Imaging Life Image Acquisition and Analysis in Biology and Medicine 1st Edition by Lawrence Griffing 1119081572 9781119081579

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ISBN-10 :  1119081572 

ISBN-13 : 9781119081579

Author : Lawrence R. Griffing

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.

 

Imaging Life: Image Acquisition and Analysis in Biology and Medicine 1st Table of contents:

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 Sing

 

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