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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Hands-on resource to understand and successfully process biological image data In Imaging Life: Image Acquisition and Analysis in Biology and Medicine, distinguished biologist Dr. Lawrence R. Griffing delivers a comprehensive and accessible exploration of scientific imaging, including but not limited to the different scientific imaging technologies, image processing, and analysis. The author discusses technical features, challenges, and solutions of the various imaging modalities to obtain the best possible image. Divided into three sections, the book opens with the basics such as the various image media, their representation and evaluation. It explains in exceptional detail pre- and postprocessing of an image. The last section concludes with common microscopic and biomedical imaging modalities in light of technical limitations and solutions to achieve the best possible image acquisition of the specimen. Imaging Life: Image Acquisition and Analysis in Biology and Medicine is written specifically for readers with limited mathematical and programming backgrounds and includes tutorials on image processing in relevant chapters. It also contains exercises in the use of popular, open-source software. * A thorough introduction to imaging methods, technical features, challenges, and solutions to successfully capture biological images * Offers tutorials on image processing using open-source software in relevant chapter * Discusses details of acquisition needs and image media covering pixels, pixel values, contrast, tonal range, and image formats * In-depth presentation of microscopic and biomedical imaging modalities Perfect for professionals and students in the biological sciences and engineering, Imaging Life: Image Acquisition and Analysis in Biology and Medicine is an ideal resource for research labs, biotech companies, and equipment vendors.
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Imaging Life
Image Acquisition and Analysis in Biology and Medicine
Lawrence R. Griffing Biology Department Texas A&M University Texas, United States
Copyright © 2023 by John Wiley & Sons, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Set in 9.5/12.5pt STIXTwoText by Integra Software Services Pvt. Ltd., Pondicherry, India
Contents
Cover
Title page
Copyright
Preface
Acknowledgments
About the Companion Website
Section 1 Image Acquisition
1 Image Structure and Pixels
1.1 The Pixel Is the Smallest Discrete Unit of a Picture
1.2 The Resolving Power of a Camera or Display Is the Spatial Frequency of Its Pixels
1.3 Image Legibility Is the Ability to Recognize Text in an Image by Eye
1.4 Magnification Reduces Spatial Frequencies While Making Bigger Images
1.5 Technology Determines Scale and Resolution
1.6 The Nyquist Criterion: Capture at Twice the Spatial Frequency of the Smallest Object Imaged
1.7 Archival Time, Storage Limits, and the Resolution of the Display Medium Influence Capture and Scan Resolving Power
1.8 Digital Image Resizing or Scaling Match the Captured Image Resolution to the Output Resolution
1.9 Metadata Describes Image Content, Structure, and Conditions of Acquisition
2 Pixel Values and Image Contrast
2.1 Contrast Compares the Intensity of a Pixel with That of Its Surround
2.2 Pixel Values Determine Brightness and Color
2.3 The Histogram Is a Plot of the Number of Pixels in an Image at Each Level of Intensity
2.4 Tonal Range Is How Much of the Pixel Depth Is Used in an Image
2.5 The Image Histogram Shows Overexposure and Underexposure
2.6 High-Key Images Are Very Light, and Low-Key Images Are Very Dark
2.7 Color Images Have Various Pixel Depths
2.8 Contrast Analysis and Adjustment Using Histograms Are Available in Proprietary and Open-Source Software
2.9 The Intensity Transfer Graph Shows Adjustments of Contrast and Brightness Using Input and Output Histograms
2.10 Histogram Stretching Can Improve the Contrast and Tonal Range of the Image without Losing Information
2.11 Histogram Stretching of Color Channels Improves Color Balance
2.12 Software Tools for Contrast Manipulation Provide Linear, Non-linear, and Output-Visualized Adjustment
2.13 Different Image Formats Support Different Image Modes
2.14 Lossless Compression Preserves Pixel Values, and Lossy Compression Changes Them
3 Representation and Evaluation of Image Data
3.1 Image Representation Incorporates Multiple Visual Elements to Tell a Story
3.2 Illustrated Confections Combine the Accuracy of a Typical Specimen with a Science Story
3.3 Digital Confections Combine the Accuracy of Photography with a Science Story
3.4 The Video Storyboard Is an Explicit Visual Confection
3.5 Artificial Intelligence Can Generate Photorealistic Images from Text Stories
3.6 Making Images Believable: Show Representative Images and State the Acquisition Method
3.7 Making Images Understood: Clearly Identify Regions of Interest with Suitable Framing, Labels, and Image Contrast
3.8 Avoid Dequantification and Technical Artifacts While Not Hesitating to Take the Picture
3.9 Accurate, Reproducible Imaging Requires a Set of Rules and Guidelines
3.10 The Structural Similarity Index Measure Quantifies Image Degradation
4 Image Capture by Eye
4.1 The Anatomy of the Eye Limits Its Spatial Resolution
4.2 The Dynamic Range of the Eye Exceeds 11 Orders of Magnitude of Light Intensity, and Intrascene Dynamic Range Is about 3 Orders
4.3 The Absorption Characteristics of Photopigments of the Eye Determines Its Wavelength Sensitivity
4.4 Refraction and Reflection Determine the Optical Properties of Materials
4.5 Movement of Light Through the Eye Depends on the Refractive Index and Thickness of the Lens, the Vitreous Humor, and Other Components
4.6 Neural Feedback in the Brain Dictates Temporal Resolution of the Eye
4.7 We Sense Size and Distribution in Large Spaces Using the Rules of Perspective
4.8 Three-Dimensional Representation Depends on Eye Focus from Different Angles
4.9 Binocular Vision Relaxes the Eye and Provides a Three-Dimensional View in Stereomicroscopes
5 Image Capture with Digital Cameras
5.1 Digital Cameras are Everywhere
5.2 Light Interacts with Silicon Chips to Produce Electrons
5.3 The Anatomy of the Camera Chip Limits Its Spatial Resolution
5.4 Camera Chips Convert Spatial Frequencies to Temporal Frequencies with a Series of Horizontal and Vertical Clocks
5.5 Different Charge-Coupled Device Architectures Have Different Read-out Mechanisms
5.6 The Digital Camera Image Starts Out as an Analog Signal that Becomes Digital
5.7 Video Broadcast Uses Legacy Frequency Standards
5.8 Codecs Code and Decode Digital Video
5.9 Digital Video Playback Formats Vary Widely, Reflecting Different Means of Transmission and Display
5.10 The Light Absorption Characteristics of the Metal Oxide Semiconductor, Its Filters, and Its Coatings Determine the Wavelength Sensitivity of the Camera Chip
