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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5829_Библиотеки_им_академика_М_И_Перельмана.pdf
Subject Index 351
homogenous plaques, 167, 168
symptomatic and asymptomatic
plaques, univariate analysis, 167,
168
gray-scale median, 172
HDU, 164
histogram parameters, 165
isotope-labelled molecules, 164
morphologic and histologic markers, 165,
166
multicenter trials, 171
STATA 4.0 software, 165
variance analysis and student’s test,
165–166
CAUDLES-EF. See Carotid automated
ultrasound double line extraction
system-edge flow
CBC. See Clinical-based classifier
CCA. See Common carotid artery
Chronic liver disease (CLD)
CBC decomposition strategy, 259
clinical and pathophysiological
characteristics, 268
definition, 256
stages of, 256–257
Circular symmetric complex Gaussian
(CSCG), 5
Cirrhosis, 256–257
compensated, 256, 258, 269, 271–277
decompensated, 256, 258, 269, 271–277
CLD. See Chronic liver disease
Clinical-based classifier (CBC)
biochemical and clinical features, 267–269
clinical data set, 269–270
feature extraction and selection, 270–272
kNN classifier, 272–274
SVM classifier
Gaussian radial-basis kernel, 275–277
polynomial kernel, 274–275
ultrasound image pre-processing
acoustic attenuation coefficient,
261–263
autoregressive model, 265–267
co-occurrence tensor, 263–264
first-order statistics, 263
liver surface contour, 267
wavelet transform, 264–265
Common carotid artery (CCA)
acoustic shadowing, 123
atherosclerosis, 100
Bland–Altman plot, 114, 121
blood–endothelium (intima) interface, 122
childhood-onset chronic kidney disease,
121
CVD, 100
GSM, 103
IL (see Intima layer)
image normalization, 104
IMC (see Intima–media complex)
inter-and intra-observer variability, 100
Mann–Whitney rank sum test, 108, 111
manual measurements, 105, 114
manual vs. snakes segmented IMT
measurement, 117, 120
media–adventitia interface, 122
ML (see Media layer)
regression analysis, 108
risk factors, 124
snakes deformation, 123
statistical analysis, 107
texture analysis, 106
texture features
FCH, 122
GSM, 115
IL vs. ML, 116, 117
Mann–Whitney rank sum test, 117–119
median and inter-quartile range, 115,
116
Wilcoxon rank sum test, 115, 116
two-dimensional ultrasound transducer,
120
ultrasound image recording, 104
Wilcoxon rank sum test, 108–110, 112–113
Compensated cirrhosis, 256, 258, 269,
271–277
Continuous mode ultrasound testing, 288–292
Co-occurrence tensor, 263–264
Coronary atherosclerotic plaque
acoustic properties, 180
cross-validation scheme, 193
data fusion process, 190
data selection process, 193–194
enhanced data set requirements, 190
ex vivo coronary section, histological
image, 178
grey-level intensity and textures, 180
image-based features, 181–182
integrated backscatter parameter/wavelet
coefficients, 179
intima and adventitia layer, 179
intimal-medial segments, 177
in vitro data validation procedure, 179–180
J function design, 195
leave one patient out technique, 190
N-fold cross-validation, 189
pattern recognition, 180
radio frequency-based features
acoustic impedance, 182
352 Subject Index
Coronary atherosclerotic plaque (cont.)
