Elsevier S & T

Microscope Image Processing

Wu, Qiang; Merchant, Fatima; Castleman, Kenneth

In stock

No reviews

43.000 KD inc. VAT

License

perpetual

Table of contents

Contentsv
Forewordxxi
Referencexxii
Prefacexxiii
Acknowledgmentsxxv
Chapter 1: Introduction1
1.1 The Microscope and Image Processing1
1.2 Scope of This Book1
1.3 Our Approach3
1.3.1 The Four Types of Images3
1.3.1.1 Optical Image4
1.3.1.2 Continuous Image4
1.3.1.3 Digital Image4
1.3.1.4 Displayed Image5
1.3.2 The Result5
1.3.2.1 Analytic Functions6
1.3.3 The Sampling Theorem7
1.4 The Challenge8
1.5 Nomenclature8
1.6 Summary of Important Points8
References9
Chapter 2: Fundamentals of Microscopy11
2.1 Origins of the Microscope11
2.2 Optical Imaging12
2.2.1 Image Formation by a Lens12
2.2.1.1 Imaging a Point Source13
2.2.1.2 Focal Length13
2.2.1.3 Numerical Aperture14
2.2.1.4 Lens Shape15
2.3 Diffraction-Limited Optical Systems15
2.3.1 Linear System Analysis16
2.4 Incoherent Illumination16
2.4.1 The Point Spread Function16
2.4.2 The Optical Transfer Function17
2.5 Coherent Illumination18
2.5.1 The Coherent Point Spread Function18
2.5.2 The Coherent Optical Transfer Function19
2.6 Resolution20
2.6.1 Abbe Distance21
2.6.2 Rayleigh Distance21
2.6.3 Size Calculations21
2.7 Aberration22
2.8 Calibration22
2.8.1 Spatial Calibration23
2.8.2 Photometric Calibration23
2.9 Summary of Important Points24
References25
Chapter 3: Image Digitization27
3.1 Introduction27
3.2 Resolution28
3.3 Sampling29
3.3.1 Interpolation30
3.3.2 Aliasing32
3.4 Noise33
3.5 Shading34
3.6 Photometry34
3.7 Geometric Distortion35
3.8 Complete System Design35
3.8.1 Cumulative Resolution35
3.8.2 Design Rules of Thumb36
3.8.2.1 Pixel Spacing36
3.8.2.2 Resolution36
3.8.2.3 Noise36
3.8.2.4 Photometry36
3.8.2.5 Distortion37
3.9 Summary of Important Points37
References37
Chapter 4: Image Display39
4.1 Introduction39
4.2 Display Characteristics40
4.2.1 Displayed Image Size40
4.2.2 Aspect Ratio40
4.2.3 Photometric Resolution41
4.2.4 Grayscale Linearity42
4.2.5 Low-Frequency Response42
4.2.5.1 Pixel Polarity42
4.2.5.2 Pixel Interaction43
4.2.6 High-Frequency Response43
4.2.7 The Spot-Spacing Compromise43
4.2.8 Noise Considerations43
4.3 Volatile Displays44
4.4 Sampling for Display Purposes45
4.4.1 Oversampling46
4.4.2 Resampling46
4.5 Display Calibration47
4.6 Summary of Important Points47
References48
Chapter 5: Geometric Transformations51
5.1 Introduction51
5.2 Implementation52
5.3 Gray-Level Interpolation52
5.3.1 Nearest-Neighbor Interpolation53
5.3.2 Bilinear Interpolation53
5.3.3 Bicubic Interpolation54
5.3.4 Higher-Order Interpolation54
5.4 Spatial Transformation55
5.4.1 Control-Grid Mapping55
5.5 Applications56
5.5.1 Distortion Removal56
5.5.2 Image Registration56
5.5.3 Stitching56
5.6 Summary of Important Points57
References57
Chapter 6: Image Enhancement59
6.1 Introduction59
6.2 Spatial Domain Methods60
6.2.1 Contrast Stretching60
6.2.2 Clipping and Thresholding61
6.2.3 Image Subtraction and Averaging61
6.2.4 Histogram Equalization62
6.2.5 Histogram Specification62
6.2.6 Spatial Filtering63
6.2.7 Directional and Steerable Filtering65
6.2.8 Median Filtering67
6.3 Fourier Transform Methods68
6.3.1 Wiener Filtering and Wiener Deconvolution68