5.11 Camera Noise and Potential Well Size Determine the Sensitivity of the Camera to Detectable Light
5.12 Scientific Camera Chips Increase Light Sensitivity and Amplify the Signal
5.13 Cameras for Electron Microscopy Use Regular Imaging Chips after Converting Electrons to Photons or Detect the Electron Signal Directly with Modified CMOS
5.14 Camera Lenses Place Additional Constraints on Spatial Resolution
5.15 Lens Aperture Controls Resolution, the Amount of Light, the Contrast, and the Depth of Field in a Digital Camera
5.16 Relative Magnification with a Photographic Lens Depends on Chip Size and Lens Focal Length
6 Image Capture by Scanning Systems
6.1 Scanners Build Images Point by Point, Line by Line, and Slice by Slice
6.2 Consumer-Grade Flatbed Scanners Provide Calibrated Color and Relatively High Resolution Over a Wide Field of View
6.3 Scientific-Grade Flatbed Scanners Can Detect Chemiluminescence, Fluorescence, and Phosphorescence
6.4 Scientific-Grade Scanning Systems Often Use Photomultiplier Tubes and Avalanche Photodiodes as the Camera
6.5 X-ray Planar Radiography Uses Both Scanning and Camera Technologies
6.6 Medical Computed Tomography Scans Rotate the X-ray Source and Sensor in a Helical Fashion Around the Body
6.7 Micro-CT and Nano-CT Scanners Use Both Hard and Soft X-Rays and Can Resolve Cellular Features
6.8 Macro Laser Scanners Acquire Three-Dimensional Images by Time-of-Flight or Structured Light
6.9 Laser Scanning and Spinning Disks Generate Images for Confocal Scanning Microscopy
6.10 Electron Beam Scanning Generates Images for Scanning Electron Microscopy
6.11 Atomic Force Microscopy Scans a Force-Sensing Probe Across the Sample
Section 2 Image Analysis
7 Measuring Selected Image Features
7.1 Digital Image Processing and Measurements are Part of the Image Metadata
7.2 The Subject Matter Determines the Choice of Image Analysis and Measurement Software
7.3 Recorded Paths, Regions of Interest, or Masks Save Selections for Measurement in Separate Images, Channels, and Overlays
7.4 Stereology and Photoquadrat Sampling Measure Unsegmented Images
7.5 Automatic Segmentation of Images Selects Image Features for Measurement Based on Common Feature Properties
7.6 Segmenting by Pixel Intensity Is Thresholding
7.7 Color Segmentation Looks for Similarities in a Three-Dimensional Color Space
7.8 Morphological Image Processing Separates or Connects Features
7.9 Measures of Pixel Intensity Quantify Light Absorption by and Emission from the Sample
7.10 Morphometric Measurements Quantify the Geometric Properties of Selections
7.11 Multi-dimensional Measurements Require Specific Filters
8 Optics and Image Formation
8.1 Optical Mechanics Can Be Well Described Mathematically
8.2 A Lens Divides Space Into Image and Object Spaces
8.3 The Lens Aperture Determines How Well the Lens Collects Radiation
8.4 The Diffraction Limit and the Contrast between Two Closely Spaced Self-Luminous Spots Give Rise to the Limits of Resolution
8.5 The Depth of the Three-Dimensional Slice of Object Space Remaining in Focus Is the Depth of Field
8.6 In Electromagnetic Lenses, Focal Length Produces Focus and Magnification
8.7 The Axial, Z-Dimensional, Point Spread Function Is a Measure of the Axial Resolution of High Numerical Aperture Lenses
8.8 Numerical Aperture and Magnification Determine the Light-Gathering Properties of the Microscope Objective
8.9 The Modulation (Contrast) Transfer Function Relates the Relative Contrast to Resolving Power in Fourier, or Frequency, Space
8.10 The Point Spread Function Convolves the Object to Generate the Image
8.11 Problems with the Focus of the Lens Arise from Lens Aberrations
8.12 Refractive Index Mismatch in the Sample Produces Spherical Aberration
8.13 Adaptive Optics Compensate for Refractive Index Changes and Aberration Introduced by Thick Samples
9 Contrast and Tone Control
9.1 The Subject Determines the Lighting
9.2 Light Measurements Use Two Different Standards: Photometric and Radiometric Units
9.3 The Light Emission and Contrast of Small Objects Limits Their Visibility
9.4 Use the Image Histogram to Adjust the Trade-off Between Depth of Field and Motion Blur
9.5 Use the Camera’s Light Meter to Detect Intrascene Dynamic Range and Set Exposure Compensation
9.6 Light Sources Produce a Variety of Colors and Intensities That Determine the Quality of the Illumination
9.7 Lasers and LEDs Provide Lighting with Specific Color and High Intensity
9.8 Change Light Values with Absorption, Reflectance, Interference, and Polarizing Filters
9.9 Köhler-Illuminated Microscopes Produce Conjugate Planes of Collimated Light from the Source and Specimen
9.10 Reflectors, Diffusers, and Filters Control Lighting in Macro-imaging
10 Processing with Digital Filters
10.1 Image Processing Occurs Before, During, and After Image Acquisition
10.2 Near-Neighbor Operations Modify the Value of a Target Pixel
10.3 Rank Filters Identify Noise and Remove It from Images
10.4 Convolution Can Be an Arithmetic Operation with Near Neighbors
10.5 Deblurring and Background Subtraction Remove Out-of-Focus Features from Optical Sections
10.6 Convolution Operations in Frequency Space Multiply the Fourier Transform of an Image by the Fourier Transform of the Convolution Mask
10.7 Tomographic Operations in Frequency Space Produce Better Back-Projections
10.8 Deconvolution in Frequency Space Removes Blur Introduced by the Optical System But Has a Problem with Noise
11 Spatial Analysis
11.1 Affine Transforms Produce Geometric Transformations
11.2 Measuring Geometric Distortion Requires Grid Calibration
11.3 Distortion Compensation Locally Adds and Subtracts Pixels
11.4 Shape Analysis Starts with the Identification of Landmarks, Then Registration
11.5 Grid Transformations are the Basis for Morphometric Examination of Shape Change in Populations
11.6 Principal Component Analysis and Canonical Variates Analysis Use Measures of Similarity as Coordinates
11.7 Convolutional Neural Networks Can Identify Shapes and Objects Using Deep Learning
11.8 Boundary Morphometrics Analyzes and Mathematically Describes the Edge of the Object
11.9 Measurement of Object Boundaries Can Reveal Fractal Relationships
11.10 Pixel Intensity–Based Colocalization Analysis Reports the Spatial Correlation of Overlapping Signals
11.11 Distance-Based Colocalization and Cluster Analysis Analyze the Spatial Proximity of Objects
11.12 Fluorescence Resonance Energy Transfer Occurs Over Small (1–10 nm) Distances
11.13 Image Correlations Reveal Patterns in Time and Space
12 Temporal Analysis
12.1 Representations of Molecular, Cellular, Tissue, and Organism Dynamics Require Video and Motion Graphics
12.2 Motion Graphics Editors Use Key Frames to Specify Motion
12.3 Motion Estimation Uses Successive Video Frames to Analyze Motion
12.4 Optic Flow Compares the Intensities of Pixels, Pixel Blocks, or Regions Between Frames
12.5 The Kymograph Uses Time as an Axis to Make a Visual Plot of the Object Motion
