Добавил:
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз: Предмет: Файл:

Kluwer - Handbook of Biomedical Image Analysis Vol

.1.pdf
Скачиваний:
106
Добавлен:
10.08.2013
Размер:
10.58 Mб
Скачать

The Editors

Dr. Jasjit S. Suri received his BS in computer engineering with distinction from Maulana Azad College of Technology, Bhopal, India, his MS in computer sciences from University of Illinois, Chicago, and Ph.D. in electrical engineering from University of Washington, Seattle. He has been working in the field of computer engineering/imaging sciences for 20 years. He has published more than 125 technical papers in body imaging. He is a lifetime member of research engineering societies: Tau-Beta Pi, Eta-Kappa-Nu, Sigma-Xi, and a member of NY Academy of Sciences, Engineering in Medicine and Biology Society (EMBS), SPIE, ACM, and is also a senior member at IEEE. He is in the editorial board/reviewer of several international journals such as Real Time Imaging, Pattern Analysis and Applications, Engineering in Medicine and Biology, Radiology, Journal of Computer Assisted Tomography, IEEE Transactions of Information Technology in Biomedicine and IASTED Board.

627

628

The Editors

He has chaired image processing tracks at several international conferences and has given more than 40 international presentations/seminars. Dr. Suri has written four books in the area of body imaging (such as cardiology, neurology, pathology, mammography, angiography, atherosclerosis imaging) covering medical image segmentation, image and volume registration, and physics of medical imaging modalities like: MRI, CT, X-ray, PET, and ultrasound. He also holds several United States patents. Dr. Suri has been listed in Who’s Who seven times, is a recipient of president’s gold medal in 1980, and has received more than 50 scholarly and extracurricular awards during his career. He is also a Fellow of American Institute of Medical and Biological Engineering (AIMBE) and ABI. Dr. Suri’s major interests are: computer vision, graphics and image processing (CVGIP), object oriented programming, image guided surgery and teleimaging. Dr. Suri had worked with Philips Medical Systems and Siemens Medical Research Divisions. He is also a visiting professor with the department of computer science, University of Exeter, Exeter, UK. Currently, Dr. Suri is with JWT Inc.

Dr. David Wilson is a professor of biomedical engineering and radiology, Case Western Reserve University. He has research interests in image analysis, quantitative image quality, and molecular imaging, and he has a significant track record of federal research funding in these areas. He has over 60 refereed journal publications and has served as a reviewer for several leading journals. Professor Wilson has six patents and two pending patents in medical imaging. Professor Wilson has been active in the development of international conferences; he was Track Chair at the 2002 EMBS/BMES conference, and he was Technical Program Co-Chair for the 2004 IEEE International Symposium on Biomedical Imaging. Professor Wilson teaches courses in biomedical imaging, and biomedical image processing and analysis. He has advised many graduate and undergraduate

The Editors

629

students, all of whom are quite exceptional, and has been primary research advisor for over 16 graduate students since starting his academic career. Prior to joining CWRU, he worked in x-ray imaging at Siemens Medical Systems at sites in New Jersey and Germany. He obtained his PhD from Rice University. Professor Wilson has actively developed biomedical imaging at CWRU. He has led a faculty recruitment effort, and he has served as PI or has been an active leader on multiple research and equipment developmental awards to CWRU, including an NIH planning grant award for an In Vivo Cellular and Molecular Imaging Center and an Ohio Wright Center of Innovation award. He can be reached at dlw@po.cwru.edu.