6.3.2 Deconvolution Using a Least-Squares Approach70
6.3.3 Low-Pass Filtering in the Fourier Domain71
6.3.4 High-Pass Filtering in the Fourier Domain71
6.4 Wavelet Transform Methods72
6.4.1 Wavelet Thresholding72
6.4.2 Differential Wavelet Transform and Multiscale Pointwise Product73
6.5 Color Image Enhancement74
6.5.1 Pseudo-Color Transformations75
6.5.2 Color Image Smoothing75
6.5.3 Color Image Sharpening75
6.6 Summary of Important Points76
References77
Chapter 7: Wavelet Image Processing79
7.1 Introduction79
7.1.1 Linear Transformations80
7.1.2 Short-Time Fourier Transform and Wavelet Transform81
7.2 Wavelet Transforms83
7.2.1 Continuous Wavelet Transform83
7.2.2 Wavelet Series Expansion84
7.2.3 Haar Wavelet Functions85
7.3 Multiresolution Analysis85
7.3.1 Multiresolution and Scaling Function86
7.3.2 Scaling Functions and Wavelets87
7.4 Discrete Wavelet Transform88
7.4.1 Decomposition88
7.4.2 Reconstruction91
7.4.3 Filter Banks92
7.4.3.1 Two-Channel Subband Coding92
7.4.3.2 Orthogonal Filter Design93
7.4.4 Compact Support95
7.4.5 Biorthogonal Wavelet Transforms96
7.4.5.1 Biorthogonal Filter Banks97
7.4.5.2 Examples of Biorthogonal Wavelets99
7.4.6 Lifting Schemes100
7.4.6.1 Biorthogonal Wavelet Design100
7.4.6.2 Wavelet Transform Using Lifting101
7.5 Two-Dimensional Discrete Wavelet Transform102
7.5.1 Two-Dimensional Wavelet Bases102
7.5.2 Forward Transform103
7.5.3 Inverse Transform105
7.5.4 Two-Dimensional Biorthogonal Wavelets105
7.5.5 Overcomplete Transforms106
7.6 Examples107
7.6.1 Image Compression107
7.6.2 Image Enhancement107
7.6.3 Extended Depth-of-Field by Wavelet Image Fusion108
7.7 Summary of Important Points108
References110
Chapter 8: Morphological Image Processing113
8.1 Introduction113
8.2 Binary Morphology115
8.2.1 Binary Erosion and Dilation115
8.2.2 Binary Opening and Closing116
8.2.3 Binary Morphological Reconstruction from Markers118
8.2.3.1 Connectivity118
8.2.3.2 Markers119
8.2.3.3 The Edge-Off Operation120
8.2.4 Reconstruction from Opening120
8.2.5 Area Opening and Closing122
8.2.6 Skeletonization123
8.3 Grayscale Operations127
8.3.1 Threshold Decomposition128
8.3.2 Erosion and Dilation129
8.3.2.1 Gradient131
8.3.3 Opening and Closing131
8.3.3.1 Top-Hat Filtering131
8.3.3.2 Alternating Sequential Filters133
8.3.4 Component Filters and Grayscale Morphological Reconstruction134
8.3.4.1 Morphological Reconstruction135
8.3.4.2 Alternating Sequential Component Filters135
8.3.4.3 Grayscale Area Opening and Closing135
8.3.4.4 Edge-Off Operator136
8.3.4.5 h-Maxima and h-Minima Operations137
8.3.4.6 Regional Maxima and Minima137
8.3.4.7 Regional Extrema as Markers138
8.4 Watershed Segmentation138
8.4.1 Classical Watershed Transform139
8.4.2 Filtering the Minima140
8.4.3 Texture Detection143
8.4.4 Watershed from Markers145
8.4.5 Segmentation of Overlapped Convex Cells146
8.4.6 Inner and Outer Markers148
8.4.7 Hierarchical Watershed151
8.4.8 Watershed Transform Algorithms152
8.5 Summary of Important Points154
References156
Chapter 9: Image Segmentation159
9.1 Introduction159
9.1.1 Pixel Connectivity160
9.2 Region-Based Segmentation160
9.2.1 Thresholding160
9.2.1.1 Global Thresholding161
9.2.1.2 Adaptive Thresholding162
9.2.1.3 Threshold Selection163