12.6 Particle Tracking Is a Form of Feature-Based Motion Estimation
12.7 Fluorescence Recovery After Photobleaching Shows Compartment Connectivity and the Movement of Molecules
12.8 Fluorescence Switching Also Shows Connectivity and Movement
12.9 Fluorescence Correlation Spectroscopy and Raster Image Correlation Spectroscopy Can Distinguish between Diffusion and Advection
12.10 Fluorescent Protein Timers Provide Tracking of Maturing Proteins as They Move through Compartments
13 Three-Dimensional Imaging, Modeling, and Analysis
13.1 Three-Dimensional Worlds Are Scalable and Require Both Camera and Actor Views
13.2 Stacking Multiple Adjacent Slices Can Produce a Three-Dimensional Volume or Surface
13.3 Structure-from-Motion Photogrammetry Reconstructs Three-Dimensional Surfaces Using Multiple Camera Views
13.4 Reconstruction of Aligned Images in Fourier Space Produces Three-Dimensional Volumes or Surfaces
13.5 Surface Rendering Produces Isosurface Polygon Meshes Generated from Contoured Intensities
13.6 Texture Maps of Object Isosurfaces Are Images or Movies
13.7 Ray Tracing Follows a Ray of Light Backward from the Eye or Camera to Its Source
13.8 Ray Tracing Shows the Object Based on Internal Intensities or Nearness to the Camera
13.9 Transfer Functions Discriminate Objects in Ray-Traced Three Dimensions
13.10 Four Dimensions, a Time Series of Three-Dimensional Volumes, Can Use Either Ray-Traced or Isosurface Rendering
13.11 Volumes Rendered with Splats and Texture Maps Provide Realistic Object-Ordered Reconstructions
13.12 Analysis of Three-Dimensional Volumes Uses the Same Approaches as Two-Dimensional Area Analysis But Includes Voxel Adjacency and Connectivity
13.13 Head-Mounted Displays and Holograms Achieve an Immersive Three-Dimensional Experience
Section 3 Image Modalities
14 Ultrasound Imaging
14.1 Ultrasonography Is a Cheap, High-Resolution, Deep-Penetration, Non-invasive Imaging Modality
14.2 Many Species Use Ultrasound and Infrasound for Communication and Detection
14.3 Sound Is a Compression, or Pressure, Wave
14.4 The Measurement of Audible Sound Intensity Is in Decibels
14.5 A Piezoelectric Transducer Creates the Ultrasound Wave
14.6 Different Tissues Have Different Acoustic Impedances
14.7 Sonic Wave Scatter Generates Speckle
14.8 Lateral Resolution Depends on Sound Frequency and the Size and Focal Length of the Transducer Elements
14.9 Axial Resolution Depends on the Duration of the Ultrasound Pulse
14.10 Scatter and Absorption by Tissues Attenuate the Ultrasound Beam
14.11 Amplitude Mode, Motion Mode, Brightness Mode, and Coherent Planar Wave Mode Are the Standard Modes for Clinical Practice
14.12 Doppler Scans of Moving Red Blood Cells Reveal Changes in Vascular Flows with Time and Provide the Basis for Functional Ultrasound Imaging
14.13 Microbubbles and Gas Vesicles Provide Ultrasound Contrast and Have Therapeutic Potential
15 Magnetic Resonance Imaging
15.1 Magnetic Resonance Imaging, Like Ultrasound, Performs Non-invasive Analysis without Ionizing Radiation
15.2 Magnetic Resonance Imaging Is an Image of the Hydrogen Nuclei in Fat and Water
15.3 Magnetic Resonance Imaging Sets up a Net Magnetization in Each Voxel That Is in Dynamic Equilibrium with the Applied Field
15.4 The Magnetic Field Imposed by Magnetic Resonance Imaging Makes Protons Spin Like Tops with the Same Tilt and Determines the Frequency of Precession
15.5 Magnetic Resonance Imaging Disturbs the Net Magnetization Equilibrium and Then Follows the Relaxation Back to Equilibrium
15.6 T2 Relaxation, or Spin–Spin Relaxation, Causes the Disappearance of Transverse (x-y Direction) Magnetization Through Dephasing
15.7 T1 Relaxation, or Spin-Lattice Relaxation, Causes the Disappearance of Longitudinal (z-Direction) Magnetization Through Energy Loss
15.8 Faraday Induction Produces the Magnetic Resonance Imaging Signal (in Volts) with Coils in the x-y Plane
15.9 Magnetic Gradients and Selective Radiofrequency Frequencies Generate Slices in the x, y, and z Directions
15.10 Acquiring a Gradient Echo Image Is a Highly Repetitive Process, Getting Information Independently in the x, y, and z Dimensions
15.11 Fast Low-Angle Shot Gradient Echo Imaging Speeds Up Imaging for T1-Weighted Images
15.12 The Spin-Echo Image Compensates for Magnetic Heterogeneities in the Tissue in T2-Weighted Images
15.13 Three-Dimensional Imaging Sequences Produce Higher Axial Resolution
15.14 Echo Planar Imaging Is a Fast Two-Dimensional Imaging Modality But Has Limited Resolving Power
15.15 Magnetic Resonance Angiography Analyzes Blood Velocity
15.16 Diffusion Tensor Imaging Visualizes and Compares Directional (Anisotropic) Diffusion Coefficients in a Tissue
15.17 Functional Magnetic Resonance Imaging Provides a Map of Brain Activity
15.18 Magnetic Resonance Imaging Contrast Agents Detect Small Lesions That Are Otherwise Difficult to Detect
16 Microscopy with Transmitted and Refracted Light
16.1 Brightfield Microscopy of Living Cells Uses Apertures and the Absorbance of Transmitted Light to Generate Contrast
16.2 Staining Fixed or Frozen Tissue Can Localize Large Polymers, Such as Proteins, Carbohydrates, and Nucleic Acids, But Is Less Effective for Lipids, Diffusible Ions, and Small Metabolites
16.3 Darkfield Microscopy Generates Contrast by Only Collecting the Refracted Light from the Specimen
16.4 Rheinberg Microscopy Generates Contrast by Producing Color Differences between Refracted and Unrefracted Light
16.5 Wave Interference from the Object and Its Surround Generates Contrast in Polarized Light, Differential Interference Contrast, and Phase Contrast Microscopies
16.6 Phase Contrast Microscopy Generates Contrast by Changing the Phase Difference Between the Light Coming from the Object and Its Surround
16.7 Polarized Light Reveals Order within a Specimen and Differences in Object Thickness
16.8 The Phase Difference Between the Slow and Fast Axes of Ordered Specimens Generates Contrast in Polarized Light Microscopy
16.9 Compensators Cancel Out or Add to the Retardation Introduced by the Sample, Making It Possible to Measure the Sample Retardation
16.10 Differential Interference Contrast Microscopy Is a Form of Polarized Light Microscopy That Generates Contrast Through Differential Interference of Two Slightly Separated Beams of Light
17 Microscopy Using Fluoresced and Reflected Light
17.1 Fluorescence and Autofluorescence: Excitation of Molecules by Light Leads to Rapid Re-emission of Lower Energy Light
17.2 Fluorescence Properties Vary Among Molecules and Depend on Their Environment
17.3 Fluorescent Labels Include Fluorescent Proteins, Fluorescent Labeling Agents, and Vital and Non-vital Fluorescence Affinity Dyes
17.4 Fluorescence Environment Sensors Include Single-Wavelength Ion Sensors, Ratio Imaging Ion Sensors, FRET Sensors, and FRET-FLIM Sensors
17.5 Widefield Microscopy for Reflective or Fluorescent Samples Uses Epi-illumination
