11/29/2015 1 Image Processing. 11/29/2015 2 Systems and Software Image file formats Image processing...
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Transcript of 11/29/2015 1 Image Processing. 11/29/2015 2 Systems and Software Image file formats Image processing...
04/21/23 1
Image Processing
04/21/23 2
Systems and Software
• Image file formats
• Image processing applications
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Image file formats
• Pixel images
• Graphic images
• Composite image files
• Images in manuscripts Graphic
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Format issues
• Color, monochrome
• Capability– Gray value
– Graphics
– Calibration: size, sensitometry, etc...
• Compatibility
• Compression
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Common formats• TIFF
– Common usage
– Versatile: pixel, calibration
– Pixel-based regions
– Dialects
• Raw
• EPS– versatile
– primarily, printed document format
• PICT, PICS– well developed standard
– pixel and graphic format
– primarily Macintosh
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Common formats (more)
• DICOM– used in medical imaging
• Graphic arts:– MPEG, JPEG, GIF
Import menu in NIH Image
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Image processing software functions
• Image acquisition
• Image processing
• Quantitative data extraction
• Annotation and editing
• Database management
• File format conversion
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Image processing software structure
• Task-dedicated
• General purpose
• Scripting/macro language
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Software package examples
• NIH Image– http://rsb.info.nih.gov/nih-image/
• Adobe Photoshop
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Examples
• NIH Image & file conversion
• Annotation
• Copying, pasting
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Software selection issues
• Compatibility with computer
• Functionality– Control of acquisition hardware (compatibility)
– Range of processing algorithms
• Productivity
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Image processing
• Contrast manipulation and enhancement
• Dyadic operation
• Image filtering
• Regions and region operation
• Image analysis
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Monadic (one picture) operations
• Contrast manipulation and enhancement
• LUT operation– brightness, contrast
– False color
• Gray value remap (apply LUT)
• Arithmetic operations
• Scale and rotate (also morphing)
• Other single image operations
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Monadic operations
• Profile plot (and projection)
• Gray value histogram
04/21/23 15
Display operations
Gray scale manipulation
False color manipulation Enlarge, contract, scale to fit window
See Adobe Illustrator for image editing
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Example of rotation
(a) (b) (c)
(a) Original image, (b) rotated by 1.2°, nearest neighbor interpolation, (c) rotated by 1.2 °, bilinear interpolation.
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Examples of histograms
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Dyadic (two image) operations
Dyadic (two variable) function:ζ = f (ξ ,υ )
Example
ζ = ξ + υ
I3(x, y) = I1(x, y) + I2 (x, y)
Examples: addition, multiplication, subtraction, and division.
Problem: finite numerical range fro picture gray value leads to roundoff.
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Example: shading correctionModel :
M(x, y) =G(x,y)I(x,y) +B(x,y) I(x,y) - 'true' ,image M(x,y) - measured image
Calibration method : set I(x, y) to zero, measure B(x, y), the black field.
set I(x, y) to 1, measure W(x, y)=G(x,y) +B(x,y) .the white field
computeG(x, y) =W(x,y)−B(x,y)
Shading correction:
Measure M(x, y)
Compute the corrected signal.
I(x, y) =(M(x,y)−B(x,y)) /G(x,y)
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Image Filtering
• Convolution
• Rank filtering
• Edge detection
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Image filtering (conceptual)
Convolution:
I2 (x, y) = I1 (u,v)h(x−u, y−v)dudv∫∫Gradient:
I2 (x,y) =∂I1(x,y)
∂xLaplacian:
I2 (x,y) =∇2 I1 (x,y) ≡∂2 I1 (x,y)
∂x2+ ∂2 I1(x,y)
∂y2
Discrete:
I2 (i, j ) =k=−N
N∑ h(
l=−N
N∑ k, l )I1 (i−k, j−l )
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Convolution with the Fourier transform
Fourier transform:
FT[ I(x, y)](ξ ,η) = I(x,y)e−2π i(ξx+ηy)dxdy∫∫
FT[ I2 (x,y)](ξ ,η) =FT[ I1 (x,y)](ξ ,η)×FT[h(x,y)](ξ ,η)
I2 =FT−1 [FT[ I1(x, y)]×FT[h(x,y)][ ]
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Examples of linear filters
Low-pass (smooth, Gauss) High-pass (sharpen, Mexican Hat, DOG) Shadow First derivative (shadow, find edges)
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Example of smoothing
Smoothing (5 times)
Before After
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ConvolveOriginal Gauss (7x7) ‘Mexican hat’(17x17)
Grad N Grad W
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Nonlinear filters
Median (noise eliminate and enhance) Minimum (erode) Maximum (dilate) Open Close
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Example of morphological (rank) filters
Opening (3)
Closing (3)
Original
Minimum,erode (1)
Maximum,dilate (1)
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Example, grain counting
Grain image Maximum (5)
Difference
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Regions and region operation
• Thresholding, density slicing
• Erosion, dilation (rank filtering)
• Skeletons
04/21/23 30
Image analysis
• Sensitometric
• Geometric
• Categorical (image recognition)
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Segmentation
• Geometrical shapes
• Interactive (outlining)
• Thresholding
• Filling
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Segmentation - connected area
Density slice, then use wand (auto-outline) to trace outline. Outline written on image with ‘draw boundary’ command.
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Region of interest (ROI) analysis
• Area
• Perimeter
• Shape
• Average value
• Standard deviation
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Example of measurement
126.00 132.48 39.27 14.37 11.16
Area Mean Length MinorMajor
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Summary
• Paradigm: scanning, preprocessing, analysis
• Success depends on all parts of analysis chain
• Quality control– Quantitative evaluation of performance
– Test samples
– Repeated analysis
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Summary
Digital image acquisition - physical data to numbers Control of accuracy
Experimental design
Experimental preparation
Scanner Scanner in the lab
Complex device, operation and maintenance
Quality control mandatory
04/21/23 37
Houston, we have a problem.
COMPARISON OF STANDARDS
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Nominal value (uCi/g)
Calibrated value (uCi/g)