Image analysis workshop ICASS 2015 - Université Laval · 2015-05-21 · Part 2 – Human imaging: the eye Similar concepts in human and scientific vision: -Light intensity -Light

Image analysis workshop

ICASS 2015

Halifax

1

Jesse Greener

Microfluidics and Spectroscopy of Materials

Laval University, Department of Chemistry

Pavillon Alexandre-Vachon

1045, avenue de la médecine, local 3607

https://jgreener.chm.ulaval.ca/

[email protected]

Image analysis

workshop plan

1- Human vision background

2- Digital images

3- Introduction to digital image processing

4- Image processing vs. data manipulation-examples

5- Introduction to imaging processing with ImageJ

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Section 1 - Human vision

Section 1 Plan

1- The human eye and visual representation of the real world

2- Introduction to digital images

3- Introduction to image processing

4- Digital image manipulation-examples

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Part 2 – Human imaging: the eye

The human eye:

We all have them.

Data is acquired and represented in very similar way to scientific instrumentation:

consisting of optical elements and photo detectors.

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Detection elements: cones and rods:

Cones : colour discrimination high resolution imaging.

Rods: colourless, high sensitivity for low-light levels, primary detector for

peripheral vison.

Photo transduction describes mechanism for capture of a photon and conversion

to a neural signal. It is a complicated biological process.

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Detectible colour range : “visible spectrum”

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Primary colours : inspired by 3 types of cone detectors in the eye

ɣ,β,ρ cones differences in

-frequency

-sensitivity

Colour blindness test :

http://www.xrite.com/online-color-

test-challenge

Colour sensitivity spectrum

after sensitivity adjustment

of ɣ,β,ρ cones

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Rods for colourless vision, in low-light environments:

All frequencies are collected, but…

they do not register in the brain as colour.

Maximum light sensitivity at 550nm.

Corresponds to what? 8


Spatial resolution (visual acuity): measured ability to discriminate b/w two

closely spaced objects. Measured by:

Eye charts: Contrast sensitivity:

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Similar concepts in human and scientific vision:

-Light intensity

-Light frequency

-Position, changing position (motion)

-Photodetectors (colour/b&w)

-Frequency reconstruction based on RGB detector elements

-Limits of detection (intensity, target duration, spectral window, spatial and spectral

resolution)

Sensitive to: -Colour (100 different colours ). -Intensity (16-32 shades of grey), -Minimum number of photons to be registered by humans is 5-14. -Differences in position (1-3 cm from 20 m)

-Motion via simultaneous imaging with peripheral vision (low resolution due to low clustering of cones and rods away from central focal point)

-Flickering light (100 ms rods, 10-15ms cones)

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http://www.xrite.com/online-color-test-challenge

Part 3 – Introduction to Digital images

What is an image? : A visual representation of a real-world target object.

Sensing based on :

• Optical imaging microscopy, spectral imaging photons.

• Electron microscopy electrons, tunneling barrier between sample and tip.

• Atomic force microscopy force-distance curves, tip deflection, etc, from

interaction between AFM tip and sample.

CHM 7001 optical imaging microscopy/spectral imaging.

Therefore, we talk about photons and optical elements.

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Why use micro imaging?

To acquire optical images at the microscale that reveal information that cannot

be seen by the eye:

• Discriminate between very close objects

(improve spatial resolution and magnification)

• Discriminate between subtle differences in photon intensity

• Extend the sensitivity outside of visible spectral bandwidth

• Analyse very small that chages/movemement in samples in time

• Precisely coordinate measurements with other events

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How are images represented by a computer?

An image

is a visual representation of… …an array of numbers

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Pixel resolution:

Screen resolution : Pixels Per Inch (PPI)

Print resolution : Dots Per Inch (DPI)

Dots/Pixels per square inch : Monitor resolution = 68-110 PPI Eg: 72 PPI = 72 spatial positions (pixels) in 1” 72 PPI image has 722 (=5184) pixels in a 1” x 1” square

Dots/Pixels per square inch : 1080x1920 ≈ 2M Screen dimensions = 11 in x26 in = 286 in2

Resolution = 7250 PPI2

= 85 PPI

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Pixel resolution:

Note: Camera resolution often quoted in Megapixels

Estimated DPI 35mm film: 20 megapixels

MegaPixels : Total number of camera sensing pixels Example : 5 Megapixel camera 2560x1920=4 915 200 ≈ 5 Mpixel

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Pixel resolution:

Note: Camera resolution often quoted in Megapixels

Pixels distributed over the sensing area

MegaPixels : Total number of camera sensing pixels Example : 5 Megapixel camera 2560x1920=4 915 200 ≈ 5 Mpixel

Which requires a certain pixel size

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Display resolution: Due to fixed monitor display resolution, higher megapixel

resolution means larger display size.

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Note: This is why when you change your monitor resolution, the size of all

display items change.


Bit-depth resolution in greyscale:

8-bit: intensity ranging from black to white is binned into 28 = 256 different values

(approximately 4 times more intensity sensitivity than the eye).

16-bit: represents 216 = 65 536 different values.