17.6 Epi-polarization Microscopy Detects Reflective Ordered Inorganic or Organic Crystallites and Uses Nanogold and Gold Beads as Labels
17.7 To Optimize the Signal from the Sample, Use Specialized and Adaptive Optics
17.8 Confocal Microscopes Use Accurate, Mechanical Four-Dimensional Epi-illumination and Acquisition
17.9 The Best Light Sources for Fluorescence Match Fluorophore Absorbance
17.10 Filters, Mirrors, and Computational Approaches Optimize Signal While Limiting the Crosstalk Between Fluorophores
17.11 The Confocal Microscope Has Higher Axial and Lateral Resolving Power Than the Widefield Epi-illuminated Microscope, Some Designs Reaching Super resolution
17.12 Multiphoton Microscopy and Other Forms of Non-linear Optics Create Conditions for Near-Simultaneous Excitation of Fluorophores with Two or More Photons
18 Extending the Resolving Power of the Light Microscope in Time and Space
18.1 Superresolution Microscopy Extends the Resolving Power of the Light Microscope
18.2 Fluorescence Lifetime Imaging Uses a Temporal Resolving Power that Extends to Gigahertz Frequencies (Nanosecond Resolution)
18.3 Spatial Resolving Power Extends Past the Diffraction Limit of Light
18.4 Light Sheet Fluorescence Microscopy Achieves Fast Acquisition Times and Low Photon Dose
18.5 Lattice Light Sheets Increase Axial Resolving Power
18.6 Total Internal Reflection Microscopy and Glancing Incident Microscopy Produce a Thin Sheet of Excitation Energy Near the Coverslip
18.7 Structured Illumination Microscopy Improves Resolution with Harmonic Patterns That Reveal Higher Spatial Frequencies
18.8 Stimulated Emission Depletion and Reversible Saturable Optical Linear Fluorescence Transitions Superresolution Approaches Use Reversibly Saturable Fluorescence to Reduce the Size of the Illumination Spot
18.9 Single-Molecule Excitation Microscopies, Photo-Activated Localization Microscopy, and Stochastic Optical Reconstruction Microscopy Also Rely on Switchable Fluorophores
18.10 MINFLUX Combines Single-Molecule Localization with Structured Illumination to Get Resolution below 10 nm
19 Electron Microscopy
19.1 Electron Microscopy Uses a Transmitted Primary Electron Beam (Transmission Electron Micrography) or Secondary and Backscattered Electrons (Scanning Electron Micrography) to Image the Sample
19.2 Some Forms of Scanning Electron Micrography Use Unfixed Tissue at Low Vacuums (Relatively High Pressure)
19.3 Both Transmission Electron Micrography and Scanning Electron Micrography Use Frozen or Fixed Tissues
19.4 Critical Point Drying and Surface Coating with Metal Preserves Surface Structures and Enhances Contrast for Scanning Electron Micrography
19.5 Glass and Diamond Knives Make Ultrathin Sections on Ultramicrotomes
19.6 The Filament Type and the Condenser Lenses Control Illumination in Scanning Electron Micrography and Transmission Electron Micrography
19.7 The Objective Lens Aperture Blocks Scattered Electrons, Producing Contrast in Transmission Electron Micrography
19.8 High-Resolution Transmission Electron Micrography Uses Large (or No) Objective Apertures
19.9 Conventional Transmission Electron Micrography Provides a Cellular Context for Visualizing Organelles and Specific Molecules
19.10 Serial Section Transmitted Primary Electron Analysis Can Provide Three-Dimensional Cellular Structures
19.11 Scanning Electron Micrography Volume Microscopy Produces Three-Dimensional Microscopy at Nanometer Scales and Includes In-Lens Detectors and In-Column Sectioning Devices
19.12 Correlative Electron Microscopy Provides Ultrastructural Context for Fluorescence Studies
19.13 Tomographic Reconstruction of Transmission Electron Micrography Images Produces Very Thin (10-nm) Virtual Sections for High-Resolution Three-Dimensional Reconstruction
19.14 Cryo-Electron Microscopy Achieves Molecular Resolving Power (Resolution, 0.1–0.2 Nm) Using Single-Particle Analysis
Index
End User License Agreement
List of Tables
CHAPTER 01
Table 1.1 Laptop, Netbook, and...
Table 1.2 Display Standards.
Table 1.3 Image Legibility.
Table 1.4 Resolving Power Required...
Table 1.5 Partial Exchangeable Image...
CHAPTER 02
Table 2.1 Raster Graphics Image...
Table 2.2 Image Mode and...
Table 2.3 Still Image File...
CHAPTER 04
Table 4.1 The Refractive Index...
CHAPTER 05
Table 5.1 Comparison of Consumer...
Table 5.2 Comparison of World...
Table 5.3 Example Digital Video...
Table 5.4 International Standards and...
Table 5.5 Effect of Digitizer...
Panel 5.1 The relationship between...
Panel 5.2 The bandwidth of...
Panel 5.3 Relationships between...
CHAPTER 06
Table 6.1 Resolving Power of...
Table 6.2 Different Tissue Computed...
Table 6.3 Some Heavy Metal...
CHAPTER 07
Table 7.1 Representative Software for...
Table 7.2 Manual Selection Tools...
Panel 7.1 Image ratio operations...
CHAPTER 09
Table 9.1 Units of Radiometric...
Table 9.2 Aperture Settings Based...
Table 9.3 Color Temperatures for...
CHAPTER 10
Table 10.1 Speed and Programming...
Table 10.2 Difference Between a...
CHAPTER 12
Table 12.1 Photoactivatable Fluorescent...
Table 12.2 Non-reversibly Photoswitchable...
Table 12.3 Fluorescent Protein Timers...
CHAPTER 14
Table 14.1 Representative Values of...
CHAPTER 15
Table 15.1 Spin Values and...
Table 15.2 Average T1 and...
Table 15.3 Different Flow Types...
CHAPTER 16
Table 16.1 Vital Colored Dyes...
Table 16.2 Histochemical Stains for...
Table 16.3 Cytochemical and...
Table 16.4 Common Fixatives for...
CHAPTER 17
Table 17.1 Properties of Fluorescent...
Table 17.2 Common and Useful...
CHAPTER 18
Table 18.1 Typical Resolution Limits...
Table 18.2 Reversibly Photoswitchable and...
Table 18.3 Examples of Photoswitchable...
CHAPTER 19
Table 19.1 Electron Microscopy Stains...
List of Illustrations
CHAPTER 01
Figure 1.1 The fishes mosaic (second...
Figure 1.2 Detail from Figure...
Figure 1.3 Cell types found...
Figure 1.4 This famous picture...
Figure 1.5 Soybean protoplasts (cells...
Figure 1.6 (A) A photograph...
Figure 1.7 (A) A 600...
Figure 1.8 Useful range for...
Figure 1.9 (A) and (B...
Figure 1.10 An image of...
Figure 1.11 (A) Image of...
Figure 1.12 Half-tone cells...
Figure 1.13 Information loss during...
Figure 1.14 Scanning electron micrograph...
CHAPTER 02
Figure 2.1 Grayscale and color...
Figure 2.2 Algebraic definition of...
Figure 2.3 Grayscale spectra. (A...
Figure 2.4 (A) Red, green...
Figure 2.5 Grayscale images at...
Figure 2.6 Intensity plots of...
Figure 2.7 Grayscale images and...
Figure 2.8 Low-key grayscale...
Figure 2.9 High key grayscale...
Figure 2.10 Indexed color image...
Figure 2.11 Color image and...
Figure 2.12 Color versions of...
Figure 2.13 Intensity transfer graph...
Figure 2.14 Changing brightness and...
Figure 2.15 Gray-level histogram...
Figure 2.16 Color balancing a...
Figure 2.17 Contrast enhancement using...
Figure 2.18 Color balance and...
Figure 2.19 Histogram equalization. A...
Figure 2.20 Noticeable differences between...
CHAPTER 03
Figure 3.1 Bog asphodel illustration...
Figure 3.2 Bog asphodel. Narthecium...