Dr. Swamy Laxminarayan currently serves as the chief of biomedical information engineering at the Idaho State University. Previous to this, he held several senior positions both in industry and academia. These have included serving as the chief information officer at the National Louis University, director of the pharmaceutical and health care information services at NextGen Internet (the premier Internet organization that spun off from the NSF sponsored John von Neuman National Supercomputer Center in Princeton), program director of biomedical engineering and research computing and program director of computational biology at the University of Medicine and Dentistry in New Jersey, vice-chair of Advanced Medical Imaging Center, director of clinical computing at the Montefiore Hospital and Medical Center and the Albert Einstein College of Medicine in New York, director of the VocalTec High Tech Corporate University in New Jersey, and the director of the Bay Networks Authorized Center in Princeton. He has also served as an adjunct professor of biomedical engineering

630

The Editors

at the New Jersey Institute of Technology, a clinical associate professor of health informatics, visiting professor at the University of Bruno in Czech Republic, and an honorary professor of health sciences at Tsinghua University in China.

As an educator, researcher, and technologist, Prof. Laxminarayan has been involved in biomedical engineering and information technology applications in medicine and health care for over 25 years and has published over 250 scientific and technical articles in international journals, books, and conferences. His expertize lies in the areas of biomedical information technology, high performance computing, digital signals and image processing, bioinformatics, and physiological systems analysis. He is the co-author of the book State-of-the-Art PDE and Level Sets Algorithmic Approaches to Static and Motion Imagery Segmentation published by Kluwer Publications and the book Angiography Imaging: State-of-the-Art-Acquisition, Image Processing and Applications Using Magnetic Resonance, Computer Tomography, Ultrasound and X-ray, Emerging Mobile E-Health Systems published by the CRC Pres and two volumes of Handbook of Biomedical Imaging to be published by Kluwer Publications. He has also worked as the editor/co-editor of 20 international conferences and has served as a keynote speaker in international conferences in 13 countries.

He is the founding editor-in-chief and editor emeritus of IEEE Transactions on Information Technology in Biomedicine. He served as an elected member of the administrative and executive committees in the IEEE Engineering in Medicine and Biology Society and as the society’s vice president for 2 years. His other IEEE roles include his appointments as program chair and general conference chair of about 20 EMBS and other IEEE conferences, an elected member of the IEEE Publications and Products Board, member of the IEEE Strategic Planning and Transnational Committees, member of the IEEE Distinguished Lecture Series, delegate to the IEEE USA Committee on Communications and Information Policy (CCIP), U.S. delegate to the European Society for Engineering in Medicine, U.S. delegate to the General Assembly of the IFMBE, IEEE delegate to the Public Policy Commission and the Council of Societies of the AIMBE, fellow of the AIMBE, senior member of IEEE, life member, Romanian Society of Clinical Engineering and Computing, life member, Biomedical Engineering Society of India, U.S. delegate to IFAC and IMEKO Councils in TC13. He was recently elected to the Administrative Board of the International Federation for Medical and Biological Engineering, a worldwide organization comprising 48

The Editors

631

national members, overseeing global biomedical engineering activities. He was also elected to serve as the republications co-chairman of the Federation.

His contributions to the discipline have earned him numerous national and international awards. He is a fellow of the American Institute of Medical and Biological Engineering, a recipient of the IEEE 3rd Millennium Medal and a recipient of the Purkynje award from the Czech Academy of Medical Societies, a recipient of the Career Achievement Award, numerous outstanding accomplishment awards, and twice recipient of the IEEE EMBS distinguished service award. He can be reached at s.n.laxminarayan@ieee.org.

Index

Absolute error, 492, 493f Accepted points, 208 Acoustic shade, 5

Active contour models, 512, 572, 589 Active meta-cells, 385, 386, 388, 397, 403 Active sets, 461

Adaptive mesh refinement data structures, 382 Additive operative splitting (AOS) schemes, 573 Advanced segmentation techniques, 479–526.