9.2.1.4 Thresholding Circular Spots165
9.2.1.5 Thresholding Noncircular and Noisy Spots167
9.2.2 Morphological Processing169
9.2.2.1 Hole Filling171
9.2.2.2 Border-Object Removal171
9.2.2.3 Separation of Touching Objects172
9.2.2.4 The Watershed Algorithm172
9.2.3 Region Growing173
9.2.4 Region Splitting175
9.3 Boundary-Based Segmentation176
9.3.1 Boundaries and Edges176
9.3.2 Boundary Tracking Based on Maximum Gradient Magnitude177
9.3.3 Boundary Finding Based on Gradient Image Thresholding178
9.3.4 Boundary Finding Based on Laplacian Image Thresholding179
9.3.5 Boundary Finding Based on Edge Detection and Linking180
9.3.5.1 Edge Detection180
9.3.5.2 Edge Linking and Boundary Refinement183
9.3.6 Encoding Segmented Images188
9.3.6.1 Object Label Map189
9.3.6.2 Boundary Chain Code189
9.4 Summary of Important Points190
References192
Chapter 10: Object Measurement195
10.1 Introduction195
10.2 Measures for Binary Objects196
10.2.1 Size Measures196
10.2.1.1 Area196
10.2.1.2 Perimeter196
10.2.1.3 Area and Perimeter of a Polygon197
10.2.2 Pose Measures199
10.2.2.1 Centroid199
10.2.2.2 Orientation200
10.2.3 Shape Measures200
10.2.3.1 Thinness Ratio201
10.2.3.2 Rectangularity201
10.2.3.3 Circularity201
10.2.3.4 Euler Number203
10.2.3.5 Moments203
10.2.3.6 Elongation205
10.2.4 Shape Descriptors206
10.2.4.1 Differential Chain Code206
10.2.4.2 Fourier Descriptors206
10.2.4.3 Medial Axis Transform207
10.2.4.4 Graph Representations208
10.3 Distance Measures209
10.3.1 Euclidean Distance209
10.3.2 City-Block Distance209
10.3.3 Chessboard Distance210
10.4 Gray-Level Object Measures210
10.4.1 Intensity Measures210
10.4.1.1 Integrated Optical Intensity210
10.4.1.2 Average Optical Intensity210
10.4.1.3 Contrast211
10.4.2 Histogram Measures211
10.4.2.1 Mean Gray Level211
10.4.2.2 Standard Deviation of Gray Levels211
10.4.2.3 Skew212
10.4.2.4 Entropy212
10.4.2.5 Energy212
10.4.3 Texture Measures212
10.4.3.1 Statistical Texture Measures213
10.4.3.2 Power Spectrum Features214
10.5 Object Measurement Considerations215
10.6 Summary of Important Points215
References217
Chapter 11: Object Classification221
11.1 Introduction221
11.2 The Classification Process221
11.2.1 Bayes’ Rule222
11.3 The Single-Feature, Two-Class Case222
11.3.1 A Priori Probabilities223
11.3.2 Conditional Probabilities223
11.3.3 Bayes’ Theorem224
11.4 The Three-Feature, Three-Class Case225
11.4.1 Bayes Classifier226
11.4.1.1 Prior Probabilities226
11.4.1.2 Classifier Training227
11.4.1.3 The Mean Vector227
11.4.1.4 Covariance228
11.4.1.5 Variance and Standard Deviation228
11.4.1.6 Correlation228
11.4.1.7 The Probability Density Function229
11.4.1.8 Classification229
11.4.1.9 Log Likelihoods229
11.4.1.10 Mahalanobis Distance Classifier230
11.4.1.11 Uncorrelated Features230
11.4.2 A Numerical Example231
11.5 Classifier Performance232
11.5.1 The Confusion Matrix233
11.6 Bayes Risk234
11.6.1 Minimum-Risk Classifier234
11.7 Relationships Among Bayes Classifiers235
11.8 The Choice of a Classifier235
11.8.1 Subclassing236
11.8.2 Feature Normalization236
11.9 Nonparametric Classifiers238
11.9.1 Nearest-Neighbor Classifiers239
11.10 Feature Selection240
11.10.1 Feature Reduction240
11.10.1.1 Principal Component Analysis241