integrated backscatter, 182–183
power spectrum, 183–184
textural and spectral features, 184
tissue properties, 182
wavelet-based approach, 184
SFFS algorithm, 191–193
test in vitro, 196–197
test in vivo, 197–198
tissue classification
decision tree, 185–186
ECOC, 186–187
ensemble architecture, 187–189
feature space, 185
probabilistic model, 185
tissue morphology and composition, IVUS,
178
Coupled active geometric functions, 245–247
Cryoblation, prostate
contraindications, 303
cryotherapy, 299–300
imaging techniques, 302–303
indications, 303
Joule–Thompson effect, 303–304
long-term complications, 308–309
post-operative follow-up, 306–307
real-time transrectal ultrasonography,
301–302
short-term complications, 308
treatment technique, 303–306
CSCG. See Circular symmetric complex
Gaussian
D
DAQ systems
free-hand ultrasound, 207–208
real time data acquisition and visualization,
204
robotic arm prototype, 205–207
Decompensated cirrhosis, 256, 258, 269,
271–277
Decomposition coefficients, 328
Denoising algorithm, 261
Derivative of Gaussian (GD), 135–136
Detailed plaque texture analysis (DPTA), 172
Difference of offset Gaussian (DOOG),
135–136
Discrete wavelet packet frame (DWPF), 326
Double line (LI/MA) border estimation
edge flow magnitude and direction
edge flow vector, 135
flow propagation and boundary
detection, 139–141
GD and DOOG, 135–136
intensity edge flow, 136–138
parameters, 136
texture edge flow, 138–139
grayscale guidance zone, 134
guidance zone mask estimation, 135
weak LI and LI missing edge estimation,
144–146
weak MA/missing MA edge estimation
area ratio, 142
disconnected edge objects, 141
edge object classification, 143–144
incorrect edge objects, 141, 142
small edge objects, 141–143
E
ECOC framework. See Error-correcting output
code framework
Elasticity imaging, 302
EM algorithm. See Expectation maximization
algorithm
Envelope radio frequency (ERF)
data histogram extraction, 15, 17, 19
Gamma and Rayleigh distributions, 13, 20
GoF test, 5
gray-scale image appearance, 18
image retrieval method, 12
Kullback–Leibler distances, 19, 20
LCM, 7–8
nonlinear compression model, 6
Rayleigh statistics, 4, 5
RF image retrieval (decompression)
method, 15
Error-correcting output code (ECOC)
framework, 35, 186–187
Euler–Mascheroni constant, 10
Europen Carotid Stenosis Trial (ECST), 172
Expectation maximization (EM) algorithm, 28,
32
F
Familial hypercholesterolemia (FCH), 122
Far adventitia estimation
tracing, 134
AD
F
automatic recognition, 132–133
fine to coarse down-sampling, 133
Gaussian kernels, 134
higher order Gaussian derivative filter, 134
speckle reduction, 133
up-sampling, 134
Figure of merit (FoM), 86
Fisher-Tippet distribution, 10
Subject Index 353
G
Gabor filters, 137
Gaussian probability function, 50
Gaussian radial-basis kernel, 275–277
Generalized Gaussian distribution (GGD), 54
Gibbs distribution, 77
Goodness of Fit (GoF) test, 20, 22
Gradient vector flow (GVF), 226
Gray-scale median (GSM), 103
Guidance zone mask estimation, 135
H
Hammersley–Clifford theorem, 77
Hausdorff distance (HD), 147
Hepatitis, 256
Hessian matrix, 80
High definition ultrasonography (HDU), 164
Hilbert transform, 52
I
Intima layer (IL)
acoustic holes, 102
automated segmentation and measurement,
102
box plots, 114, 115
Mann–Whitney rank sum test, 108, 111,
117–119
manual measurements, 105, 108–110
median and inter-quartile range, 115, 116
vs. ML, texture characteristics, 116, 117
snakes segmentation, 105–106
statistical analysis, 107
texture analysis, 106
ultrasound image recording, 104
Wilcoxon rank sum test, 108, 112–113,
115, 116
Intima–media complex (IMC), 100, 101
box plots, 114, 115
Mann–Whitney rank sum test, 108, 111,
117–119
manual measurements, 105, 108–110
median and inter-quartile range, 115, 116
snakes segmentation, 105–106
statistical analysis, 107
texture analysis, 106
ultrasound image recording, 104
Wilcoxon rank sum test, 108, 112–113,
115, 116
Intima-media thickness (IMT). See Common
carotid artery
Intravascular ultrasound (IVUS) image, 62–63
albeit vulnerable plaque, 26
analog to digital converter, 26
automatic quantitative method, 27
data acquisition, 314–315
display, 314–315
EM algorithm, 28, 32
hypothetical acoustic tissue model, 27
image formation, 314–315
inner arterial wall, 26
lumen borders, automatic detection
brushlet analysis, 317–319
2.5-D magnitude–phase histogram,
320–321
experimental results, 323–324
frequency tiling and overcomplete
representation, 319
motivations, 316–317
signal modeling, 320
surface function actives, 322–323
monolithic description, 33
pixel-wise classification, 28
plaque echo-morphology, 28
RMM (see Rayleigh mixture model)
speckle formation, 28
speckle signal, 26
J
Joule–Thompson effect, 303–304
K
k-Nearest Neighbor (kNN) classifier, 272–274
Kullback–Leibler (KL) divergence, 38, 39
L
LCL. See Log–Compression Law
LCM. See Log–compression model
Lemarie-Battle wavelet, 329
Linear homogeneous mask area filter
(LHMAF), 84
Linear scaling filter (LSF), 84
Liver surface contour, 267
Local hypoechogenic region labeling
3D reconstruction, 215, 216
energy function, 214
gray-scale mapping, 218
heterogeneous plaque, 212
labeling procedure, 213
P40 3D maps, 216, 217
penalization function, 214
piecewise smooth region detection, 214,
215
Local hypoechogenic region labeling (cont.)