Figure 3.3 Figures for identification...
Figure 3.4 Cladogram and skull...
Figure 3.5 Archived images of...
Figure 3.6 Two three-dimensional...
Figure 3.7 Digital voucher of...
Figure 3.8 Video storyboard for...
Figure 3.9 Photorealistic composition generated...
Figure 3.10 The figure legend...
Figure 3.11 Textbook examples of...
Figure 3.12 Scanning electron micrograph...
Figure 3.13 A Scanning electron...
Figure 3.14 Multiplexed image of...
Figure 3.15 Light micrographs of...
Figure 3.16 A comparison of...
Figure 3.17 Fraudulent altering of...
Figure 3.18 Structural similarity index...
CHAPTER 04
Figure 4.1 Anatomy of the...
Figure 4.2 Cross-section of...
Figure 4.3 The dynamic range...
Figure 4.4 The contrast transfer...
Figure 4.5 The wavelength (λ...
Figure 4.6 The wavelength of...
Figure 4.7 Diagram based on...
Figure 4.8 Wavelength absorbance of...
Figure 4.9 Diagram in Figure...
Figure 4.10 Color gamut of...
Figure 4.11 The slowing of...
Figure 4.12 Constructive versus destructive...
Figure 4.13 (A) The bending...
Figure 4.14 The difference between...
Figure 4.15 Three-dimensional (3D...
Figure 4.16 Red/blue anaglyph...
Figure 4.17 An electronically addressable...
Figure 4.18 Holography. (A) A...
Figure 4.19 Designs of the...
Figure 4.20 Stereomicroscopy images of...
CHAPTER 05
Figure 5.1 Circuit dynamics in...
Figure 5.2 Charge-coupled device...
Figure 5.3 Diagram of a...
Figure 5.4 Movement of charge...
Figure 5.5 Full-frame architecture...
Figure 5.6 Charge-coupled device...
Figure 5.7 Raster scan convention...
Figure 5.8 Frame-transfer architecture...
Figure 5.9 Interline-transfer architecture...
Figure 5.10 A microlens captures...
Figure 5.11 There are frequently...
Figure 5.12 The frequency of...
Figure 5.13 The spectral-dependence...
Figure 5.14 Calibrating color. (A...
Figure 5.15 Linear gamma of...
Figure 5.16 Total noise (dark...
Figure 5.17 Signal-to-noise...
Figure 5.18 The read-out...
Figure 5.19 Construction of a...
Figure 5.20 (A) The difference...
Figure 5.21 The design of...
Figure 5.22 An electron-bombarded...
Figure 5.23 Transmission electron microscope...
Figure 5.24 Comparison of images...
Figure 5.25 (A) Average of...
Figure 5.26 Digital single-lens...
Figure 5.27 Diffraction of light...
Figure 5.28 Comparison of the...
Figure 5.29 A comparison of...
Figure 5.30 The effect of...
Figure 5.31 A comparison of...
Figure 5.32 Image projected by...
CHAPTER 06
Figure 6.1 Internal view from...
Figure 6.2 Silver-stained dot...
Figure 6.3 Inverting a petri...
Figure 6.4 Peroxidase-conjugated secondary...
Figure 6.5 Jablonski energy diagram...
Figure 6.6 Difference gel electrophoresis...
Figure 6.7 X-radiography or...
Figure 6.8 Standard end-on...
Figure 6.9 Quantum efficiencies of...
Figure 6.10 Avalanche photodiode. Under...
Figure 6.11 A standard X...
Figure 6.12 Planar X-radiography...
Figure 6.13 Image acquisition and...
Figure 6.14 Construction of a...
Figure 6.15 Iodine-stained and...
Figure 6.16 Barley flower spike...
Figure 6.17 Micro computed tomography...
Figure 6.18 Micro computed tomography...
Figure 6.19 Lens arrangement for...
Figure 6.20 Range-finder laser...
Figure 6.21 Optical train of...
Figure 6.22 Optical train of...
Figure 6.23 Fluorescent microtubules in...
Figure 6.24 Optical train of...
Figure 6.25 Electron detector of...
Figure 6.26 (A) Cantilever of...
Figure 6.27 Peak force tapping...
Figure 6.28 Multichannel atomic force...
CHAPTER 07
Figure 7.1 An example of...
Figure 7.2 Every processing event...
Figure 7.3 Image processing and...
Figure 7.4 A graphical interface...
Figure 7.5 Using the free...
Figure 7.6 A freehand selection...
Figure 7.7 In ImageJ, the...
Figure 7.8 Conversion of selections...
Figure 7.9 In Photoshop, a...
Figure 7.10 In Photoshop, a...
Figure 7.11 For counting photoquadrats...
Figure 7.12 Comparison of point...
Figure 7.13 The Cavalieri method...
Figure 7.14 The disector grid...
Figure 7.15 Manual and automated...
Figure 7.16 Density slicing. (A...
Figure 7.17 Color spaces. (A...
Figure 7.18 Change in the...
Figure 7.19 Color segmentation of...
Figure 7.20 User-interactive selection...
Figure 7.21 Basic morphological image...
Figure 7.22 Network analysis using...
Figure 7.23 Separating touching objects...
Figure 7.24 Voronoi segmentation of...
Figure 7.25 Daughter colony identification...
Figure 7.26 Gray morphology operations...
Figure 7.27 Common measures and...
CHAPTER 08
Figure 8.1 A comparison of...
Figure 8.2 Ray diagram of...
Figure 8.3 The object, in...
Figure 8.4 (A) A thin...
Figure 8.5 (A) A non...
Figure 8.6 An Airy disk...
Figure 8.7 (A) If two...
Figure 8.8 The point spread...
Figure 8.9 Adding a condenser...
Figure 8.10 (A) Airy patterns...
Figure 8.11 (A) With a...
Figure 8.12 The lens equation...
Figure 8.13 Relationship of lens...
Figure 8.14 Changing the magnification...
Figure 8.15 The construction of...
Figure 8.16 The z-dimensional...
Figure 8.17 Frequencies and their...
Figure 8.18 Representation of a...
Figure 8.19 Multiple spatial frequencies...
Figure 8.20 The Fourier transform...
Figure 8.21 The modulation transfer...
Figure 8.22 The modulation transfer...
Figure 8.23 (A) The modulation...
Figure 8.24 Object convolution in...
Figure 8.25 Ray diagram of...
Figure 8.26 Correcting for spherical...
Figure 8.27 Ray diagram of...
Figure 8.28 An achromat lens...
Figure 8.29 Typical inscriptions on...
Figure 8.30 Chromatic aberration in...
Figure 8.31 Comatic aberration results...
Figure 8.32 (A) An image...
Figure 8.33 Spherical aberration results...
Figure 8.34 (A) Olympus 40...
Figure 8.35 (A) Comparison of...
Figure 8.36 (A) Diagrammatic comparison...
Figure 8.37 Sensing the wave...
Figure 8.38 Using a Shack...
Figure 8.39 A cell from...
CHAPTER 09
Figure 9.1 Phototropism of the...
Figure 9.2 Wavelength dependence of...
Figure 9.3 Production of one...
Figure 9.4 The spectral luminous...
Figure 9.5 Example metering modes...
Figure 9.6 Light intensity imposes...
Figure 9.7 The mode dial...
Figure 9.8 (A) Waterfall taken...
Figure 9.9 (A) A flower...
Figure 9.10 Effective dynamic range...