11.10.1.2 Linear Discriminant Analysis242
11.11 Neural Networks243
11.12 Summary of Important Points244
References245
Chapter 12: Fluorescence Imaging247
12.1 Introduction247
12.2 Basics of Fluorescence Imaging248
12.2.1 Image Formation in Fluorescence Imaging249
12.3 Optics in Fluorescence Imaging250
12.4 Limitations in Fluorescence Imaging251
12.4.1 Instrumentation-Based Aberrations251
12.4.1.1 Photon Shot Noise251
12.4.1.2 Dark Current252
12.4.1.3 Auxiliary Noise Sources252
12.4.1.4 Quantization Noise253
12.4.1.5 Other Noise Sources253
12.4.2 Sample-Based Aberrations253
12.4.2.1 Photobleaching253
12.4.2.2 Autofluorescence254
12.4.2.3 Absorption and Scattering255
12.4.3 Sample and Instrumentation Handling–Based Aberrations255
12.5 Image Corrections in Fluorescence Microscopy256
12.5.1 Background Shading Correction256
12.5.2 Correction Using the Recorded Image257
12.5.3 Correction Using Calibration Images258
12.5.3.1 Two-Image Calibration258
12.5.3.2 Background Subtraction258
12.5.4 Correction Using Surface Fitting259
12.5.5 Histogram-Based Background Correction261
12.5.6 Other Approaches for Background Correction261
12.5.7 Autofluorescence Correction261
12.5.8 Spectral Overlap Correction262
12.5.9 Photobleaching Correction262
12.5.10 Correction of Fluorescence Attenuation in Depth265
12.6 Quantifying Fluorescence266
12.6.1 Fluorescence Intensity Versus Fluorophore Concentration266
12.7 Fluorescence Imaging Techniques267
12.7.1 Immunofluorescence267
12.7.2 Fluorescence in situ Hybridization (FISH)270
12.7.3 Quantitative Colocalization Analysis271
12.7.4 Fluorescence Ratio Imaging (RI)275
12.7.5 Fluorescence Resonance Energy Transfer (FRET)277
12.7.6 Fluorescence Lifetime Imaging (FLIM)284
12.7.7 Fluorescence Recovery After Photobleaching (FRAP)286
12.7.8 Total Internal Reflectance Fluorescence Microscopy (TIRFM)288
12.7.9 Fluorescence Correlation Spectroscopy (FCS)289
12.8 Summary of Important Points290
References291
Chapter 13: Multispectral Imaging299
13.1 Introduction299
13.2 Principles of Multispectral Imaging300
13.2.1 Spectroscopy301
13.2.2 Imaging302
13.2.3 Multispectral Microscopy304
13.2.4 Spectral Image Acquisition Methods304
13.2.4.1 Wavelength-Scan Methods304
13.2.4.2 Spatial-Scan Methods305
13.2.4.3 Time-Scan Methods306
13.3 Multispectral Image Processing306
13.3.1 Calibration for Multispectral Image Acquisition307
13.3.2 Spectral Unmixing312
13.3.2.1 Fluorescence Unmixing315
13.3.2.2 Brightfield Unmixing317
13.3.2.3 Unsupervised Unmixing318
13.3.3 Spectral Image Segmentation321
13.3.3.1 Combining Segmentation with Classification322
13.3.3.2 M-FISH Pixel Classification322
13.4 Summary of Important Points323
References324
Chapter 14: Three-Dimensional Imaging329
14.1 Introduction329
14.2 Image Acquisition329
14.2.1 Wide-Field Three-Dimensional Microscopy330
14.2.2 Confocal Microscopy330
14.2.3 Multiphoton Microscopy331
14.2.4 Other Three-Dimensional Microscopy Techniques333
14.3 Three-Dimensional Image Data334
14.3.1 Three-Dimensional Image Representation334
14.3.1.1 Three-Dimensional Image Notation334
14.4 Image Restoration and Deblurring335
14.4.1 The Point Spread Function335
14.4.1.1 Theoretical Model of the Point Spread Function337