354 Subject Index
plaque texture, 217, 218
potential application, 216, 218
thresholding procedure, 215
Log–Compression Law (LCL), 4, 7–8
Log–compression model (LCM)
block diagram, ultrasound imaging system,
7
BUS image formation, 8, 9
compressed image statistics, 4
LCL, 7–8
Log-Euclidean potential function (LEPF), 79
Low-intensity ultrasound, 284
Lumen borders, automatic detection
brushlet analysis, 317–319
2.5-D magnitude–phase histogram,
320–321
experimental results, 323–324
frequency tiling and overcomplete
representation, 319
motivations, 316–317
signal modeling, 320
surface function actives, 322–323
Lumen–intima (LI) boundary, 130
M
MAP estimation. See Maximum a posteriori
estimation
Markov random field (MRF), 55
Maximum a posteriori (MAP) estimation, 54,
55
Maximum homogeneity over pixel
neighborhood filter (MHOPNF), 84
Maximum likelihood (ML) estimation, 32, 54
Mean preservation variance reduction (MPVR)
method, 85
Media–adventitia (MA) boundary, 130
Media layer (ML)
automated segmentation and measurement,
102
box plots, 114, 115
vs. IL, texture characteristics, 116, 117
Mann–Whitney rank sum test, 108, 111,
117–119
manual measurements, 105, 108–110
median and inter-quartile range, 115, 116
snakes segmentation, 105–106
statistical analysis, 107
texture analysis, 106
ultrasound image recording, 104
Wilcoxon rank sum test, 108, 112–113,
115, 116
Media layer thickness (MLT), 102
Median fiters (MF), 84
ML estimation. See Maximum likelihood
estimation
Modified Gabor filters (MGFs), 56, 84
Multi-channel wavelet analysis, 326–328
N
Non-local means filter (NLMF), 84
Non-parametric kNN classifier, 259–260
O
One-vs-One coding technique, 187
Oriented Speckle Reduction Anisotropic
Diffusion (OSRAD) algorithm, 55
filter robustness, 58, 61, 62
in vivo experiments
pairwise error comparison, 65–67
snake manual and automatic
segmentation, 63–65
normalized intensity profile, denoised
images, 58, 59
P
PDF. See Probability density function
Point spread function (PSF), 51
Polyline distance (PD), 147–148
Polynomial kernel, 274–275
Portal hypertension, 268
Probability density function (PDF), 20, 51
Prostate cryoblation
contraindications, 303
imaging techniques, 302–303
indications, 303
Joule–Thompson effect, 303–304
long-term complications, 308–309
post-operative follow-up, 306–307
real-time transrectal ultrasonography,
301–302
short-term complications, 308
treatment technique, 303–306
Pulsed mode ultrasound testing, 292–295
R
Radio frequency (RF) ultrasound estimation
compression law, 5
CSCG, 5
decompression method
anatomical structures/tissues, 15, 16
carotid plaque and contour, 20, 21
data histogram extraction, 15, 17–19
ERF, 12
Subject Index 355
gray-scale image appearance, 18
interpolation operation., 21
Kullback–Leibler distance, 19, 20
Monte Carlo tests, 13, 14
