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Introduction to Image Processing and Analysis
Starting Soon…
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Overview
• Analytical Imaging Process or Workflow• What is an Image• Image Quality and Other Issues • Image Processing• Analysis• Advanced Techniques
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• Sample Preparation*• Acquisition – how do we acquire an image into the
computer?• Enhancement – how do we make it look better for
visualization. How do we process the image to extract information?
• Identification – which attributes of the image are we interested in?
• Measurement – what information can we obtain?• Report Generation – how can we present this
information?• Archive – how can we store the information?
The Analytical Imaging Process
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What is an Image?
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What is an image?
A digital image is a numerical representation of a “picture” in a 2D array
– a set of numbers interpreted by the computer which creates a visual representation that is understood by humans.
255, 255, 199143, 97, 18732, 12, 3423, 22, 11
244, 198, 179123, 94, 19532, 43, 5213, 32, 11
253, 217, 23468, 185, 9713, 12, 2711, 14, 26
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Pixels are identified by their position in a grid (two-dimensional array), referenced by its row (x), and column (y).
Image: Pixel Array
Pixel = Picture Element
Each pixel is a sample of an original image.
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Binary Digits (bits)
Bitonal
0 = Black1 = White
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• BIT DEPTH is determined by the number of bits used to define each pixel. The greater the bit depth, the greater the number of tones (grayscale or color) that can be represented.
What is bit-depth?
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Bit Depth
How many gray levels between the darkest and brightest areas
8-bit 28 = 256 gray values
12-bit 212 = 4,096 gray values
16-bit 216 = 65,536 gray values
How many gray levels between the darkest and brightest areas
8-bit 28 = 256 gray values
12-bit 212 = 4,096 gray values
16-bit 216 = 65,536 gray values
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What Makes a Good Image
• Nothing can substitute for excellent sample preparation
• Make full use of the dynamic range of your detector (PMT, CCD etc)*
• Avoid saturation of detector
• Properly aligned microscope
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What Makes a Good Image
Uneven Illumination
• White Balance
• Same exposure and illumination per experiment
• Proper microscope alignment
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What Makes a Good Image
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How to increase S/N?• Increase signal
– Proper filter selection for fluorescence microscopy
– More efficient excitation– Improved signal capture
• Higher NA objective• More sensitive detector
and/or cooled camera• Increased exposure time
– Reduce photobleaching
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How to increase S/N?
• Decrease noise– Reduce background
fluorescence• Non-specific binding• autofluorescence
– Reduce cross talk– Longer integration
time– Averaging removes
random noise– Image filtering
methods (Gaussian, Median etc)
– Reduce system noise
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1x10.108 mm/pixel
2x20.216 mm/pixel
3x30.324 mm/pixel
4x40.432 mm/pixel
Same display settings
Different contrast and brightnessImages courtesy of Claire M. Brown, PhD, McGill University Department of Biochemistry
Camera Binning
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The number of pixels in the image must be sufficient to distinguish features of interest:
Resolution
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Image Enhancement
and Processing
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Image Processing
• Why?– Prepares image/features for analysis– Remove or reduce noise– Enhance or reduce image features
• Visualization or for analysis purposes
• Important point about image processing.• These operations may or may not change the data, you
need to be aware of this and what it means to your results.
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There are basic ways to enhance an image:
• Modify its intensity index: brightness, contrast, gamma• Background correction: flatten, compensate for irregularities• Apply a spatial filter or operation: sharpen, low-pass, edge
Advanced enhancement
• Manipulate the image frequencies: Fourier transform• Morphological transformations: erode, dilate, both…
Image Enhancement
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Low dynamic range
Medium contrast
0 72.8571 182.143
0
10000
20000
30000
Full dynamic range
Good contrast
0 72.8571 182.143
0
10000
20000
30000
Enhancement: Grey-value Histogram Stretch
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brightness contrast All Threelinear gamma 0.5gamma 2
Image Intensity
Dis
play
Int
ensi
ty
Images courtesy of Claire M. Brown, PhD, McGill University Department of Biochemistry
Image Enhancement: All Three
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Background Correction
• Background correction• Image processing method-Flatten Filter• Image Collection-Align system, maintain exp
time and illumination
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Commonly used convolution filters:
• Low-pass: blurs, or smoothes an object
• Sharpen: enhances all intensity transitions
• Hi-pass: enhances high frequency information to increase contrast.