14.4.2 Models for Microscope Image Formation338
14.4.2.1 Poisson Noise338
14.4.2.2 Gaussian Noise338
14.4.3 Algorithms for Deblurring and Restoration339
14.4.3.1 No-Neighbor Methods339
14.4.3.2 Nearest-Neighbor Method340
14.4.3.3 Linear Methods342
14.4.3.4 Nonlinear Methods346
14.4.3.5 Maximum-Likelihood Restoration349
14.4.3.6 Blind Deconvolution353
14.4.3.7 Interpretation of Deconvolved Images354
14.4.3.8 Commercial Deconvolution Packages354
14.5 Image Fusion355
14.6 Three-Dimensional Image Processing356
14.7 Geometric Transformations356
14.8 Pointwise Operations357
14.9 Histogram Operations357
14.10 Filtering359
14.10.1 Linear Filters359
14.10.1.1 Finite Impulse Response (FIR) Filter359
14.10.2 Nonlinear Filters360
14.10.2.1 Median Filter360
14.10.2.2 Weighted Median Filter360
14.10.2.3 Minimum and Maximum Filters361
14.10.2.4 alpha-Trimmed Mean Filters361
14.10.3 Edge-Detection Filters361
14.11 Morphological Operators362
14.11.1 Binary Morphology363
14.11.2 Grayscale Morphology364
14.12 Segmentation365
14.12.1 Point-Based Segmentation366
14.12.2 Edge-Based Segmentation367
14.12.3 Region-Based Segmentation369
14.12.3.1 Connectivity369
14.12.3.2 Region Growing370
14.12.3.3 Region Splitting and Region Merging370
14.12.4 Deformable Models371
14.12.5 Three-Dimensional Segmentation Methods in the Literature372
14.13 Comparing Three-Dimensional Images375
14.14 Registration375
14.15 Object Measurements in Three Dimensions376
14.15.1 Euler Number376
14.15.2 Bounding Box377
14.15.3 Center of Mass377
14.15.4 Surface Area Estimation378
14.15.5 Length Estimation379
14.15.6 Curvature Estimation380
14.15.6.1 Surface Triangulation Method381
14.15.6.2 Cross-Patch Method381
14.15.7 Volume Estimation381
14.15.8 Texture382
14.16 Three-Dimensional Image Display382
14.16.1 Montage382
14.16.2 Projected Images384
14.16.2.1 Voxel Projection384
14.16.2.2 Ray Casting384
14.16.3 Surface and Volume Rendering385
14.16.3.1 Surface Rendering385
14.16.3.2 Volume Rendering386
14.16.4 Stereo Pairs387
14.16.5 Color Anaglyphs388
14.16.6 Animations388
14.17 Summary of Important Points389
References392
Chapter 15: Time-Lapse Imaging401
15.1 Introduction401
15.2 Image Acquisition403
15.2.1 Microscope Setup404
15.2.2 Spatial Dimensionality405
15.2.3 Temporal Resolution410
15.3 Image Preprocessing411
15.3.1 Image Denoising411
15.3.2 Image Deconvolution412
15.3.3 Image Registration413
15.4 Image Analysis414
15.4.1 Cell Tracking415
15.4.1.1 Cell Segmentation415
15.4.1.2 Cell Association417
15.4.2 Particle Tracking417
15.4.2.1 Particle Detection418
15.4.2.2 Particle Association419
15.5 Trajectory Analysis420
15.5.1 Geometry Measurements421
15.5.2 Diffusivity Measurements421
15.5.3 Velocity Measurements423
15.6 Sample Algorithms423
15.6.1 Cell Tracking424
15.6.2 Particle Tracking427
15.7 Summary of Important Points432
References434
Chapter 16: Autofocusing441
16.1 Introduction441
16.1.1 Autofocus Methods441
16.1.2 Passive Autofocusing442
16.2 Principles of Microscope Autofocusing442
16.2.1 Fluorescence and Brightfield Autofocusing443
16.2.2 Autofocus Functions444
16.2.3 Autofocus Function Sampling and Approximation445
16.2.3.1 Gaussian Fitting447
16.2.3.2 Parabola Fitting447
16.2.4 Finding the In-Focus Imaging Position448