See also Fuzzy segmentation; Stochastic image models

Advection (intrinsic) diffusion model, 591, 592, 609

Advective part of the speed function, 206 Adventitia, arterial, 22, 23, 31, 44, 47, 49f, 50f Affine (firm) thresholding, 320 Affine-invariant denoising method, 392

Algorithme a` trous, 312 Aliasing, 156, 432

in phase images, 158–159 in speed images, 157

Alzheimer’s disease, 96 Aneurysms, 174, 183, 188, 480, 520 Anger camera, 98–99

Angular resolution, IVUS, 15–17 Anisotropic diffusion

deformable models and, 389, 391, 392, 393–394, 396, 401

level set segmentation and, 440, 441–442, 443–444, 447, 449

linear, 444, 447 planar, 444, 447

Anistropic speed function, 223–224 Antisymmetry, 321

AOS schemes. See Additive operative splitting schemes

Arsenic-75, 60

Arterialized venous (a-v) sampling method, 91–92

Arterial spin tagging projection coronary magnetic resonance angiography (CMRA) technique, 178

Artery reconstruction, 394–396 Artifacts

inflow related, in MRA, 130–131 of IVUS catheter, 5

of IVUS transducers, 24 motion-induced, 180–181 Venetian blind, 166

Artifacts scatterers, 24 Asymmetric echo acquisitions, 137

Attenuated coronary blood-myocardium in-flow contrast 3D coronary magnetic resonance angiography (CMRA), 192

Attenuation coefficient, 36–37 Attenuation correction, 78–83 Autocorrected magnetic resonance

angiography (MRA), 180–181 Automatic seed initialization, 521–522, 523,

524–525

Axial resolution, IVUS, 13–14, 15f

Backward difference formula, 268, 271 BACSPIN. See Breathing AutoCorrection with

SPiral INterleaves coronary magnetic resonance angiography

Band-limited wavelets, 289, 297 Barium fluoride, 85

Batman image, 593, 595f, 596 Bayes classifiers, 343, 489, 495 Bayes’ rule, 485

BCFCM. See Bias corrected fuzzy c-means objective function

Beam intensity, IVUS, 17, 18f

633

634

Index

Beam number, IVUS, 37–38

Beam sweeping criterion, IVUS, 17–19 Beam width, IVUS, 15–16

Bell function, 317

BFS algorithm. See Breadth-first-search algorithm

BGO. See Bismuth germanate oxide Bias corrected fuzzy c-means (BCFCM)

objective function, 505–512, 513f, 514f algorithm, 506–507

cluster prototype updating, 506 membership evaluation, 505 parameter estimation, 505 results, 507–512

Bias fields, 504, 506

Bicubic interpolation function, 212–214, 217–218, 227

Binary trees, 210–212, 215, 315

Biological volume datasets, 415–467. See also Level set segmentation

multiple volume results, 438–439 segmentation from multiple nonuniform,

429–439

Bismuth germanate oxide (BGO), 66–67, 78, 85

Black blood magnetic resonance angiography (MRA), 168

Blank scans, 81 Bloch equations, 124

Block detectors, 66, 85 Blood

IVUS imaging and, 31, 50f PET and, 91–92

Blood flow. See Flow

Blood pool contrast enhancement, 173 Boltzmann constant, 482

Boltzmann distribution, 482 BONO. See Branch-on-need octree Born approximation, 10

Brain

DT-MRI of, 440–449

MRI of, 507–512, 513f, 514f, 565f Brain stroke, 480, 521

Brain tumors, 60, 514f

Branch-on-need octree (BONO), 383–385 Breadth-first-search (BFS) algorithm, 387 Breast cancer, 96

Breath-hold contrast enhanced magnetic resonance angiography (MRA), 177–178

Breathing AutoCorrection with SPiral INterleaves (BACSPIN) coronary magnetic resonance angiography (CMRA), 191

Bright blood imaging, 167 Brightness, 391–392 Brightness error term, 281

Brushlet(s), 314 attributes of, 316–319

spatial-temporal analysis using, 326–332 Brushlet basis functions, 318

B-spline functions, 325, 376, 503 Bump function, 317

Burnt nodes, 369–370, 371

Calculated attenuation correction, 82–83 Cancer

brain, 60, 514f breast, 96 colon, 96

gallbladder, 186–187 head and neck, 96 liver, 96

lung (see Lung cancer) pancreatic, 96

PET and, 58, 75, 96 thyroid, 96

Canny edge detectors, 341, 374, 423, 432, 436 Capacity function, 603

Carbon-11 (11C), 62t, 96

Cardiac-gated 2D phase contrast magnetic resonance angiography (PC MRA), 151–153

Cardiac-triggered free-breathing 3D balanced fast-field echo projection magnetic resonance angiography (MRA), 182