Figure 9.11 Exposure compensation setting...
Figure 9.12 Different color temperatures...
Figure 9.13 Some examples of...
Figure 9.14 Vertical cavity laser...
Figure 9.15 Constructive and destructive...
Figure 9.16 Transmission of long...
Figure 9.17 Production of plane...
Figure 9.18 The arrangement of...
Figure 9.19 The light ray...
Figure 9.20 Light path (yellow...
Figure 9.21 Illumination for macro...
Figure 9.22 Ring light source...
Figure 9.23 A colony of...
Figure 9.24 A fruit bat...
Figure 9.25 Mice expressing a...
CHAPTER 10
Figure 10.1 Picture style settings...
Figure 10.2 Sharpening filters used...
Figure 10.3 Example target pixel...
Figure 10.4 (A) Near-neighbor...
Figure 10.5 Circular masks of...
Figure 10.6 Consequences of a...
Figure 10.7 Noise filters. (A...
Figure 10.8 (A) A crab...
Figure 10.9 The variance filter...
Figure 10.10 Convolution operation that...
Figure 10.11 Sharpening, smoothing, and...
Figure 10.12 Effect of sharpening...
Figure 10.13 (A) An 11...
Figure 10.14 Sharpening, derivative, and...
Figure 10.15 Out-of-focus...
Figure 10.16 High- and low...
Figure 10.17 The convolution mask...
Figure 10.18 Graphical representation of...
Figure 10.19 Comparisons of maximum...
Figure 10.21 Madin-Darby canine...
Figure 10.20 Drosophila S2 cell...
Figure 10.22 Deconvolution of the...
CHAPTER 11
Figure 11.1 Geometric operations transform...
Figure 11.2 Aligning two histological...
Figure 11.3 Increasing accuracy of...
Figure 11.4 Convex curve shape...
Figure 11.5 Registration result from...
Figure 11.6 Three examples of...
Figure 11.7 (A) Piranha with...
Figure 11.8 Ontogenic change in...
Figure 11.9 Principal component analysis...
Figure 11.10 Principal components analysis...
Figure 11.11 A two-dimensional...
Figure 11.12 Canonical variates (CV...
Figure 11.13 Convolutional neural network...
Figure 11.14 A neural network...
Figure 11.15 Example training (red...
Figure 11.16 Elliptical Fourier functions...
Figure 11.17 Automated karyotyping using...
Figure 11.18 Dolphin identification using...
Figure 11.19 (A) to (D...
Figure 11.20 Three different domains...
Figure 11.21 Colocalization and their...
Figure 11.22 Simulated colocalizations with...
Figure 11.23 Colocalization of brain...
Figure 11.24 Statistical object distance...
Figure 11.25 Comparison of statistical...
Figure 11.26 Colocalization using transmission...
Figure 11.27 Fluorescence resonance energy...
Figure 11.28 Actin and calcium...
Figure 11.29 Cross-correlation between...
Figure 11.30 Multi-template matching...
Figure 11.31 Particle classification using...
CHAPTER 12
Figure 12.1 Challenges in particle...
Figure 12.2 Video sequence editor...
Figure 12.3 Video output properties...
Figure 12.4 The original series...
Figure 12.5 Using the Blender...
Figure 12.6 Locust jump kinematics...
Figure 12.7 Summary of the...
Figure 12.8 Aperture problem in...
Figure 12.9 (A) Motion relative...
Figure 12.10 Displaced frame difference...
Figure 12.11 Movement in the...
Figure 12.12 A thresholded sum...
Figure 12.13 Motion estimation of...
Figure 12.14 Two-dimensional (2D...
Figure 12.15 Rotation of a...
Figure 12.16 The three steps...
Figure 12.17 Assignment and display...
Figure 12.18 The local track...
Figure 12.19 Workflow for cell...
Figure 12.20 Diffusion, anomalous diffusion...
Figure 12.21 (A) Fluorescence recovery...
Figure 12.22 Inverse fluorescence recovery...
Figure 12.23 Analysis of movement...
Figure 12.24 Lamin B receptor...
Figure 12.25 Fluorescent protein photoactivation...
Figure 12.26 Photoactivation of photoactivated...
Figure 12.27 Photoconversion of the...
Figure 12.28 Two time series...
Figure 12.29 Fluorescence correlation spectroscopy...
Figure 12.30 Raster image correlation...
Figure 12.31 Raster image correlation...
Figure 12.32 Fluorescent protein timers...
Figure 12.33 Intracellular localization of...
Figure 12.34 mCherry/superfolded green...
CHAPTER 13
Figure 13.1 Macro scales in...
Figure 13.2 Micro scales in...
Figure 13.3 Blender three-dimensional...
Figure 13.4 Different Blender menu...
Figure 13.5 View frustum of...
Figure 13.6 Terms for movement...
Figure 13.7 Parenting a cube...
Figure 13.8 Reconstruction of tobacco...
Figure 13.9 Aliased isosurface volume...
Figure 13.10 First steps for...
Figure 13.11 The multiview stereo...
Figure 13.12 Three-dimensional reconstructed...
Figure 13.13 A frog surrounded...
Figure 13.14 Comparison of surface...
Figure 13.15 Transmission electron microscopy...
Figure 13.16 (A) Crystallographic reconstruction...
Figure 13.17 Three-dimensional reconstruction...
Figure 13.18 Marching cubes surface...
Figure 13.19 Rasterization of a...
Figure 13.20 Color and reflection...
Figure 13.21 Flat vs. smooth...
Figure 13.22 Vertex painting of...
Figure 13.23 Quad re-meshing...
Figure 13.24 The edges of...
Figure 13.25 Ray tracing a...
Figure 13.26 Ray tracing for...
Figure 13.27 Ray traversal of...
Figure 13.28 Ray tracing three...
Figure 13.29 Interpolation of 10...
Figure 13.30 An armature attached...
Figure 13.31 Volume rendering with...
Figure 13.32 Mereotopological classification of...
Figure 13.33 Three-dimensional (3D...
Figure 13.34 Measure three-dimensional...
Figure 13.35 Measuring distance and...
Figure 13.36 Hologram imaging. (A...
CHAPTER 14
Figure 14.1 Ultrasound analysis of...
Figure 14.2 A longitudinal wave...
Figure 14.3 (A) Construction elements...
Figure 14.4 A hand-held...
Figure 14.5 Snell’s...
Figure 14.6 Ultrasound image of...
Figure 14.7 Speckle is caused...
Figure 14.8 The trade-off...
Figure 14.9 The focus achieved...
Figure 14.10 Axial resolution depends...
Figure 14.11 Ophthalmic pachymetry uses...
Figure 14.12 M-mode data...
Figure 14.13 B-mode data...
Figure 14.14 Three-dimensional (3D...
Figure 14.15 Ultrasound three-dimensional...
Figure 14.16 Ultrafast planar imaging...
Figure 14.17 (A) Principle behind...
Figure 14.18 Functional ultrasound imaging...
Figure 14.19 Microbubble and gas...
Figure 14.20 Microvasculature of a...
Figure 14.21 Gas vesicle reporter...
CHAPTER 15
Figure 15.1 Example magnetic resonance...
Figure 15.2 Conventional full-body...
Figure 15.3 Comparison of magnetic...
Figure 15.4 The spin of...
Figure 15.5 Proton configurations. In...
Figure 15.6 When a spinning...
Figure 15.7 (A) When there...
Figure 15.8 Relaxation of net...