16.3 Multiresolution Autofocusing448
16.3.1 Multiresolution Image Representations449
16.3.2 Wavelet-Based Multiresolution Autofocus Functions451
16.3.3 Multiresolution Search for In-Focus Position451
16.4 Autofocusing for Scanning Microscopy452
16.5 Extended Depth-of-Field Microscope Imaging454
16.5.1 Digital Image Fusion455
16.5.2 Pixel-Based Image Fusion456
16.5.3 Neighborhood-Based Image Fusion457
16.5.4 Multiresolution Image Fusion458
16.5.5 Noise and Artifact Control in Image Fusion459
16.5.5.1 Multiscale Pointwise Product460
16.5.5.2 Consistency Checking461
16.5.5.3 Reassignment462
16.6 Examples462
16.7 Summary of Important Points462
References465
Chapter 17: Structured Illumination Imaging469
17.1 Introduction469
17.1.1 Conventional Light Microscope469
17.1.2 Sectioning the Specimen470
17.1.3 Structured Illumination471
17.2 Linear SIM Instrumentation472
17.2.1 Spatial Light Modulator473
17.3 The Process of Structured Illumination Imaging473
17.3.1 Extended-Depth-of-Field Image475
17.3.2 SIM for Optical Sectioning475
17.3.3 Sectioning Strength477
17.4 Limitations of Optical Sectioning with SIM479
17.4.1 Artifact Reduction via Image Processing480
17.4.1.1 Intensity Normalization480
17.4.1.2 Grid Position Error482
17.4.1.3 Statistical Waveform Compensation484
17.4.1.4 Parameter Optimization485
17.5 Color Structured Illumination486
17.5.1 Processing Technique487
17.5.2 Chromatic Aberration488
17.5.3 SIM Example490
17.6 Lateral Superresolution491
17.6.1 Bypassing the Optical Transfer Function491
17.6.2 Mathematical Foundation492
17.6.2.1 Shifting Frequency Space492
17.6.2.2 Extracting the Enhanced Image493
17.6.3 Lateral Resolution Enhancement Simulation495
17.7 Summary of Important Points496
References496
Chapter 18: Image Data and Workflow Management499
18.1 Introduction499
18.1.1 Open Microscopy Environment500
18.1.2 Image Management in Other Fields500
18.1.3 Requirements for Microscopy Image Management Systems500
18.2 Architecture of Microscopy Image/Data/Workflow Systems501
18.2.1 Client–Server Architecture501
18.2.2 Image and Data Servers502
18.2.3 Users, Ownership, Permissions503
18.3 Microscopy Image Management504
18.3.1 XYZCT Five-Dimensional Imaging Model504
18.3.2 Image Viewers504
18.3.3 Image Hierarchies506
18.3.3.1 Predefined Containers506
18.3.3.2 User-Defined Containers507
18.3.4 Browsing and Search508
18.3.5 Microscopy Image File Formats and OME-XML509
18.3.5.1 OME-XML Image Acquisition Ontology511
18.4 Data Management512
18.4.1 Biomedical Ontologies513
18.4.2 Building Ontologies with OME SemanticTypes514
18.4.3 Data Management Software with Plug-in Ontologies516
18.4.4 Storing Data with Ontological Structure517
18.4.4.1 Image Acquisition Meta-Data517
18.4.4.2 Mass Annotations517
18.4.4.3 Spreadsheet Annotations518
18.5 Workflow Management519
18.5.1 Data Provenance519
18.5.1.1 OME AnalysisModules520
18.5.1.2 Editing and Deleting Data520
18.5.2 Modeling Quantitative Image Analysis521
18.5.2.1 Coupling Algorithms to Informatics Platforms522
18.5.2.2 Composing Workflows524
18.5.2.3 Enacting Workflows524
18.6 Summary of Important Points527
References529
Glossary of Microscope Image Processing Terms531
References539
Index541
Color Plate Section549