Cardiac ultrasound, 341–342 Carotid bifurcations, 138–140 Carotid siphons. See Vessel turns Cartesian coordinates, 259 Catheters, IVUS, 3–4, 5, 6

CE-MRA. See Contrast-enhanced magnetic resonance angiography

Centered difference formula, 268

Central and forward difference method, 271 Central difference method, 271

Central slice theorem, 72

Cerebral arteriovenous malformations (AVMs), 187

Cerebral ischemia, 176

Cervical magnetic resonance angiography (MRA), 182–185

CFL. See Courant-Friedrichs-Levy restriction Chain rule, 418

Characteristic function, 368

Children of nodes, 210–212, 315, 316, 385 Chromaticity, 391–392

Chromosomes, 489–490

Cine gradient-echo technique, 176 Cine ventriculography, 123

Circle of Willis, 181

Clique(s), 483, 484

Index

635

Clique coefficients, 490

Cluster prototype updating, 502, 506 CMRA. See Coronary magnetic resonance

angiography

Coarse to fine procedures, 344, 345 Coincidence detection, 63–65, 78, 80. See also

Detectors, PET

Coincidence resolving time, 64–65, 67, 78 Colon cancer, 96

Color images, RAGS and, 555–557, 558, 560f, 561, 566–571, 572, 573

Combinatorial manifolds, 368 Compex flow, 162–163 Compton scattering, 77

Computed tomography (CT), 57–58, 79, 96, 100, 363, 479

geometric snakes and, 541

level set segmentation and, 416, 429, 451, 467

lung segmentation and, 480, 482, 491–498 PET combined with, 101–102

Conjugate mirror filters (CMF), 310 Consoles, IVUS, 3, 4f

Constructive Solid Geometry (CSG) model, 422, 432

Continuation algorithms, 379–380 Continuous wavelet transform, 307–309 Contrast-enhanced magnetic resonance

angiography (CE-MRA), 173–174, 184 breath-hold, 177–178

collateral flow assessment with, 179 FLASH, 181

multiphase, 181 ultrashort, 187

Contrast to noise ratio signal (CNRS), 30, 44, 46t, 48f, 192

Control points, 344 Convolution-backprojection reconstruction

method, 73 Convolution methods, 77 Copper-64, 60

Coronary magnetic resonance angiography (CMRA), 188, 193

attenuated coronary blood-myocardium in-flow, 192

BACSPIN, 191

3D real-time navigator, 190 free-breathing 3D, 191

motion-adapted gating window in, 191–192 ROC analysis, 189–190

Coulombic attractive forces, 61 Courant-Friedrichs-Levy (CFL) restriction,

517–518

Co-volume level set method, 583–620 numerical results, 609–616

related mathematical models, 587–597 semi-implicit, 586, 598–609

Crisp segmentation, 480 Cross-scale regularization

for images with low SNR, 335–338 for tomographic images, 332–338

CSG model. See Constructive Solid Geometry model

CT. See Computed tomography Curvelets, 325

Data cache, 386–387 Daubechies scaling function, 294

Daubechies wavelets, 289, 293, 297 DBC. See Differential backscattering

cross-section Dead-time correction, 76–77 Decomposition

brushlet, 327 tetrahedral, 367, 368

wavelets and, 315, 317, 324 Decomposition filters, 310, 311–312 Deconvolution methods, 77