parameter estimation, 10–12
RF image retrieval application, 15
speckle noise contamination, 13
3D US reconstruction problem, 6
fractional moments iterative algorithm, 6
GoF test, 20, 22
intensity signal, 5
LCM
block diagram, ultrasound imaging
system, 7
BUS image formation, 8, 9
compressed image statistics, 4
LCL, 7–8
maximum likelihood estimation, 20
probability density function, 20
Rayleigh statistics, ERF, 4, 5
speckle recognition, BUS, 4
speckle reduction, 4
Rayleigh-log total variation (RLTV) filter, 75
Rayleigh mixture model (RMM)
ECOC framework, 35
features weight analysis, 44–45
in–vitro data processing, 33–35
Lagrange multiplier, 31
leave-one-patient-out technique, 35
likelihood function, 29–31
ML estimator, 32
PDFs, parameter, 29, 30
pixel intensity, 29
plaque echo-morphology, 32–33
plaque local characterization
C.1, C.2 classifiers, 40, 41
C.3 classifiers, 40–42
kernel size, 39, 40
plaque monolithic
KL divergence, 38, 39
mixture components, 37
monolithic classification, 37
SRM, 37–39
tissue types, 35, 36
statistical analysis, 43–44
Rayleigh probability function, 50
Real–time radio–frequency data, 33, 34
Reflection coefficient
continuous mode ultrasound testing,
291–292
pulsed mode ultrasound testing, 294–295
RF ultrasound estimation. See Radio frequency
ultrasound estimation
RMM. See Rayleigh mixture model
Robotic arm prototype system
acquisition modes, 206, 207
experimental robot setup, 206, 207
image acquisition process, 205
system components, 205, 206
S
Salvage cryotherapy, 303
Sequential floating forward selection (SFFS)
algorithm, 191–193
SFA. See Surface function actives
Single Rayleigh model (SRM), 37–39
Speckle reducing anisotropic diffusion (SRAD)
algorithm, 55, 58, 59
Speckle reducing anisotropic diffusion filter
(SRADF), 84
Squeeze box filter (SBF), 84
SRAD algorithm. See Speckle reducing
anisotropic diffusion algorithm
SRBF
filter robustness, 58, 61, 62
in vivo experiments
pairwise error comparison, 65–67
snake manual and automatic
segmentation, 63–65
normalized intensity profile, denoised
images, 58, 59
SRM. See Single Rayleigh model
Sum of square differences (SSD) criterion, 57
Support vector machine (SVM) classifier
Gaussian radial-basis kernel, 275–277
polynomial kernel, 274–275
Surface function actives (SFA), 322–323
Sylvester–Lyapunov equation, 55
T
TGC function. See Time gain compensation
(TGC) function
Three-dimensional ultrasound plaque
characterization
atherosclerosis, 203
carotid disease, 204
DAQ systems, 3D ultrasound
free-hand ultrasound, 207–208
real time data acquisition and
visualization, 204
robotic arm prototype, 205–207
gray-scale median and P
40
, 204
local hypoechogenic region labeling,
graph-cuts
3D reconstruction, 215, 216
356 Subject Index
Three-dimensional ultrasound plaque
characterization (cont.)