Figure 15.9 Faraday induction produces...
Figure 15.10 Different sets of...
Figure 15.11 Gradient coils in...
Figure 15.12 The gradient echo...
Figure 15.13 Map of x...
Figure 15.14 Different views produced...
Figure 15.15 Basic spin-echo...
Figure 15.16 Three-dimensional gradient...
Figure 15.17 Echo planar imaging...
Figure 15.18 Time-of-flight...
Figure 15.19 Maximum intensity projection...
Figure 15.20 Diffusion tensor imaging...
Figure 15.21 Design of a...
Figure 15.22 Functional magnetic resonance...
Figure 15.23 Effect of contrast...
CHAPTER 16
Figure 16.1 Inverted microscope for...
Figure 16.2 Serial staining of...
Figure 16.3 Immunoglobulin G (IgG...
Figure 16.4 Vibratome. Brain specimen...
Figure 16.5 Cryostat. (A) A...
Figure 16.6 Counterstained immunohistochemical preparations...
Figure 16.7 β-Glucuronidase (GUS...
Figure 16.8 A rotary microtome...
Figure 16.9 Darkfield image of...
Figure 16.10 Optical train of...
Figure 16.11 Low numerical aperture...
Figure 16.12 High numerical aperture...
Figure 16.13 Rheinberg illumination. (A...
Figure 16.14 Phase objects revealed...
Figure 16.15 Optical train of...
Figure 16.16 Positive phase contrast...
Figure 16.17 Negative phase contrast...
Figure 16.18 Modulation transfer function...
Figure 16.19 Alignment of phase...
Figure 16.20 The azimuth of...
Figure 16.21 Double refraction of...
Figure 16.22 Production of polarized...
Figure 16.23 In-phase interfering...
Figure 16.24 Production of circularly...
Figure 16.25 Optical path of...
Figure 16.26 Horizontal configuration of...
Figure 16.27 Intensity relationships between...
Figure 16.28 Pleurosigma diatoms viewed...
Figure 16.29 Part of a...
Figure 16.30 Production of the...
Figure 16.31 Determining the orientation...
Figure 16.32 A spiral xylem...
Figure 16.33 Optical train of...
Figure 16.34 Production of sheared...
Figure 16.35 Upper Wollaston recombination...
Figure 16.36 Generation of contrast...
Figure 16.37 Embossed appearance of...
Figure 16.38 Modulation transfer function...
Figure 16.39 Extended depth of...
CHAPTER 17
Figure 17.1 Autofluorescence of a...
Figure 17.2 Stokes shift of...
Figure 17.3 Modified Perrin-Jablonski...
Figure 17.4 Endoplasmic reticulum (ER...
Figure 17.5 Fluorescence spectra of...
Figure 17.6 Labeling proteins with...
Figure 17.7 Quantum dots. (A...
Figure 17.8 Multiplexing using multiple...
Figure 17.9 Single-wavelength calcium...
Figure 17.10 Ratio imaging calcium...
Figure 17.11 Cells from plant...
Figure 17.12 In vivo fluorescence...
Figure 17.13 Köhler...
Figure 17.14 Epi-polarization microscopy...
Figure 17.15 Combined fluorescence and...
Figure 17.16 Nikon Super Fluor...
Figure 17.17 Adaptive optics for...
Figure 17.18 Diagram of scan...
Figure 17.19 (A) A comparison...
Figure 17.20 Logarithmic plot of...
Figure 17.21 UV and White...
Figure 17.22 Diagrammatic representation of...
Figure 17.23 Two enhanced green...
Figure 17.24 Filter wheel arrangement...
Figure 17.25 Use of a...
Figure 17.26 Spectral unmixing of...
Figure 17.27 Axial point spread...
Figure 17.28 Definition of one...
Figure 17.29 The tradeoff between...
Figure 17.30 Comparison of lateral...
Figure 17.31 Comparison of normalized...
Figure 17.32 Comparison of confocal...
Figure 17.33 4Pi microscopy. (A...
Figure 17.34 Deep tissue imaging...
Figure 17.35 Non-linear photon...
Figure 17.36 Focus of photons...
Figure 17.37 Temporal focusing of...
Figure 17.38 Holographic microscope with...
CHAPTER 18
Figure 18.1 The lattice light...
Figure 18.2 Three-dimensional reconstruction...
Figure 18.3 Fluorescence lifetime microscopy...
Figure 18.4 Protein retention expansion...
Figure 18.5 Protein retention expansion...
Figure 18.6 Light sheet fluorescence...
Figure 18.7 Light sheet fluorescence...
Figure 18.8 Organization of lattice...
Figure 18.9 Illumination with hexagonal...
Figure 18.10 Three-dimensional cell...
Figure 18.11 Total internal reflection...
Figure 18.12 Total internal reflection...
Figure 18.13 Comparison of epifluorescence...
Figure 18.14 COS-7 cell...
Figure 18.15 Structured illumination microscopy...
Figure 18.16 Assembly and presentation...
Figure 18.17 Three beam three...
Figure 18.18 Fixed COS-7...
Figure 18.19 (A) Map of...
Figure 18.20 The production of...
Figure 18.21 Optical transfer function...
Figure 18.22 Comparison of total...
Figure 18.23 Live-cell three...
Figure 18.24 Two reversibly saturable...
Figure 18.25 Basic design and...
Figure 18.26 Two-color 4Pi...
Figure 18.27 Parallelized or widefield...
Figure 18.28 Resolution of reversible...
Figure 18.29 Example of widefield...
Figure 18.30 Principle of single...
Figure 18.31 The number of...
Figure 18.32 Limits to localization...
Figure 18.33 The resolution of...
Figure 18.34 z-dimensional analysis...
Figure 18.35 STORM images of...
Figure 18.36 Whole COS-7...
Figure 18.37 Minimal number of...
Figure 18.38 Minimal photon fluxes...
Figure 18.39 The targeted coordinate...
Figure 18.40 Nuclear pore subunits...
CHAPTER 19
Figure 19.1 Head of the...
Figure 19.2 Interaction of a...
Figure 19.3 Design of the...
Figure 19.4 A commercial backscatter...
Figure 19.5 Pressure limited apertures...
Figure 19.6 The environmental scanning...
Figure 19.7 Radish leaf imaged...
Figure 19.8 Freeze-dried pollen...
Figure 19.9 (A) Chemical structure...
Figure 19.10 Chemical reactions of...
Figure 19.11 Superior preservation with...
Figure 19.12 Ultramicrotome with a...
Figure 19.13 Design of a...
Figure 19.14 Phase diagram of...
Figure 19.15 A simplified view...
Figure 19.16 During embedment in...
Figure 19.17 An ultramicrotome showing...
Figure 19.18 The position and...
Figure 19.19 Getting a thin...
Figure 19.20 Different kinds of...
Figure 19.21 Diagram of the...
Figure 19.22 Electron guns used...
Figure 19.23 Focusing the electron...
Figure 19.24 Interaction of mass...
Figure 19.25 Equations for the...
Figure 19.26 Generation of Fresnel...
Figure 19.27 Contrast transfer functions...
Figure 19.28 Thon rings. Fourier...
Figure 19.29 Defocus contrast generation...
Figure 19.30 Comparison of the...
Figure 19.31 Transmission electron microscopy...
Figure 19.32 Tangential longitudinal sections...
Figure 19.33 Transmission electron microscopy...
Figure 19.34 Transmission electron microscopy...