Book details

Vendor Elsevier S & T
SKU 9780123725783
ISBN-13 9780080558547
Author Wu, Qiang; Merchant, Fatima; Castleman, Kenneth
Category Science
Subject Microscopes & Microscopy

Do you have questions about this book?

Ask an expert!

Digital image processing, an integral part of microscopy, is increasingly important to the fields of medicine and scientific research. This book provides a unique one-stop reference on the theory, technique, and applications of this technology.

Written by leading experts in the field, this book presents a unique practical perspective of state-of-the-art microscope image processing and the development of specialized algorithms. It contains in-depth analysis of methods coupled with the results of specific real-world experiments. Microscope Image Processing covers image digitization and display, object measurement and classification, autofocusing, and structured illumination.

Key Features:

• Detailed descriptions of many leading-edge methods and algorithms
• In-depth analysis of the method and experimental results, taken from real-life examples
• Emphasis on computational and algorithmic aspects of microscope image processing
• Advanced material on geometric, morphological, and wavelet image processing, fluorescence, three-dimensional and time-lapse microscopy, microscope image enhancement, MultiSpectral imaging, and image data management

This book is of interest to all scientists, engineers, clinicians, post-graduate fellows, and graduate students working in the fields of biology, medicine, chemistry, pharmacology, and other related fields. Anyone who uses microscopes in their work and needs to understand the methodologies and capabilities of the latest digital image processing techniques will find this book invaluable.

* Presents a unique practical perspective of state-of-the-art microcope image processing and the development of specialized algorithms.
* Each chapter includes in-depth analysis of methods coupled with the results of specific real-world experiments.
* Co-edited by Kenneth R. Castleman, world-renowned pioneer in digital image processing and author of two seminal textbooks on the subject.

Instant delivery by email
Your access email arrives within minutes of checkout, with a sign-in link for each book — no shipping, no waiting.

Read on any device
Books open in VitalSource Bookshelf on your phone, tablet, or computer, online or offline. Your library is always available at aafaq.vitalsource.com — just log in with the email you used at checkout.

Lost the email?
Resend it to yourself in seconds from My eBook orders, or email cs@aafaqeducation.com and we'll help.