Nu

mb

er

of

pix

els

Pixel intensity 18

A histogram


RGB colour images:

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RGB histogram


RGB colour images:

Bit-depth resolution in colour:

15-bit rgb colour (5-bit red, 5-bit green, 5-bit blue)=25x25x25=215=32 768

16 bit rgb (5-bit red, 6-bit green, 5-bit blue)=25x26x25=215=65 536

Rgb24 (“True colour”): 28 bits red, 28 bits green, 28 bits blue

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R G B

*0* 0 0

1 1 1

2 *2* 2

3 3 *3*

4 4 4

5 5 5

6 6 6

7 7 7

+ =

0,2,3

RGB limited intensity values:

Example: RGB9: 23 bits Red, 23 bits Green, 23 bits Blue

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R G B

*0* 0 0

1 1 1

2 2 2

3 3 3

4 *4* 4

5 5 5

6 6 *6*

7 7 7

+

0,4,6 0,2,3

+ =

Therefore, RGB9 can only represent 2 intensities for pixel : 40% G 60% B

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RGB limited intensity values:

Example: RGB9: 23 bits Red, 23 bits Green, 23 bits Blue


RGBA Bit-depth resolution in colour 4 components:

“RGB32”: RGBA32: 28 bits Red, 28 bits Green, 28 bits Blue, 28 bits Alpha (for intensity)

“RGB64”: RGBA64: 216 bits Red, 216 bits Green, 216 bits Blue, 216 bits Alpha (for intensity)

RGB rumber

Alpha

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R G B

*0* 0 0

1 1 1

2 *2* 2

3 3 *3*

4 4 4

5 5 5

6 6 6

7 7 7

+

RGBA Bit-depth resolution in colour - 4 components:

Example: RGBA12: 23 bits Red, 23 bits Green, 23 bits Blue, 23 bits Alpha

A RGBA

= =

0,2,3 RGB

1,2,4,A

+

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Image dimensionality:

A 1D image can be produced using the plot profile function


2D

1D

Exercise:

Using ImageJ:

Open image (File/Open Samples/Dot Blot)

Draw a line with line tool

Generate plot profile (Analyse/plot profile)

Image dimensionality:

2D

3D

4D

5D


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Why use Image Processing?

1. To improve the appearance of the image.

2. To bring out obscure details in an image.

3. To carry out quantitative measurements.

Global contrast/brightness change

Local changes based

on original intensity

Local changes

based on position

Part 4 – Introduction to image processing

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Image processing :

Ranges from simple global changes to contrast, to complex multistep data

analysis using:

- macros for automation

- other programs (matlab) for data preprocessing

- batch processing of many files at once

- Etc.

Part 4 – Introduction to image processing

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Image processing :

Ranges from simple global changes to contrast, to complex multistep data

analysis using:

- macros for automation

- other programs (matlab) for data preprocessing

- batch processing of many files at once

- Etc.

When processing goes too far:

Image manipulation is data manipulation

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Part 5 – Image manipulation =

data manipulation

θ

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data manipulation

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data manipulation

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data manipulation

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data manipulation

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data manipulation

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data manipulation

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data manipulation

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Rossner M , and Yamada K M J Cell Biol 2004;166:11-15

Merging fields of view:

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Selective enhancement (immunogold nanoparticles)


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Selective enhancement

Manipulation of blots: brightness and contrast adjustments.


Global (over)

enhancement by contrast

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Histogram-equalized stretch

a. Contrast enhancement (linear uniform stretch) :

b. Contrast enhancement (non-linear / non-uniform) :

Image “manipulation”:

• Non-linear modifications that selectively enhance/change parts of the image

• Merging objects from multiple fields of view

Linear vs. non-linear image adjustments

Problem avoided if: • All image processing steps are explained • Original image is given (ie in supporting

information) • We do not rely on images as data, but

quantitative image analysis of images

IF presented as the original image

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Image Analysis 1

1- Introduction to image analysis software “ImageJ”

-development history

-ImageJ vs. Fiji

-installation

-ImageJ website

-sample images

-tour of software environment

2- Basic image analysis using ImageJ:

-opening/saving a file

-file size vs. bit-depth and image-resolution

-calibration

-histograms, “live updates”

-contrast, brightness, saturation

-measurement: linear profiles, statistical information

-Region of Interest (ROI) and local measurements/manipulations

-image calculations

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http://rsb.info.nih.gov/ij Adapted from : Joel B. Sheffield 43

ImageJ

• An adaptation of NIH image for the Java platform.