Deformable models, 359–406. See also Level set deformations; Snake models

background to, 364–371

diffusion model convergence with, 388–392 discrete, 367

experimental results, 393–399 free-form, 364–365, 455–456

initialization of, 361–363, 364, 367, 371–377, 401

reconstruction method for, 381–383 Deformable surface models, 363, 367

Degree of freedom (DF) nodes, 601–602, 606, 607

Delayed coincidence detection method, 78 3D-ELLIP. See Three-dimensional elliptical

centric view ordering

Delta functions, 230, 235, 518–519, 520 Denoising

affine-invariant method, 392 deformable models and, 392, 401

state-of-the-art and applications, 326–338 thresholding operators for, 319–320, 325–326,

336

threshold value selection and, 323–324 time inclusion in, 328–332

wavelet transforms and, 306, 316, 319–338, 345

Density gradient estimation, 557 Density parameter estimation, 456–457 Depth, of SPS models, 266

Depth maps, 274, 275, 278, 285, 286 Derin-Elliot model, 484

636

Index

Detectors, PET description of, 66–67

events detected with, 67–69 normalization in, 76

Differences of normals method, 460–461 Differential backscattering cross-section

(DBC), 10, 12, 31–32, 33f, 34f Diffused region forces, 546, 550, 571–572

numerical solutions for, 553 properties of, 547–549 weak-edge leakage and, 561, 562f

Diffusion models, 388–392 Diffusion-reaction equation, 363

Diffusion tensor magnetic resonance imaging (DT-MRI), 440–449, 450f

geometric modeling and, 444–445 segmentation and, 445–449 tensor invariants in, 442, 443–444

Diffusion-weighted imaging, 174 Diffusive part of the speed function, 206 Digital subtraction magnetic resonance

angiography (DSA MRA), 172, 173–174, 187 Dilation, 309, 373, 374

Direct method, for SPS models, 267 Dirichlet boundary conditions, 583, 596, 602,

605, 607, 610, 612

Discrete deformable models, 367 Discrete sampling, 308

Discrete search space, 362

Discrete wavelet transforms, 309–313 dyadic, 311–313

orthogonal, 309–310 Distant points, 208 Divergence theorem, 603

Dominant Gaussian components extracting algorithm, 486–487, 492

Down-sweep process, 211

DSA MRA. See Digital subtraction magnetic resonance angiography

DT-MRI. See Diffusion tensor magnetic resonance imaging

3D-TRICKS. See Three-dimensional time-resolved imaging of contrast kinetics

Dual active contour (ACM), 361–362 Dual T-snakes model, 362

Dynamic programming (DP), 362 Dynamic-range compression, 151

Echo amplitude, 7, 25 Echocardiography, 615–616 Echogram generation

1D, 24–26 2D, 26–28

Echo time (TE), 118, 120, 133 flow-related enhancement and, 128

high-resolution MRA and, 182 INFLOW method and, 170 shorter, 162, 170

slice-transition phenomenon and, 126 spin phase phenomenon and, 132 TOF MRA and, 135, 139, 142, 144, 145 variable, 182

Edge crispening, 321 Edge definition, 390–391

Edge detection and detectors Canny, 341, 374, 423, 432, 436 deformable models and, 366

level set segmentation and, 423, 431–432, 434, 436

RAGS and, 555

wavelet, 333–334, 336, 339–342 Edge points, 341, 389

Elastic net algorithm, 376 Electron capture, 61

Elliptic solvers, coupling to, 230–240

EM algorithm. See Expectation maximization algorithm

Embedding function, 201, 202, 207

Emission computed tomography, 59. See also Positron emission tomography; Single-photon emission computed tomography

Empirical approaches to scatter correction, 77 Energy functions

Gibbs random field and, 483

SPS models and, 260, 264, 281–282 wavelet-based methods and, 293, 294, 295

Enhancement operators, 320–323, 336 ENO method, 206

Enrichment functions, 232–233

Entropy condition, 202, 369, 371, 372, 554, 555 Entropy-satisfying schemes, 574–575

