June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.comJune 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
An Introduction to Image Processing presented by
Nick BeaversApplications Specialist
Media Cybernetics
From Images
to Answers
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
What is an image?
An image is a numerical representation of a “picture”
– 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
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
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
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Binary Digits (bits)
Bitonal
0 = Black1 = White
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
• 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?
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Bits Tones Binary Digits Array
1 bit (21) 2 tones
(0 – 1)
0 or 1
2 bits (22) 4 tones
(0 – 3)
00, 01, 10, 11
3 bits (23) 8 tones
(0 – 7)
000, 001, 010, 011, 100, 101, 110, 111
4 bits (24) 16 tones
(0 – 15)
0000, 0001, 0010, 0100, 1000, 0011, 0101, 1001, 1010, 0111,1011, 1100, 1101, 1110, 1111, 0110
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Bits Tones Binary Digits Array
8 bit (28) 256 tones
(0 – 255)
00000000, 00000001, etc.
12 bits (212) 4,096 tones
(0 – 4,095)
000000000000, 000000000001,etc.
16 bits (216) 65,536 tones
(0 – 65,535)
0000000000000000, 0000000000000001, etc.
24 bits (224) 16.7 million tones
(0 – 16,699,999)
000000000000000000000000, 000000000000000000000001, etc.
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
The number of pixels in the image must be sufficient to distinguish features of interest:
Resolution
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Aliasing: Distortion introduced when an image of high resolution is sampled by a detector of lower resolution.
Resolution
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
1x1
0.108 mm/pixel
2x2
0.216 mm/pixel
3x3
0.324 mm/pixel
4x4
0.432 mm/pixel
Same display settings
Different contrast and brightness
Images courtesy of Claire M. Brown, PhD, McGill University Department of Biochemistry
Binning
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Zoom 1
0.140 mm/pixel
Zoom 3
0.047 mm/pixel
Zoom 6
0.023 mm/pixel
Zoom 8
0.017 mm/pixel
Zoom 10
0.014 mm/pixel
63X NA 1.4 Oil
Images courtesy of Claire M. Brown, PhD, McGill University Department of Biochemistry
Distance (mm)
0.0 0.5 1.0 1.5 2.0
Inte
nsity
0
200
400
600
800
1000
1200
1400
1600
1800
Zoom 1
Zoom 3
Zoom 6
Zoom 8
Zoom 10
Over Sampling
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
• Input device – the source of the images; camera, microscope, scanner, etc
• Interface hardware – the connection between the input device and the computer; takes the input signal and digitizes it for use on a PC
• Imaging software – the user interface to all the imaging components
• Output devices – printers, image storage devices, monitors
What components are involved in imaging?
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
• Capture only or “driver” software: software used to capture and save an image from a device – developed mostly by hardware manufacturers
Example: TWAIN drivers
• “Imaging” software, Image Editing, Photo Retouching: software used primarily in home and general business applications, mostly consumer oriented
Example: Adobe PhotoShop, Microsoft Photo Editor, Image Tools
• Basic Image Measurement Software: used for basic image capture, enhancement, with simple measuring tools
Example: Image-Pro Express
• General Analytical Image Analysis Software: used in scientific/industrial analysis of images to generate proven data
Example: Image-Pro Plus
• Vertical Market Image Analysis Software: used to solve specific imaging problems in a related industry
Example: Array-Pro, Scope-Pro, Materials-Pro Analyzer, or vision software libraries
Types of Imaging Software
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
• Sample Preparation*
• Acquisition – how do we acquire an image into the computer?
• Enhancement – how do we make it look better 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
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
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
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Low dynamic
range
Medium
contrast0 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
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
brightness contrast All Threelinear gamma 0.5gamma 2
Image Intensity
Dis
pla
y In
tensi
ty
Images courtesy of Claire M. Brown, PhD, McGill University Department of Biochemistry
Image Enhancement: All Three
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Background Automatic flatten of Background
Original
Image Enhancement: Background Correction
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Commonly used convolution filters:
• Low-pass: blurs, or smoothes an object
• Sharpen: enhances all intensity transitions
• Hi-pass: creates harsh intensity variations
• Median: removes random impulse noise
Advanced Filters:
• Sigma: removes local impulse noise without
Image Enhancement: Spatial Filtering
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Kernel size
Preview window
Filter description
Sharpen Filter
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Examples
Image Enhancement: Sharpening
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
DAPI
Cy3
FITC
Processing / EnhancementMerge Images
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Red
Green
Blue
Processing / EnhancementExtract Images
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Extended Depth of Field
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
“Stitching” of
Images
through
Automatic
Microscope
and Stage
control
Tiling
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
• Thresholding techniques – Identify objects, based on
intensity variations from background or other objects of
non-interest
•Grayscale
•Color
• Area of Interest (AOI) – manually defining regions of
interest
Identification
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Thresholding / Segmentation
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
• Object splitting – using filters, or manually splitting by drawing lines between touching objects.
• Guard frame – when working on multiple fields side by side, we may need to specify that object touching the borders of the image.
• Pseudo-color – adds false color to the image to show changes in gray values not noticeable to the human eye.
Pre Measurement Steps
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
• Size (area, perimeter, length, etc)
• Shape (roundness, aspect ratio)
• Density / IOD
• Clusters
• Fractal Dimension
• Uniformity
Once objects are identified, we are
dealing then with a set of pixels,
which are a set of numbers and
thus we are able to measure
anything as needed
Measurement Parameters
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Statistical Measurement of Objects
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Line Profile
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
• Horizontal section
• Vertical section
• Curved section
Thickness Measurements
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Area Percentage Measurements
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Colocalization
Intensities in Time-Series
Fluorescence Measurements
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
• DDE data to Excel for further statistical analysis
• Create reports – single sheet with image, data, charts using a custom template creating a unified and consistent report mechanism for use department wide
• Data Collector – collection of analysis data from multiple images into a single space – which can then be used to DDE, or create reports
• Poster Printing – allows for taking a single image and printing it on multiple sheets of paper for presentation/poster sessions
• Annotation – all measurements can be placed onto the image as a layer so it does not interfere with future needs for analysis
Data Output
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Data Output
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Original measured image Objects sorted by e.g. area
Sort Object in Gallery
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Volume rendering
Real Time Interaction
Clipping
Surface rendering
Volume of Interest
Three Dimensional Reconstruction
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.comJune 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
Thank You!
Nick BeaversApplications Specialist
Media Cybernetics
From Images
to Answers
June 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.comJune 2008 McGill University Image Processing Workshop presented by Media Cybernetics© www.mediacy.com
For more information or demo copies please contact:
For information on Image-Pro Plus and AutoQuant software please visit:
www.mediacy.com
From Images
to Answers