Manipulation et analyse d`images numériques

Transcription

Your images
Image processing and analysis
Monique Vasseur et Gabriel Lapointe
BCM6013 Summer 2011
1
Image processing for what?
Image processing for what?
Processing for different purposes
2
What can you do with an imaging software?
Make your image nicer
Crop only one person
Zoom on details
Erase what you don’t want
Replace a spot by an other image
Make a montage of pictures
Change its format, colors…
Can you do that for scientific images?
What are you allowed to do
with scientific images?
NOT EVERYTHING IS PERMITTED!
There is rules and ethics in imaging
Certain manipulations will destroy your data
Quantification needs more caution
Be aware that some softwares will
automatically and irreversibly modify your
image (scaling, stretching, loose metadata )
3
Ethics in scientific imaging
Images are data
Always keep the originals
Work on copies
Always compare images acquired and processed
in the same way
Do not modify partially an image
Log all image modification details, reviewers can
ask for them
Imaging software, what for?
Image processing
Image - Image
Image Segmentation
Image - objects
Image Analysis
Objects/Image - Informations
4
Imaging Introduction
Why ImageJ?
Main interface
Menus
What is a digital Image?
Pixel characteristics
Intensity in greyscale, pseudocolor and color images
Intensity and dynamic range
ImageJ image types
Image formats and conversion
Why ImageJ ?
Software in the public domain open source: IT’S FREE!
Can run on any system where you can install Java
A laptop at home is enough for 95% of the cases
If missing memory on your laptop, use one of ESI servers at UdeM
Supports multiple types of programming languages
Macro in "Java Simplified" (uses existing ImageJ functions)
Plugins in Java (add new features)
Javascript and Python
Frequently updated (every month)
Supported by an active community
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5
ImageJ
ImageJ Menus
11
©2011 Gabriel Lapointe Certains droits réservés
The main interface
Open, Save,
New image
Assembly,
drawing tools
Modification and
conversion,
geometric
operation
Filters &
Operations
Statistics,
Measures,
graphic
Access to plugins
Window
Management
Shortcuts to the
website,
information and
updates
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6
Menu : Edit
●
●
●
●
●
Maximum memory
Windows 32-bit:
the smallest number, 75% of
total or 1.4 Gb
Windows 64-bit:
75% of total
Mac OSX 32-bit:
the smallest number, 75% of
total or 1.8 Gb
Linux 32-bit: 3 Gb
Linux 64-bit: Unlimited
13
Menu : Image
14
7
Menu: Process
15
Menu : Analyze
16
8
Menu : Plugins
17
The Plugins
ImageJ can be viewed as a collection
of small programs or "plugins"
written in java
This modular structure has
the
advantage of allowing easy addition
of new features in ImageJ
18
9
Recording macro commands and operations register
Plugins > Macros > Record...
Records the actions
taken and implement
macro language
Can also be used as
a logging tool for
future references
19
Menus : Help
20
10
Exercises 0:
Launch ImageJ
Folder: Logiciels spécialisés
1) Create a new image (assembly)
2) Look at the pictures provided as examples
File / Open Samples
3) Have a look on your experimental images
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What is a digital image?
22
11
Digital image
Matrix of pixels (Picture Elements)
Digital image
Pixel: smallest subunit of an image
Pixel characteristics
x and y coordinates
Intensity value (values for color image)
The maximum intensity value is
defined by the bit depth
12
Each pixel has an xy coordinate & intensity
(x,y)
(0,0)
(3,4)
(?,?)
©2011Gabriel Lapointe Certains droits réservés
25
Each pixel has an xy coordinate & intensity
Analyse > Plot profile
Analyse > Surface plot
13
How does it work with
color images?
27
Colors
In everyday’s life
Painting colors
Our eye view
Light sources
On screen
Digital images
Does it work the same way?
14
Formation of colors
Object colors
Paint mix
Human view
Light sources
On screen
Digital images
Colors are subtractive
Colors are additive
Primary colors: magenta, cyan & yellow
Primary colors: red, green & blue
Light reflected by
object is visible
Screen: self-luminous light source
Light absorbed by
object is not visible
How intensity of a color pixel is written?
RGB color image
Standard images, fluorescence images
Pixel intensity: (R,G,B):
WHITE: (255, 255, 255)
BLACK: ( 0, 0, 0)
RED:
(255, 0, 0)
YELLOW: (255, 255, 0)