Entry slice phenomenon, 127–128, 130 Epanechnikov kernel, 557

Epilepsy, 96 Erosion, 373, 374 Error

absolute, 492, 493f IVUS and, 5–6

level set segmentation and, 453, 455 lung CT and, 492, 493f

maximum radial, 563, 565f

sum square, in IVUS, 35–36, 44, 46t total squared brightness, 265

Error sinograms, 456 Euclidean curves, 537

Euclidean distance functions, 434, 502, 554 Euclidean metric, 538–539

Euclidean space, 373, 557

Euler equations, 264–265, 267, 282

Index

637

Eulerian methods, 241, 370 Euler-Lagrange equations, 365, 392, 539

Expectation maximization (EM) algorithm, 74, 492, 495

fuzzy segmentation and, 503–504, 507–509 maximum likelihood, 74–75

ordered subsets, 75

stochastic image models and, 486, 487–488 Extended finite element method (X-FEM),

231–235, 239, 240 External forces

deformable models and, 359, 369 RAGS and, 550, 552

Face recognition, neural nets for, 375

Fast marching method, 207–214, 215, 216, 217, 243, 298

applications of, 242 geometric snakes and, 573 initialization of, 209, 212–214

level set segmentation and, 418 novel extension of, 223–225 velocity extensions and, 226

Fast spoiled gradient echo (SPGR), 186 Fat-suppressed three-dimensional magnetic

resonance angiography (MRA), 186 FBP. See Filtered backprojection

FCM algorithm. See Fuzzy c-means algorithm FDG. See [18F]fluorodeoxyglucose

FDM. See Finite difference method Feature points, 344

[18F]fluorodeoxyglucose (FDG), 87–88, 93 [18F]fluorodeoxyglucose-6-phosphate

(FDG-6-P), 88

Filter bank, 309–313, 319

Filtered backprojection (FBP), 72–73, 75, 85, 86 introduction of, 60

level set segmentation and, 451–452, 462, 463–465, 466

wavelet transforms and, 333, 334f, 335 Filters and filtering

conjugate mirror, 310 decomposition, 310, 311–312 Gabor, 327

Gaussian, 28, 30, 447 Hann, 333 high-pass, 310 homomorphic, 503

Lemarie´-Battle, 338, 339 linear, 421

low-pass, 310, 333, 421, 422 median, 30

morphological, 421–422 ramp, 85, 333 reconstruction, 311–312

steerable, 327 Wiener, 332

Finite difference method (FDM), 260, 366, 371, 606

convergence of, 271

formula and examples, 267–271 Finite elements methods, 366

First-order gradient moment nulling, 160–161 Fitness function, 490

FLAG. See Flow adjusted gradient Flame propagation, 201, 202

FLASH contrast-enhanced magnetic resonance angiography (CE-MRA), 181

Flip angle, 137, 143, 144, 164–165, 166 Flow

collateral, assessment of, 179 compex, 162–163

laminar, 121–123, 124, 125 physical principles of, 124–134 plug, 122, 124, 125

principles of, 117–124 pulsatile, 121–123 turbulent, 121–123

Flow adjusted gradient (FLAG), 169, 171–172 Flow compensation (FC), 160

PC, 162

phase/frequency, high-resolution MRA with, 182

TOF MRA and, 134, 142, 144–145 Flow eddies, 167

Flow encoding gradients, 146, 155 Flow phase, 146

Flow-related enhancement, 128–129 Flow velocity, 121–123, 136, 145–146 Flow void, 147

Fluorine-18 (18F), 62, 96

Forward difference formula, 268, 269, 271, 274, 277

Fourier-based image reconstruction, 71–73 Fourier transforms, 450, 451. See also Inverse

Fourier transforms brushlets and, 317–318

SPS models and, 267, 273, 274, 275, 276, 277, 286–287

wavelets and, 290, 306–307, 308, 314 windowed, 306–307, 308

Fractional volume segments, 124 Free-breathing 3D coronary magnetic

resonance angiography (CMRA), 191 Free-form deformations, 364–365, 455–456 Fresnelets, 325

Frog embyo, MRI scan of, 427–428 Fully-discrete semi-implicit co-volume scheme,

604

Function, defined, 58