Figure 19.35 Transmission electron microscopy...
Figure 19.36 Transmission electron microscopy...
Figure 19.37 Acid phosphatase staining...
Figure 19.38 Construction of three...
Figure 19.39 Volume of interaction...
Figure 19.40 An in-lens...
Figure 19.41 Different approaches to...
Figure 19.42 Sectioning for array...
Figure 19.43 An array tomography...
Figure 19.44 Filling the axial...
Figure 19.45 The design of...
Figure 19.46 Focused ion beam...
Figure 19.47 Development of the...
Figure 19.48 Commercial integrated correlative...
Figure 19.49 Tetracysteine organoarsenide reactive...
Figure 19.50 Superresolution light microscopy...
Figure 19.51 Rotation of grids...
Figure 19.52 Comparison of the...
Figure 19.53 Workflow with light...
Figure 19.54 CopI vesicle coats...
Figure 19.55 Grids for single...
Figure 19.56 Assembly intermediates of...
Guide
Cover
Imaging Life
Copyright
Table of Contents
Preface
Acknowledgments
About the Companion Website
Begin Reading
Index
End User License Agreement
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Preface
Imaging Life Has Three Sections: Image Acquisition, Image Analysis, and Imaging Modalities
The first section, Image Acquisition, lays the foundation for imaging by extending prior knowledge about image structure (Chapter 1), image contrast (Chapter 2), and proper image representation (Chapter 3). The chapters on imaging by eye (Chapter 4), by camera (Chapter 5), and by scanners (Chapter 6) relate to prior knowledge of sight, digital (e.g., cell phone) cameras, and flatbed scanners.
The second section, Image Analysis, starts with how to select features in an image and measure them (Chapter 7). With this knowledge comes the realization that there are limits to image measurement set by the optics of the system (Chapter 8), a system that includes the sample and the light- and radiation-gathering properties of the instrumentation. For light-based imaging, the nature of the lighting and its ability to generate contrast (Chapter 9) optimize the image data acquired for analysis. A wide variety of image filters (Chapter 10) that operate in real and reciprocal space make it possible to display or measure large amounts of data or data with low signal. Spatial measurement in two dimensions (Chapter 11), measurement in time (Chapter 12), and processing and measurement in three dimensions (Chapter 13) cover many of the tenets of image analysis at the macro and micro levels.
The third section, Imaging Modalities, builds on some of the modalities necessarily introduced in previous chapters, such as computed tomography (CT) scanning, basic microscopy, and camera optics. Many students interested in biological imaging are particularly interested in biomedical modalities. Unfortunately, most of the classes in biomedical imaging are not part of standard biology curricula but in biomedical engineering. Likewise, students in biomedical engineering often get less exposure to microscopy-related modalities. This section brings the two together.
The book does not use examples from materials science, although some materials science students may find it useful.
Imaging Life Can Be Either a Lecture Course or a Lab Course
This book can stand alone as a text for a lecture course on biological imaging intended for junior or senior undergraduates or first- and second-year graduate students in life sciences. The annotated references section at the end of each chapter provides the URLs for supplementary videos available from iBiology.com and other recommended sites. In addition, the recommended text-based internet, print, and electronic resources, such as microscopyu.com, provide expert and in-depth materials on digital imaging and light microscopy. However, these resources focus on particular imaging modalities and exclude some (e.g., single-lens reflex cameras, ultrasound, CT scanning, magnetic resonance imaging [MRI], structure from motion). The objective of this book is to serve as a solid foundation in imaging, emphasizing the shared concepts of these imaging approaches. In this vein, the book does not attempt to be encyclopedic but instead provides a gateway to the ongoing advances in biological imaging.
The author’s biology course non-linearly builds off this text with weekly computer sessions. Every third class session covers practical image processing, analysis, and presentations with still, video, and three-dimensional (3D) images. Although these computer labs may introduce Adobe Photoshop and Illustrator and MATLAB and Simulink (available on our university computers), the class primarily uses open-source software (i.e., GIMP2, Inkscape, FIJI [FIJI Is Just ImageJ], Icy, and Blender). The course emphasizes open-source imaging. Many open-source software packages use published and archived algorithms. This is better for science, making image processing more reproducible. They are also free or at least cheaper for students and university labs.
The images the students acquire on their own with their cell phones, in the lab (if taught as a lab course), or from online scientific databases (e.g., Morphosource.org) are the subjects of these tutorials. The initial tutorials simply introduce basic features of the software that are fun, such as 3D model reconstruction in FIJI of CT scans from Morphosource, and informative, such as how to control image size, resolving power, and compression for analysis and publication. Although simple, the tutorials address major pedagogical challenges caused by the casual, uninformed use of digital images. The tutorials combine the opportunity to judge and analyze images acquired by the students with the opportunity to learn about the software. They are the basis for weekly assignments. Later tutorials provide instruction on video and 3D editing, as well as more advanced image processing (filters and deconvolution) and measurement. An important learning outcome for the course is that the students can use this software to rigorously analyze and manage imaging data, as well as generate publication-quality images, videos, and presentations.
This book can also serve as a text for a laboratory course, along with an accompanying lab manual that contains protocols for experiments and instructions for the operation of particular instruments. The current lab manual is available on request, but it has instructions for equipment at Texas A&M University. Besides cell phones, digital single-lens reflex cameras, flatbed scanners, and stereo-microscopes, the first quarter of the lab includes brightfield transmitted light microscopy and fluorescence microscopy. Assigning Chapter 16 on transmitted light microscopy and Chapter 17 on epi-illuminated light microscopy early in the course supplements the lab manual information and introduces the students to microscopy before covering it during class time. Almost all the students have worked with microscopes before, but many have not captured images that require better set-up (e.g., Köhler illumination with a sub-stage condenser) and a more thorough understanding of image acquisition and lighting.
The lab course involves students using imaging instrumentation. All the students have access to cameras on their cell phones, and most labs have access to brightfield microscopy, perhaps with various contrast-generating optical configurations (darkfield, phase contrast, differential interference contrast). Access to fluorescence microscopy is also important. One of the anticipated learning outcomes for the lab course is that students can troubleshoot optical systems. For this reason, it is important that they take apart, clean, and correctly reassemble and align some optical instruments for calibrated image acquisition. With this knowledge, they can become responsible users of more expensive, multi-user equipment. Some might even learn how to build their own!
Access to CT scanning, confocal microscopy, multi-photon microscopy, ultrasonography, MRI, light sheet microscopy, superresolution light microscopy, and electron microscopy will vary by institution. Students can use remote learning to view demonstrations of how to set up and use them. Many of these instruments have linkage to the internet. Zoom (or other live video) presentations provide access to operator activity for the entire class and are therefore preferable for larger classes that need to see the operation of a machine with restricted access. Several instrument companies provide video demonstrations of the use of their instruments. Live video is more informative, particularly if the students read about the instruments first with a distilled set of instrument-operating instructions, so they can then ask questions of the operators. Example images from the tutorials for most of these modalities should be available for student analysis.
Acknowledgments
Peter Hepler and Paul Green taught a light and electron microscopy course at Stanford University that introduced me to the topic while I was a graduate student of Peter Ray. After working in the lab of Ralph Quatrano, I acquired additional expertise in light and electron microscopy as a post-doc with Larry Fowke and Fred Constabel at the University of Saskatchewan and collaborating with Hilton Mollenhauer at Texas A&M University. They were all great mentors.