energy function, 214
gray-scale mapping, 218
heterogeneous plaque, 212
labeling procedure, 213
P40 3D maps, 216, 217
penalization function, 214
piecewise smooth region detection, 214,
215
plaque texture, 217, 218
potential application, 216, 218
thresholding procedure, 215
reconstruction method
data fidelity, 209
de-speckling algorithm, 209, 210
interpolation, 211
MAP, 209
Rayleigh distribution, 212
regularization parameter function, 211
surface rendering, 208–209
total variation, 209
volume rendering, 209
voxel representation, 210
Time gain compensation (TGC) function, 34
Tissue analysis. See Ultrasound
speckle/despeckle image
decomposition
U
Ultrasound despeckle method
anisotropic filters (see Anisotropic filters)
denoising process, 49
edge-preservation property, 50
speckle, definition, 49
speckle noise
Gaussian-weighted sinusoidal function,
52
Gaussian white noise, 52
PSF, 51
statistics, 50–51
ultrasound simulator, 51
speckle reduction methods
anisotropic diffusion filters, 55–56
Bayesian filters, 54–55
linear filters, 53
median filters, 53
wavelet-based filters, 54
Ultrasound speckle/despeckle image
decomposition, 75, 76
arterial diseases, 73
AWGN, 74
Bayesian approach, 74
constructive and destructive interference,
74
convex energy function, 79
convex optimization, 80, 81
data fidelity, 76
echogenicity decay, 82–83
echogenicity index, 82
echo-morphology and texture
characterization, 75
edge maps, 85, 87, 88
FOM, 86
Hammersley–Clifford theorem, 77
Hessian matrix, 80
image intensity profiles, 85, 87, 89
IVUS image, 86, 89, 91
Kolmogorov–Smirnov conformity test, 85
LEPF, 79
linear filters, 84
line search algorithm, 79
liver steatosis binary classification, 91, 92
local Rayleigh estimator, 82
Log-Euclidean prior, 77–79
MAP, 76
MPVR, 85
MRF, 77
Newton method, 80
noiseless image outcomes, 85, 87
Rayleigh statistics, 74
RLTV filtering, 75, 83–84
speckle decomposition algorithm, 86, 90
speckle, definition, 74
speckle-derived wavelet energies, 83
speckle extraction, 80–81
subject identification, thyroid ultrasound
data, 91–92
tissue-dependent textural features, 89, 92
visual diagnosis, 93
V
Vibratory devices, 284
Vibro-acoutsography, 302
W
Wavelet-based filter (WAVF), 84
Wavelet transform analysis, 264–265
Weighted averaged sensitivity, 195
Weighted median filter (WMF), 53
Weighted Rayleigh maximum likelihood filter
(WRMLF), 84
Biographies of the Editors
Biography of Jo˜ao Miguel Sanches
Jo˜ao Miguel Sanches received the E.E., M.Sc. and Ph.D. degrees from the
Instituto Superior T´ecnico (IST) in the Technical University of Lisbon, Portugal,
in 1991, 1996 and 2003, respectively. He is Assistant Professor at the recently
created Department of Bioengineering at the Instituto Superior T´ecnico, Technical
University of Lisbon. Before, he was at the Department of Electrical and Computer
Engineering (DEEC) where he has taught in the area of signal processing, systems
and control. He has been actively involved in the course of Biomedical Engineering
advising master thesis of the Biomedical Engineering course and PhD students of
the doctoral program in Biomedical Engineering. He has proposed a PhD course of
Medical Image Reconstruction that he has been taught in the last years.
He is researcher at the Institute for Systems and Robotics (ISR) and in the last
years his work has been focused on Biomedical Engineering, namely,in Biomedical
Image Processing, Physiological-based Modeling of Biological Systems in the
perspective of the Systems and Control theory and statistical signal processing
J.M. Sanches et al. (eds.), Ultrasound Imaging: Advances and Applications,
DOI 10.1007/978-1-4614-1180-2, © Springer Science+Business Media, LLC 2012
357
358 Biographies of the Editors
of physiological data. His research activity is focused on the morphological and
tissue characterization of tissues from Ultrasound (US) images for the diagnosis
of the atherosclerotic disease of the carotids and diffuse diseases of the liver. He
is also working in functional Magnetic Resonance Imaging (fMRI) and Fluorescence Confocal Microscope (FCM) imaging in collaboration with the Institute of
Molecular Medicine at the Medical School of the University of Lisbon. He is
also involved in the development of signal processing algorithms and biomedical
applications for mobile phones for the diagnosis of sleep disorders in collaboration
with the Electroencephalography and Clinical Neurophysiology Center (Centro
de ElectroencefalografiaNeurofisiologiaCl´ınica – CENC), also with the Medical
School of the University of Lisbon.
He is member of the IEEE Engineering in Medicine and Biology Society and
Associate Member of the Bio Imaging and Signal Processing Technical Committee
(BISP-TC) of the IEEE Signal Processing Society. He is also president of the
Portuguese Association of Pattern Recognition (APRP).