GREEN: ( 0, 255, 0)
CYAN:
( 0, 255, 255)
BLUE:
( 0, 0, 255)
15
Intensity of an HSB color image?
HSI (HSB) color image
(more suitable for histology stained images)
Pixel intensity:
(Hue, Saturation, Brightness)
WHITE: ( 0, 0, 255)
BLACK: ( 0, 0, 0)
RED:
( 0, 255, 255)
YELLOW: ( 60, 255, 255)
GREEN: (120, 255, 255)
CYAN:
(180, 255, 255)
BLUE:
(240, 255, 255)
RGB Color Image intensity matrix
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16
Color Image intensity value
Main interface bar
Analyze > Tools > Color Histogram
As we move the cursor over different parts of the
image, the color values appear in the status bar
of the program.
RGB color image intensity
Image > Plugins > Graphics > RGB profile plot
Analyse > Plot profile
RGB color
Plugins > Graphics > RGB Profiles plot
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Intensity values
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Max. intensity is defined by bit depth
Max intensity of
1bit image:
1
18
Bit depth defines the max. intensity
Max intensity of
8 bit image:
255
12 bit image: 4 095
16 bit image: 65 536
Bit depth and Dynamic range
Higher dynamic range permits to see more details; low and high signals together
Binary: Fax, Mask
2 (2¹ tones)
4 (2² tones)
8 (2³ tones)
16
32
64
128
8-bits: Camera,
Computer screen...
256 (2⁸ tones)
...
12 et 16-bits: CCD
Camera, PMT...
4 096 (2¹² tones)
...
65 536 (2¹⁶ tones)
32-bits: deconvolved
images...
...
4 294 967 296 (2³² tones)
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Image type: Binary
Only 2 possibilities: Black or white
No dynamic range
Black = 0
White = …
1 bit image:
1
8 bit image:
255
12 bit image: 4 095
Image type: Greyscale
Monochrome (one color) in levels of greys
Min. = 0
Max. intensity of …
8 bit image:
255
12 bit image: 4 095
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Image type: Color - Pseudocolor
Monochrome, each grayvalue is represented by a different color
Color is not related to specimen color, but to pixel intensity
Min. = 0
8 bit image:
255
12 bit image: 4 095
Image type: Color - Monochrome
Monochrome, colored in levels of one color
Min. = 0
8 bit image:
255
12 bit image: 4 095
21
Image type: Color - Multicolored
Mix of 3 colors
Red, Green and Blue: RGB
Bayer mask in
Standard digital
camera
NOT FOR
QUANTITATIVE
MEASURES
You are getting
only
¼ red photons
¼ blue photons
½ green photons
3 frames with
CCD camera
For each frame:
Min. = 0
Max. =…
8 bit image:
255
12 bit image: 4 095
OK FOR
QUANTITATIVE
MEASURES
But expect delay!
Image type: Color - Multicolored
Mix of 3 colors
Red, Green and Blue: RGB
Bayer mask in
Standard digital
camera
NOT FOR
QUANTITATIVE
MEASURES
You are getting
only
¼ red photons
¼ blue photons
½ green photons
3 frames with
CCD camera
! Note that… Max. intensity for each
individual frame is the same as the total
added frames in the RGB overlay image:
3 x 8 bit image: 24 bit RGB
255
4 095
65 536
OK FOR
QUANTITATIVE
MEASURES
But expect delay!
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Image type: Multi-dimensional
Image types in ImageJ
Binary: 1 bit (21)
Greyscale: 8, 12, 16 bit
Color
Pseudocoloured: 8, 16, 32 bit indexed colors
Monochrome color: 8, 16 bit color
Multicoloured: 8, 24 or 32 bit RGB
Composite: “stack of channels”: keeps originals as is
Multidimensional: hyperstacks
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Image types in ImageJ
8-bit
Binary images are considered as 8-bit images
with only 2 values from 0 (black) and 255
(white)16-bit
32-bit: addition of 4 8-bits images
8-bit Color
Grayscale with artificial coloring (Lookup Table)
RGB Color
Standard color images
RGB Stack
Images whose colors have been separated into
their red, green and blue in a stack (group of
pictures stacked on top of each other).
HSB Stack
Images whose colors have been separated into
their components Hue, Saturation and
Brightness.
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Need info about your image?
Image > Show info
Image > Properties
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Image conversion
In ImageJ
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8 or 16 bit color TO RGB color
Image > Type > RGB color
R
Original
monochrome
16 bit green
Transformed to
RBG color
Be careful each
16 bit channel will
be decreased
to 8 bits
G
B
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16 bit color images TO Hyperstacks
Image > Color > Merge Channels… (Create composite)
Hyperstack Advantages:

Composite
Preserves bit depth
Multi-color (the first 4 colors are easy,
more requires a macro)

Multidimensional (Z and T axis
supported)

preserves the16 bit
Dynamic range of
original channels
Can be spilt to original image without
data loss

Look at:
Image > hyperstack > Channels tools...
2011-06-09

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©2011 Gabriel Lapointe some rights reserved.
RGB color TO individual channels
Image > Color > Split Channels
Split in 3 grayscale images
Image > LUT
Original RGB color
26
RGB color TO a montage
Plugins > Color Function > RGB to montage
Original RGB color
If pseudocolour option is OFF,
each channel will be greyscale
RGB color to greyscale
Image > Plugins > Graphics > RGB profile plot
RGB color
To greyscale
Each color intensity
is divided by 3,
And then added together
To give the new grey value
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RGB color TO individual channels
Image > Color > Split Channels
Split in 3 grayscale images
Image > LUT
RGB color of 4 channels
If not a composite image,
you can not get back to
your 4 original images
Image formats
Commun format
Specialized
Generally limited to 8 bit, no meta-data
and unique image.
Contain all the information (lossless), with
meta-data, and can be multidimensional
Lossy
Standard: Can be opened in most software
.jpg
.tif
.gif
.ics
Lossless
.tif
.ome.tif and .ome
Proprietary: Frequent incompatibility.
.png
.stk (Metamorph)
.raw
.lei & .lif (Leica)
.gif
.lsm (Zeiss LSM)
.oif (Olympus Floview1000)
.nd2 (Nikon)...
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Conversion to smaller files or other formats
Compression hazards
57
Pre-processing:
Enhance Visualisation
Image histogram
Brightness and contrast
LUT
Single pixel operations on single image
Single pixel operations on multiple images
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Image Histogram
To enhance visualisation
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Histogram
Analyze > Histogram
Représentation de la répartition de
l'intensité lumineuse d'une image où
l'on porte en abscisse le numéro des
niveaux de gris et en ordonnée pour
chaque niveau le nombre de pixels
ayant l'intensité correspondante à ce
niveau.
Frequency distribution of the image intensity
Linear
Logaritmic
The abscisse represents the gray level
The ordinate, the number of pixels
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30
The interpretation of the histogram
Underexposed
Normal
Overexposed
Reduced
dynamic range
61
Histogram and Microscopy images
In fluorescence image
The biggest peak is often
the background!
An easy image to work with
might have a signal 3X higher
than the background
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31
Brightness and Contrast
63
Comparison of images
Why do I see red where it was supposed to negative?
What is the problem?
Tubuline control
PABP control (no tubuline marquage)
BE CAREFUL TO
Autoscale display
Adjustment of brightness and contrast
Display range: 143-3722
32
Comparison of images
Tubuline control
PABP control (no tubuline marquage)
Why do I see something strong in red.
I didn’t expect this!
Same display range for both images
To be comparable, the images issued from the
same channel (same filter cube) should be
displayed with the same range of values
Adjusting brightness and contrast
Image > Adjust > Brightness/Contrast...
Adjusts the maximum and minimum
value of the pixels and change the
brightness and contrast of an image
Works on all types of images
For RGB images, Color Balance allows
you to control each color individually
Will not change the value of the pixels
Unless you press Apply (for 8-bit and
color images only)
Set can directly enter the maximum
or minimum values
Useful to normalize multiple images
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33
Adjusting brightness and contrast
Image > Adjust > Brightness/Contrast...
67
LUT: Lookup tables
68
34
Lookup Table (LUT)
Image > Lookup Table
Notes:
Image is not converted into RGB
Pixel values are preserved
Image can be used in subsequent testing
69
Exercises 1:
Texte-1.tif
Read the text without changing the contraste
Make text white on black background
Cellules-1.tif
Identify:
Image creation date
software used
exposure time
pixel size
Make transfected cells visible (only)
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Operations on pixels
Point
or Single pixel
Local
Global
Operations
Changes the pixel values according to
different mathematical functions
Single pixel operations:
Change the value of a pixel without
considering the value of adjacent pixels
Filters :
Change the value of a pixel depending on the
value of adjacent pixels
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Single pixel operations
Addition and subtraction
•Multiplication and division
•Histrogram stretching
•Gamma
•
Mathematics and images !
Addition
Intensity
Intensity
Subtraction
Pixels
Pixels
Multiplication
Logarythmic
Intensity
Intensity
Division
Exponential
Pixels
Pixels
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37
Addition and subtraction
Process > Math > Add...
-125
Original
+125
Addition and subtraction = change the image brightness
75
Multiplication and division
Process > Math > Multiply...
X 0.5
Original
X2
Multiplication and division = change the image contrast
