, we show that our local features and global Hamming
signatures are complementary—the inverted index based on local features provides candidate images
with good recall, while the multi-reference re-ranking with global Hamming signature leads to good
precision.
As a result, our system is not only scalable but also outperforms the linear scan retrieval system using
the state-of the- art face recognition feature in term of the quality.
ENHANCED ASSESSMENT OF THE WOUND-HEALING PROCESS BY ACCURATE MULTIVIEW TISSUE
CLASSIFICATION
ABSTRACT:
A pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by
pressure, shear, or friction. Diagnosis, treatment, and care of pressure ulcers are costly for health
services.
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Accurate wound evaluation is a critical task for optimizing the efficacy of treatment and care. Clinicians
usually evaluate each pressure ulcer by visual inspection of the damaged tissues, which is an imprecise
manner of assessing the wound state. Current computer vision approaches do not offer a global solution
to this particular problem.
In this paper, a hybrid approach based on neural networks and Bayesian classifiers is used in the design
of a computational system for automatic tissue identification in wound images. We focus here on tissue
classification from color and texture region descriptors computed after unsupervised segmentation. Due
to perspective distortions, uncontrolled lighting conditions and view points, wound assessments vary
significantly between patient examinations.
The experimental classification tests demonstrate that enhanced repeatability and robustness are
obtained and that metric assessment is achieved through real area and volume measurements and
wound outline extraction.
FACE RECOGNITION BY EXPLORING INFORMATION JOINTLY IN SPACE, SCALE AND ORIENTATION
ABSTRACT:
Information jointly contained in image space, scale and orientation domains can provide rich important
clues not seen in either individual of these domains. The position, spatial frequency and orientation
selectivity properties are believed to have an important role in visual perception.
This paper proposes a novel face representation and recognition approach by exploring information
jointly in image space, scale and orientation domains. Specifically, the face image is first decomposed
into different scale and orientation responses by convolving multiscale and multiorientation Gabor
filters.
Second, local binary pattern analysis is used to describe the neighboring relationship not only in image
space, but also in different scale and orientation responses. This way, information from different
domains is explored to give a good face representation for recognition. Neural Networks provide
significant benefits in face recognition.
They are actively being used for such advantages as locating previously undetected patterns, controlling
devices based on feedback, and detecting characteristics in face recognition. It improves the level of
accuracy compared with existing face recognition methods.
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MIXTURE OF GAUSSIANS-BASED BACKGROUND SUBTRACTION FOR BAYER-PATTERN IMAGE SEQUENCES
ABSTRACT:
This letter proposes a background subtraction method for Bayer-pattern image sequences. The
proposed method models the background in a Bayer-pattern domain using a mixture of Gaussians
(MoG) and classifies the foreground in an interpolated red, green, and blue (RGB) domain.
This method can achieve almost the same accuracy as MoG using RGB color images while maintaining
computational resources (time and memory) similar to MoG using grayscale images.
Experimental results show that the proposed method is a good solution to obtain high accuracy and low
resource requirements simultaneously.
This improvement is important for a low-level task like background subtraction since its accuracy affects
the performance of high-level tasks, and is preferable for implementation in real-time embedded
systems such as smart cameras.
NO-REFERENCE METRIC DESIGN WITH MACHINE LEARNING FOR LOCAL VIDEO COMPRESSION ARTIFACT
LEVEL
ABSTRACT
In decoded digital video, the local perceptual compression artifact level depends on the global
compression ratio and the local video content. In this paper, we show how to build a highly relevant
metric for video compression artifacts using supervised learning.
To obtain the ground truth for training, we first build a reference metric for local estimation of the
artifact level, which is robust to scaling and sensitive to all types of compression artifacts. Next, we
design a large feature set and use AdaBoost to create no-reference metrics trained with the output of
the reference metric.
Two separate trained no-reference metrics, one for flat and one for detailed areas, respectively, are
necessary to cover all types of artifacts. The relevance of these metrics is validated in a compression
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artifact reduction application, using objective scores like PSNR and BIM, but also a subjective evaluation
as proof.
We conclude that our created reference metric is an accurate local estimator of the compression artifact
level. We were able to copy the performance to two no-reference metrics, based on a weighted mixture
of low-level features.
A NOVEL 3-D COLOR HISTOGRAM EQUALIZATION METHOD WITH UNIFORM 1-D GRAY SCALE
HISTOGRAM
ABSTRACT:
The majority of color histogram equalization methods do not yield uniform histogram in gray scale. After
converting a color histogram equalized image into gray scale, the contrast of the converted image is
worse than that of an 1-D gray scale histogram equalized image.
We propose a novel 3-D color histogram equalization method that produces uniform distribution in gray
scale histogram by defining a new cumulative probability density function in 3-D color space.
Test results with natural and synthetic images are presented to compare and analyze various color
histogram equalization algorithms based upon 3-D color histograms. We also present theoretical
analysis for nonideal performance of existing methods.