Biography of Andrew Laine
Andrew F. Laine, D.Sc., is Professor of Biomedical Engineering and Radiology
(Physics), and the Director of the Heffner Biomedical Imaging Laboratory in the
Department of Biomedical Engineering. Since 2001 he has served as Vice Chair of
the Department of Biomedical Engineeringat Columbia. Professor Laine pioneered
the application of multiresolution representations for feature analysis of digital
mammography and ultrasound.He served as Associate Editor of IEEE Transactions
on Image Processing and edited the book, “Wavelet Theory and Applications”,
Kluwer, 1995. He has jointly chaired the SPIE conference on “Mathematical
Imaging: Wavelet Application in Signal and Image Processing”, during the years
1993–2003, and co-authored several book chapters including“Wavelet Applications
in Medicine and Biology”, CRC Press, 1995, and “Time-Frequency and Wavelets
Biographies of the Editors 359
Transforms in Biomedical Engineering,” IEEE Press, 1998. He is a member of
the Editorial Board of the book series Emerging Technologies in Biomedical
Engineering, sponsored by the IEEE-EMBS Society and on the Editorial Board
of the Journal of Visual Communication and Image Representation (Elsevier). He
also served on the program committee for the IEEE-EMBS Workshop on Wavelet
Applications in Medicine in 1994, 1998, 1999 and 2004. Dr. Laine was recently
elected to ADCOM of the IEEE Engineering in Medicine and Biology Society,
and is Chair of Technical Committee (TC) on Medical Imaging for the EMBS,
and a member of the TC of IEEE Signal Processing Society, BISP (Biomedical
Imaging and Signal Processing). He was the Program Chair for the IEEE EMBS
conference in 2006, held in New York City and is the Program Co-Chair for
IEEE International Symposium on Biomedical Imaging (ISBI) in 2008. He is Vice
President elect of Publications for the IEEE EMBS, startingin January 2008.He is a
Senior Member of IEEE (Institute of Electrical and Electronics Engineering) and a
Fellow of AIMBE (American Institute for Medical and Biological Engineering). His
research interests include methods of multi-resolution analysis applied to problems
in medical imaging, image analysis and signal processing, computational biology,
computed aided diagnosis, pattern recognition in biology, biometrics and applied
mathematics.
Biography of Jasjit Suri
Jasjit S. Suri, MS, PhD, MBA, Fellow AIMBE, is an innovator, visionary,
scientist, and an internationally known world leader, has spent over 25 years in
the field of biomedical engineering/sciences, software and hardware engineering
and its management. During his career in biomedical industry/imaging, he has
had an upstream growth and responsibilities from scientific Engineer, Scientist,
Manager, Director R&D, Sr. Director, Vice President to Chief Technology Officer
(CTO) level positions in industries like Siemens Medical Systems, Philips Medical
360 Biographies of the Editors
Systems, Fisher Imaging Corporation and Eigen Inc. & Biomedical Technologies,
respectively and managed up to 100 people.
He has developed products and worked extensively in the areas of breast,
mammography, orthopedics (spine), neurology (brain), angiography (blood vessels), atherosclerosis (plaque), ophthalmology (eye), urology (prostate), image
guided surgery and several kinds of biomedical devices from inception phase to
commercialization phase, including 510(K)/FDA clearances. Under his leadership,
he has obtained over five FDA clearances in Urology, Angiography and Image
Guided Surgery ProductLines ranging from 1000 pages to 5000 pages submissions.
He has conducted in vivo and ex vivo validations on biomedical devices and
surgery systems. Dr. Suri has developed several collaboration programs between
University–Industry partnerships. He has managed funds ranging up to ten million
dollars. He has very successfully built IP portfolios during his career bringing
attraction for larger OEMs spin-offs. Dr. Suri has over 60 US/European Patents,
over 25 books and over 300 peer-reviewed articles. He is a well-known speaker and
has spoken over 50 times at national and international levels. Dr. Suri has won over
50 scientific and extracurricular awards during his career.
He received his Masters from University of Illinois, Chicago, Doctorate from
University of Washington, Seattle, and Executive Management from Weatherhead
School of Management, Case Western Reserve University (CWRU), Cleveland. Dr.
Suri was crowned with President’s Gold medal in 1980 and the Fellow of American
Institute of Medical and Biological Engineering (AIMBE) for his outstanding
contributions at Washington, DC. He believes in “getting a job done” using his
strengths of innovation, strategic partnerships and strong team collaborations by
bringing cross-functional and multi-disciplinary teams together both in-house and
outsourcing relationships.