• Median: removes random impulse noise
Advanced Filters:
• Sigma: removes local impulse noise without
• Large Spectral: Larger kernal size Lo- and Hi-Pass filter, edge and Band Pass
Image Enhancement: Spatial Filtering
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Examples of filter kernels: -3 -3 -3 -3 0 +3 -2 -2 -2 0 0 0 -3 0 +3 -2 +9 -2+3 +3 +3 -3 0 +3 -2 -2 -2 horizontal vertical sharpeningedge detect edge detect filter
Spatial Filters
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Examples
Image Enhancement: Sharpening
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Provides a method for combining two or more images into a single resultant image. The final results will depend on the operation performed.
Logical:• AND• OR• NOT• NAND• XOR
Arithmetic:• Add• Average• Subtract • Difference• Max & Min
Arithmetic operators
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Image Operations
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Red
Green
Blue
Processing / EnhancementExtract Images
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DAPI
Cy3
FITC
Processing / EnhancementMerge Images
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Image Analysis
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Data Analysis Considerations
• What measurements are meaningful?
• How can I optimize my image capture to improve measurement quality?
• What image features need to be preserved?
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What Measurements are Meaningful?
• Spatial– Length, roundness, xy coordinates
• Temporal– Velocity, distance traveled, vector
• Volumetric– Shape change, spatial relationship
• Intensity– Temporal changes, ratiometric comparisons
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What Image Features to Preserve?
• Intensity• Spatial• Bit depth
• Some of the choices you make now can impact your ability to measure raw data later
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Calibrated Measurements
• Manual• Automated
• Both require that the image be calibrated in advance– How many pixels
represent a given distance?
– How large an intensity change indicates a positive result?
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Types of Measurements
• Histogram• Line Profile• Manual Measurements
– Length, area, angle, thickness, count. • Automated Measurements
– #of objects, roundness, size, % area, etc.• Object Tracking
– Distance, velocity• Edge Detection and Measurement
– Distance between features• Volume
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Histogram
• Used to evaluate the intensity information and/or analyze the image
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0 100 200 300
0
100
200
0 100 200 300
0
100
200
Line Profile/Automated Edge Detection
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Thresholding / Segmentation
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Measurement of Objects
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Area Percentage Measurements
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Counting Objects within Objects
• The ability to define primary objects in one image (e.g. cells nuclei, composites, etc.) and measure objects from another image that reside within these primary objects.
• Example, how many DNA repair sites are in each nuclei?
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Object Tracking
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Data Output
Area
Objects
172 226.20 280.40 334.60 388.80
0
2
4
6
Correlation: .1139581
Area
Roundness
200 300 400
1
1.10
1.20
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Advanced Techniques
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Extended Depth of Field
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OBJECTIVE
Axial Resolution
Depth of Field
Z P
lane
sExtended Depth of Field cont…
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“Stitching” of Images through Automatic Microscope and Stage control
Tiling
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Colocalization
Intensities in Time-Series
Fluorescence Measurements
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Dan MulvihillCell Developmental Biology GroupUniversity of Kent
Raw Image Deconvolved Threshold
Deconvolution - Analysis
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Deconvolution - Visualization
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Volume renderingReal Time InteractionClipping
Surface renderingVolume of Interest
Three Dimensional Reconstruction and Analysis
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Macro Recording
• A series of mouse clicks can be recorded
• Simplifies repeated operations.
• Reproducibility.
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Credits
• Simon Watkins – University of Pittsburgh- CBI
• MDIBL/Bar Harbor - QFM course• MBL/Woods Hole – AQLM course• MBL/Woods Hole – OMIB course• UTHSCSA- Optical Microscopy course• Molecular Expressions Web Site-Mike
Davidson
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Thank You For Attending…
Introduction to Image ProcessingPresented by Jeff Knipe
For more information, please contact:
[email protected]@mediacy.comwww.mediacy.com
Sponsored by: Media Cybernetics