COLOR EXTENDED VISUAL CRYPTOGRAPHY USING ERROR DIFFUSION
ABSTRACT:
Color visual cryptography (VC) encrypts a color secret message into color halftone image shares.
Previous methods in the literature show good results for black and white or gray scale VC schemes,
however, they are not sufficient to be applied directly to color shares due to different color structures.
Some methods for color visual cryptography are not satisfactory in terms of producing either
meaningless shares or meaningful shares with low visual quality, leading to suspicion of encryption.
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This paper introduces the concept of visual information pixel (VIP) synchronization and error diffusion to
attain a color visual cryptography encryption method that produces meaningful color shares with high
visual quality.
VIP synchronization retains the positions of pixels carrying visual information of original images
throughout the color channels and error diffusion generates shares pleasant to human eyes.
Comparisons with previous approaches show the superior performance of the new method.
A NEW SUPERVISED METHOD FOR BLOOD VESSEL SEGMENTATION IN RETINAL IMAGES BY USING GRAYLEVEL AND MOMENT INVARIANTS-BASED FEATURES
ABSTRACT:
This paper presents a new supervised method for blood vessel detection in digital retinal images. This
method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed
of gray-level and moment invariants-based features for pixel representation.
The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this
purpose, since they contain retinal images where the vascular structure has been precisely marked by
experts. Method performance on both sets of test images is better than other existing solutions in
literature.
The method proves especially accurate for vessel detection in STARE images. Its application to this
database (even when the NN was trained on the DRIVE database) outperforms all analyzed
segmentation approaches.
Its effectiveness and robustness with different image conditions, together with its simplicity and fast
implementation, make this blood vessel segmentation proposal suitable for retinal image computer
analyses such as automated screening for early diabetic retinopathy detection.
USING A VISUAL DISCRIMINATION MODEL FOR THE DETECTION OF COMPRESSION ARTIFACTS IN
VIRTUAL PATHOLOGY IMAGES
ABSTRACT:
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A major issue in telepathology is the extremely large and growing size of digitized “virtual” slides, which
can require several gigabytes of storage and cause significant delays in data transmission for remote
image interpretation and interactive visualization by pathologists. Compression can reduce this massive
amount of virtual slide data, but reversible (lossless) methods limit data reduction to less than 50%,
while lossy compression can degrade image quality and diagnostic accuracy.
“Visually lossless” compression offers the potential for using higher compression levels without
noticeable artifacts, but requires a rate-control strategy that adapts to image content and loss visibility.
We investigated the utility of a visual discrimination model (VDM) and other distortion metrics for
predicting JPEG 2000 bit rates corresponding to visually lossless compression of virtual slides for breast
biopsy specimens.
Threshold bit rates were determined experimentally with human observers for a variety of tissue
regions cropped from virtual slides. For test images compressed to their visually lossless thresholds, justnoticeable difference (JND) metrics computed by the VDM were nearly constant at the 95th percentile
level or higher, and were significantly less variable than peak signal-to-noise ratio (PSNR) and structural
similarity (SSIM) metrics.
Our results suggest that VDM metrics could be used to guide the compression of virtual slides to achieve
visually lossless compression while providing 5–12 times the data reduction of reversible methods.
DETECTION OF ARCHITECTURAL DISTORTION IN PRIOR MAMMOGRAMS
ABSTRACT:
We present methods for the detection of sites of architectural distortion in prior mammograms of
interval-cancer cases. We hypothesize that screening mammograms obtained prior to the detection of
cancer could contain subtle signs of early stages of breast cancer, in particular, architectural distortion.
The methods are based upon Gabor filters, phase portrait analysis, a novel method for the analysis of
the angular spread of power, fractal analysis, Laws’ texture energy measures derived from geometrically
transformed regions of interest (ROIs), and Haralick’s texture features. With Gabor filters and phase
portrait analysis, 4224 ROIs were automatically obtained from 106 prior mammograms of 56 interval-
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cancer cases, including 301 true-positive ROIs related to architectural distortion, and from 52
mammograms of 13 normal cases.
For each ROI, the fractal dimension, the entropy of the angular spread of power, 10 Laws’ measures, and
Haralick’s 14 features were computed. The areas under the receiver operating characteristic curves
obtained using the features selected by stepwise logistic regression and the leave-one-ROI-out method
are 0.76 with the Bayesian classifier, 0.75 with Fisher linear discriminant analysis, and 0.78 with a singlelayer feed-forward neural network.
Free-response receiver operating characteristics indicated sensitivities of 0.80 and 0.90 at 5.8 and 8.1
false positives per image, respectively, with the Bayesian classifier and the leave-one-image-out method.
A DIFFERENTIAL GEOMETRIC APPROACH TO AUTOMATED SEGMENTATION OF HUMAN AIRWAY TREE
ABSTRACT:
Airway diseases are frequently associated with morphological changes that may affect the physiology of
the lungs. Accurate characterization of airways may be useful for quantitatively assessing prognosis and
for monitoring therapeutic efficacy.
