Manipulation et analyse d'images numériquesesilrch1.esi.umontreal.ca/~syguschj/cours/BCM6013/2011/imagerie... · ImageJ ImageJ Menus ©2011 Gabriel Lapointe Certains droits réservés

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Your images

Image processing and analysis

Monique Vasseur et Gabriel Lapointe

BCM6013 Summer 2011

http://rsb.info.nih.gov/ij/

http://creativecommons.org/licenses/by-nc-sa/2.5/ca/deed.fr

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Image processing for what?

Image processing for what?

Processing for different purposes

http://upload.wikimedia.org/wikipedia/commons/4/40/Viewer_medecine_nucleaire_keosys.JPG

http://upload.wikimedia.org/wikipedia/commons/4/40/Viewer_medecine_nucleaire_keosys.JPG

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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 )

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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

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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

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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

http://rsbweb.nih.gov/ij/developer/macro/macros.html

http://rsbweb.nih.gov/ij/plugins/index.html

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ImageJ

ImageJ Menus

©2011 Gabriel Lapointe Certains droits réservés 11


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

http://gabriellapointe.ca/






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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


Menu : Image







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Menu: Process


Menu : Analyze







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Menu : Plugins


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







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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


Menus : Help







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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?




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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

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©2011Gabriel Lapointe Certains droits réservés 25

Each pixel has an xy coordinate & intensity

(?,?) (3,4)

(0,0) (x,y)

Each pixel has an xy coordinate & intensity

Analyse > Plot profile

Analyse > Surface plot





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How does it work with

color images?

Colors

In everyday’s life

Painting colors

Our eye view

Light sources

On screen

Digital images

Does it work the same way?

http://upload.wikimedia.org/wikipedia/commons/6/6a/Mona_Lisa.jpg

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Formation of colors

Object colors

Paint mix

Human view

Light sources

On screen

Digital images

Light absorbed by

object is not visible

Light reflected by

object is visible

Screen: self-luminous light source

Colors are subtractive Primary colors: magenta, cyan & yellow

Colors are additive Primary colors: red, green & blue

How intensity of a color pixel is written?

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)

RGB color image Standard images, fluorescence images

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Intensity of an HSB color image?

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)

HSI (HSB) color image (more suitable for histology stained images)


RGB Color Image intensity matrix




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As we move the cursor over different parts of the image, the color values appear in the status bar of the program.

Color Image intensity value Main interface bar Analyze > Tools > Color Histogram

RGB color image intensity Image > Plugins > Graphics > RGB profile plot

RGB color

Analyse > Plot profile

Plugins > Graphics > RGB Profiles plot

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Intensity values

Max. intensity is defined by bit depth

Max intensity of

1bit image: 1

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Max intensity of

8 bit image: 255

12 bit image: 4 095

16 bit image: 65 536

Bit depth defines the max. intensity


Bit depth and Dynamic range

2 (2¹ tones)

256 (2⁸ tones)

65 536 (2¹⁶ tones)

4 096 (2¹² tones)

4 294 967 296 (2³² tones)

...

...

...

Binary: Fax, Mask

8-bits: Camera, Computer screen...

12 et 16-bits: CCD Camera, PMT...

32-bits: deconvolved images...

4 (2² tones)

8 (2³ tones)

16

32

64

128

Higher dynamic range permits to see more details; low and high signals together





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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


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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

OK FOR

QUANTITATIVE

MEASURES But expect delay!

For each frame:

Min. = 0

Max. =…

8 bit image: 255

12 bit image: 4 095


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

OK FOR

QUANTITATIVE

MEASURES But expect delay!

! 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

3 x 12 bit image: 36 bit RGB 4 095

3 x 16 bit image: 48 bit RGB 65 536

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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.


Need info about your image? Image > Show info Image > Properties







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Image conversion

In ImageJ

8 or 16 bit color TO RGB color Image > Type > RGB color

Original

monochrome

16 bit green

Transformed to

RBG color

Be careful each

16 bit channel will

be decreased

to 8 bits

R

G

B

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2011-06-09 ©2011 Gabriel Lapointe some rights reserved. 51

16 bit color images TO Hyperstacks Image > Color > Merge Channels… (Create composite)

Hyperstack Advantages:

Preserves bit depth

Multi-color (the first 4 colors are easy,

more requires a macro)

Multidimensional (Z and T axis

supported)

Can be spilt to original image without

data loss

Look at:

Image > hyperstack > Channels tools...

Composite

preserves the16 bit

Dynamic range of

original channels

RGB color TO individual channels Image > Color > Split Channels

Original RGB color

Split in 3 grayscale images Image > LUT



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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

RGB color of 4 channels Split in 3 grayscale images Image > LUT

If not a composite image,

you can not get back to

your 4 original images

©2011 Gabriel Lapointe Certains droits réservés

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Image formats

Commun format

Generally limited to 8 bit, no meta-data

and unique image.