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38
Mathematics on images
Multiplication
Addition
Intensity
Intensity
Division
Subtraction
Pixels
Pixels
Fluorescence light emission
is an additive process
Transmitted light absorption
is a multiplicative process
So background correction
operations seems better when
using a substraction for
fluorescence image and
division for transmitted light
images.
Automatic contrast enhancement
Process > Enhance Contrast
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Automatic contrast enhancement
Process > Enhance Contrast
79
Gamma (γ) : logarithmic or exponential factor
Process > Math > Gamma
Bring non-linear change to the pixel intensity
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40
Color image
gamma 1.0
Gamma 2.2 on the same image
41
The importance of gamma (γ)
83
The influence of gamma (γ)
Original
Brightness & Contrast
Gamma 0.5
Gamma 2
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Gamma correction (γ)
Process > Math > Gamma...
Plugins > Filters > Gamma scroll
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Image enhancement
43
Single pixel operations
To help visualisation of multiple images
Overlay of images
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Image Overlay
Process > Image Calculator...
16-bit Images
with LUT
Convert to RGB
You can also use plugins:
Addition
Color Merge
●
2 images at a time
●Choice of several colors
●images 16-bit supported
●
RGB Gray Merge
●
Up to 4 images at a time
Weak color choice
●Transform 16 bit to 8-bit (24 bit result)
●
●
Image > Color > Channel Merging...
●
Up to 4 images at a time
Weak color choice
●8, 16 et 32 bit
●Can provide an Hyperstack (depth conserved)
●
●
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44
Image Overlay
Subtraction
Convert to RGB
16-bit images
with LUT
Addition
89
Image Overlay
Additions
Additions + Subtractions
90
45
Mask subtraction for data mining
Inverted Mask
Subtraction
Is there a signal of tubulin in the nuclei?
91
Background correction
=
+
Edit / Invert
Background (illumination problem) – to eliminate
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46
Exercises 2:
Restore-1.tif
Improve image
Cellules-3_*.tif
Improve images (Contrast, γ, etc.)
Make a color assembly
93
Pre-processing:
Enhance Visualisation
Local operations on pixels:
Median filter
Mean filter
Modal filter
FFT Bandpass filter
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47
Filters in imagery
©2011 Gabriel Lapointe Certains droits réservés.
95
The concept of Kernel
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48
Kernel size and shape
r=0.5
r=1.0
r=1.5
r=2.0
r=2.5
r=3.0
r=3.5
r=4.0
r=4.5
r=5.0
r=5.5
r=6.0
97
The noise
Random variation in the
intensity of the pixels. Do not
confused with the background,
the black level or a nonspecific
signal.
Salt & pepper : Black pixel in a
clear area and white in a dark
area. Often caused by a dead
pixel in the camera.
Gaussien : Variation with a
Gaussian distribution,
independent of the intensity of the
pixels. Generally increases with
the voltage applied to the
detector.
2010-11-12
98
49
Noise correction
Process > Filters > ...
Median
Gaussian
Mean
99
The impact of the size of the kernel
Median
Mean
r=0.5
r=1.0
r=2.0
r=6.0
100
50
Edge Enhancement
Sharpen
Variance
Unsharpen
101
Edge Enhancement : a bad idea
Sharpen
Variance
Unsharpen
102
51
The space of sequences
Fourrier transform
(Fast Fourrier Transform)
103
Bandpass Filter
Process > FFT > Bandpass Filter...
104
52
Periodicity and frequency
105
Images rebuild with FFT
Process > FFT > ...
FFT
Inverse
FFT
106
53
Exercises 3:
Restore-3.tif
Remove image noise using the filters
Try several and see the differences, which seems
most appropriate?
Cellules-5_*.tif (series of images)
Enhancing images using filters…
Attempt to obtain greater precision of details
107
Afternoon workshop
Work on your images
Follow the procedures which will be
given to improve your images
Questions, problems ... Ask us, we're
here for you!!
54
Ressources
Official website :
http://rsbweb.nih.gov/ij/index.html
Macbiophotonics :
http://www.macbiophotonics.ca/imagej/
Wiki :
http://imagejdocu.tudor.lu/doku.php
Diffusion list :
https://list.nih.gov/archives/imagej.html
Burger et Burge : Digital Image Processing, An Algorithmic
Introduction using Java; Springer Verlag, 2008
http://www.imagingbook.com/
109
Plugins : Installing and Compiling
(1)
The plugins are easily installed simply by placing
them in the plugins folders
To find the plugins folder:
Plugins > Utilities > ImageJ Properties...
Find the line that begins : Menus.getPlugInsPath
The plugins are found in 3 formats
*.java : Source code, can change the plugins. Need to be compiled to use them (Plugins>
Compile and run ...)
*.class : File already compiled that can be used directly by ImageJ
*.jar : Plugins for more complex, requiring multiple class files are grouped in an archive file.
Can be used directly by ImageJ
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Plugins : Installing and Compiling
(2)
All *. class, *. jar and *. txt in the plugins
folder, and contain a "_"in their name, will be
recognized by ImageJ and accessible in the
menu Plugins.
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Manipulation et analyse d`images numériques

Transcription

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