The information gained may also provide insight into the underlying mechanisms of various lung
diseases. We developed a computerized scheme to automatically segment the 3-D human airway tree
depicted on computed tomography (CT) images.
The method takes advantage of both principal curvatures and principal directions in differentiating
airways from other tissues in geometric space. A “puzzle game” procedure is used to identify false
negative regions and reduce false positive regions that do not meet the shape analysis criteria.
The negative impact of partial volume effects on small airway detection is partially alleviated by
repeating the developed differential geometric analysis on lung anatomical structures modeled at
multiple iso-values (thresholds).
In addition to having advantages, such as full automation, easy implementation and relative insensitivity
to image noise and/or artifacts, this scheme has virtually no leakage issues and can be easily extended
to the extraction or the segmentation of other tubular type structures (e.g., vascular tree).
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The performance of this scheme was assessed quantitatively using 75 chest CT examinations acquired
on 45 subjects with different slice thicknesses and using 20 publicly available test cases that were
originally designed for evaluating the performance of different airway tree segmentation algorithms.
A SUPERVISED FRAMEWORK FOR THE REGISTRATION AND SEGMENTATION OF WHITE MATTER FIBER
TRACTS
ABSTRACT:
A supervised framework is presented for the automatic registration and segmentation of white matter
(WM) tractographies extracted from brain DT-MRI. The framework relies on the direct registration
between the fibers, without requiring any intensity-based registration as preprocessing.
An affine transform is recovered together with a set of segmented fibers. A recently introduced
probabilistic boosting tree classifier is used in a segmentation refinement step to improve the precision
of the target tract segmentation.
The proposed method compares favorably with a state-of-the-art intensity-based algorithm for affine
registration of DTI tractographies. Segmentation results for 12 major WM tracts are demonstrated.
Quantitative results are also provided for the segmentation of a particularly difficult case, the optic
radiation tract. An average precision of 80% and recall of 55% were obtained for the optimal
configuration of the presented method.
CURVATURE INTERPOLATION METHOD FOR IMAGE ZOOMING
ABSTRACT:
We introduce a novel image zooming algorithm, called the curvature interpolation method (CIM), which
is partial- differential-equation (PDE)-based and easy to implement. In order to minimize artifacts arising
in image interpolation such as image blur and the checkerboard effect, the CIM first evaluates the
curvature of the low-resolution image.
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After interpolating the curvature to the high-resolution image domain, the CIM constructs the highresolution image by solving a linearized curvature equation, incorporating the interpolated curvature as
an explicit driving force.
It has been numerically verified that the new zooming method can produce clear images of sharp edges
which are already denoised and superior to those obtained from linear methods and PDE-based
methods of no curvature information. Various results are given to prove effectiveness and reliability of
the new method.
IMAGE RESOLUTION ENHANCEMENT BY USING DISCRETE AND STATIONARY WAVELET DECOMPOSITION
ABSTRACT:
In this correspondence, the authors propose an image resolution enhancement technique based on
interpolation of the high frequency subband images obtained by discrete wavelet transform (DWT) and
the input image.
The edges are enhanced by introducing an intermediate stage by using stationary wavelet transform
(SWT). DWT is applied in order to decompose an input image into different subbands. Then the high
frequency subbands as well as the input image are interpolated.
The estimated high frequency subbands are being modified by using high frequency subband obtained
through SWT. Then all these subbands are combined to generate a new high resolution image by using
inverse DWT (IDWT).
The quantitative and visual results are showing the superiority of the proposed technique over the
conventional and state-of-art image resolution enhancement techniques.
TEXT SEGMENTATION FOR MRC DOCUMENT COMPRESSION
ABSTRACT:
The mixed raster content (MRC) standard (ITU-T T.44) specifies a framework for document compression
which can dramatically improve the compression/quality tradeoff as compared to traditional lossy image
compression algorithms.
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The key to MRC compression is the separation of the document into foreground and background layers,
represented as a binary mask. Therefore, the resulting quality and compression ratio of a MRC
document encoder is highly dependent upon the segmentation algorithm used to compute the binary
mask.
In this paper, we propose a novel multiscale segmentation scheme for MRC document encoding based
upon the sequential application of two algorithms. The first algorithm, cost optimized segmentation
(COS), is a blockwise segmentation algorithm formulated in a global cost optimization framework.
The second algorithm, connected component classification (CCC), refines the initial segmentation by
classifying feature vectors of connected components using an Markov random field (MRF) model. The
combined COS/CCC segmentation algorithms are then incorporated into a multiscale framework in
order to improve the segmentation accuracy of text with varying size.
In comparisons to state-of-the-art commercial MRC products and selected segmentation algorithms in
the literature, we show that the new algorithm achieves greater accuracy of text detection but with a
lower false detection rate of nontext features.
We also demonstrate that the proposed segmentation algorithm can improve the quality of decoded
documents while simultaneously lowering the bit rate.
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