Lossy

.jpg

.gif

Lossless

.tif

.png

.raw

.gif

Specialized

Contain all the information (lossless), with

meta-data, and can be multidimensional

Standard: Can be opened in most software

.tif

.ics

.ome.tif and .ome

Proprietary: Frequent incompatibility.

.stk (Metamorph)

.lei & .lif (Leica)

.lsm (Zeiss LSM)

.oif (Olympus Floview1000)

.nd2 (Nikon)...




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Conversion to smaller files or other formats Compression hazards

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.

Linear

Logaritmic Frequency distribution of the image intensity

The abscisse represents the gray level

The ordinate, the number of pixels




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The interpretation of the histogram

Normal Underexposed Overexposed

Reduced dynamic range


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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Brightness and Contrast

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Comparison of images

Tubuline control PABP control (no tubuline marquage)

BE CAREFUL TO

Autoscale display

Adjustment of brightness and contrast

Display range: 140-240 Display range: 143-3722

Why do I see red where it was supposed to negative?

What is the problem?

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Comparison of images

Tubuline control

Why do I see something strong in red.

I didn’t expect this!

To be comparable, the images issued from the

same channel (same filter cube) should be

displayed with the same range of values

Same display range for both images

Display range: 143-3722

PABP control (no tubuline marquage)

Display range: 143-3722


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

Adjusting brightness and contrast Image > Adjust > Brightness/Contrast...




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Adjusting brightness and contrast Image > Adjust > Brightness/Contrast...

LUT: Lookup tables

68





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Lookup Table (LUT) Image > Lookup Table

Image is not converted into RGB Pixel values are preserved Image can be used in subsequent testing

Notes:


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

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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 To enhance visualisation

•Addition and subtraction

•Multiplication and division

•Histrogram stretching

•Gamma


Intensity

Pixels

Intensity

Pixels

Addition

Intensity

Pixels

Multiplication

Intensity

Pixels

Logarythmic

Mathematics and images !

Subtraction

Division Exponential




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Addition and subtraction Process > Math > Add...

Original -125 +125

Addition and subtraction = change the image brightness


Multiplication and division Process > Math > Multiply...

Original X 0.5 X 2

Multiplication and division = change the image contrast







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Intensity

Pixels

Addition

Intensity

Pixels

Multiplication

Mathematics on images

Subtraction Division

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


Gamma (γ) : logarithmic or exponential factor Process > Math > Gamma

Bring non-linear change to the pixel intensity








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Color image gamma 1.0

Gamma 2.2 on the same image

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The importance of gamma (γ)


The influence of gamma (γ) Brightness & Contrast Original Gamma 0.5 Gamma 2







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Gamma correction (γ)

Process > Math > Gamma... Plugins > Filters > Gamma scroll

Image enhancement




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Single pixel operations

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To help visualisation of multiple images

Overlay of images

©2011 Gabriel Lapointe Certains droits réservés

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Image Overlay Process > Image Calculator...

Convert to RGB

16-bit Images

with LUT

Addition You can also use plugins: ●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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Subtraction

16-bit images

with LUT Addition

Con

vert to

RG

B



Additions + Subtractions


Additions







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Inverted Mask

Mask subtraction for data mining Process > Image Calculator...

Subtraction

Is there a signal of tubulin in the nuclei?


Background correction

Edit / Invert

+ =

Background (illumination problem) – to eliminate







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Exercises 2:

Restore-1.tif

Improve image

Cellules-3_*.tif

Improve images (Contrast, γ, etc.)

Make a color assembly

Pre-processing: Enhance Visualisation

Local operations on pixels:

Median filter

Mean filter

Modal filter

FFT Bandpass filter

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©2011 Gabriel Lapointe Certains droits réservés. 95

Filters in imagery


The concept of Kernel





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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

2010-11-12 ©2010 Gabriel Lapointe Certains droits réservés. 98

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.





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Noise correction Process > Filters > ...

Median

Mean

Gaussian


The impact of the size of the kernel

Median

r=0.5 r=1.0 r=2.0 r=6.0

Mean





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Edge Enhancement Process > Filters > ...

Sharpen

Unsharpen

Variance


Edge Enhancement : a bad idea Process > Filters > ...

Sharpen

Unsharpen

Variance





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The space of sequences Fourrier transform (Fast Fourrier Transform)


Bandpass Filter Process > FFT > Bandpass Filter...





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Periodicity and frequency


Images rebuild with FFT Process > FFT > ...

FFT

Inverse FFT





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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

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!!



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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/


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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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.

Plugins : Installing and Compiling (2)




Documents

Manipulation et analyse d'images numériquesesilrch1.esi.umontreal.ca/~syguschj/cours/BCM6013/2011/imagerie... · ImageJ ImageJ Menus ©2011 Gabriel Lapointe Certains droits réservés