• Can run on any computer systems that can run Java (Sun Microsystems)

• Open source

• Two powerful scripting languages

– Java Plugins

– Macro Language

• Continual Upgrades

• Active community of several thousand users

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Resources

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1. Ferreia, T. et Rasband, W. Ieds) « ImageJ User Guide : IJ1.46r », National Institues of Health,

2012. (lien)

2. Baecker, V. (ed) « Workshop: Image Processing and Analysis with ImageJ », Montpellier RIO

Imaging, 2013. (lien)

3. Miure, K (ed) « Basics of image processing and analysis », Centre for Molecular and Cellular

Imaging EMBL Heidelberg, 2014. (lien)

4. ImageJ Website: http://imagej.nih.gov/ij/

5. Fiji Website: http://fiji.sc/Fiji

http://imagej.nih.gov/ij/docs/guide/user-guide.pdf

http://dev.mri.cnrs.fr/attachments/download/1057/Image_processing_and_analysis_with_ImageJ.pdf



http://jgreener.chm.ulaval.ca/fileadmin/jgreener/documents/CHM_7001/Resources/CMCIBasicCourse201102Bib-1.pdf

http://jgreener.chm.ulaval.ca/fileadmin/jgreener/documents/CHM_7001/Resources/CMCIBasicCourse201102Bib-1.pdf

http://imagej.nih.gov/ij/







http://fiji.sc/Fiji

http://fiji.sc/Fiji

http://fiji.sc/Fiji

http://fiji.sc/Fiji

http://fiji.sc/Fiji

1.ImageJ Website: http://imagej.nih.gov/ij/

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1. Features

2. Documentation ImageJ User Guide

3. Documentation Keyboard Shortcuts

4. Mailing list

5. Downloads example images (get these)








Menus

Introduction to the Main Menu

Of these, we’ll concentrate on:

– Image

– Process

– Analyze

– Plugins

– Help

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49

Principale interface

Open, save, create image

Design tools

Modificatino and conversion, geometric operations

Filters and math operations.

Statistics, measurements,

graphs

Plugins and macros

Window management

Links to the website, updates

Help Menu

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Opening and saving files

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Drag and Drop files onto IJ control strip, or

Open samples

Exercise: open sample image “Dot Blot”

Image Menu

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Demo:

1) Dot blots


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Dot_Blot.tif (open sample images)

File size = 141kB

Adjust size reduce save

Zoom comparison.

File size comparison

Save as jpg

File size = 7kB

Save back to tiff

File size = 141 kB


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File size = 141kB


Zoom comparison.


Exercise:

Save as jpg

File size = 7kB

Save back to tiff

File size = 141 kB

Zooming

Pixels, position indicator, intensity value


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File size = 141kB


Zoom comparison.


Exercise:

Save as jpg

File size = 7kB

Save back to tiff

File size = 141 kB

Zooming

Pixels, position indicator, intensity value


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M51c.tif (open sample images)

Demo:

1. Save as jpg

File size = 4kB

Do an image subtraction of new file

from original file.

2. Save back to tiff

File size = 160 kB

Do an image subtraction of new file

from original file.

Opening and saving files: Best practice

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ALWAYS KEEP A COPY OF

THE ORIGINAL .TIF FILE.

Image Menu

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Look Up Tables (LUTs)

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File size = 141kB

Image Adjust Type

Apply LUT

Histogram comparison

“Ice”

“fire”

Analyze Menu

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The Image Histogram

Log Scale

The histogram shows the number of pixels of

each value, regardless of location. The log

display allows for the visualization of minor

components. Note that there are unused pixel

values

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In this case, the log display indicates that virtually all pixel values are used, even

though they are a small percentage of the total.

Log Scale

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Image Menu

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Demo:

1) M51.tif

Adjust brightness contrast

Saturation?

No! Pixel values do not change.

Process Menu

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Process Menu

65

Vs.

Math: +6000

Process Menu

66

Vs.

Image Calc.

+

Analyze Menu

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Demo:

1) Dot blots

-Measure

-Set measurements

-Changing measurement types and loss of information

2) Embryos

-RGB

Analyze Menu

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Demo:

1) Results

-Set measurements

-Make measurements

-Use math function

-Summarize

2) Edit

-Copy data

3) Plot in Excel Exercise:

1) Open M51 Galaxy sample image

-Set measurements

-Make measurements

-Use multiply command in Process/math menu (x2)

-Make measurement

-repeat 5 times

3) Plot in Excel

Analyze Menu

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Subtract (-25)

y = 165,68e0,6931x

0

500

1000

1500

2000

2500

3000

0 1 2 3 4 5

y = -25x + 2650,6

2540

2550

2560

2570

2580

2590

2600

2610

2620

2630

0 1 2 3 4 5

Multiply (x2)

Brightness Adjustment

The brightness adjustment essentially adds or subtracts a constant to every pixel,

causing a shift in the histogram along the x axis, but no change in the distribution 70

Contrast Enhancement

For contrast enhancement, a lower value, in this case, 88, is set at zero, and a higher value, 166, is set at 255. The values of each of the pixels are adjusted proportionately. Note that because of the integer values, not all of the pixel values are used. 71

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Addition and subtraction Process > Math > Add...

Original -125 +125

Addition and subtraction = modification in the image brightness

2500

2550

2600

2650

0 5

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Multiplication et division Process > Math > Multiply...

Original

X 0.5 X 2

Multiplication and division = modifies the image contrast

0

1000

2000

3000

0 5

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Automatic optimization of contrast

Process > Enhance Contrast

Thresholding

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Analyze particles

Plugins Menu

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Automation with ImageJ: Macros

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Image stacks

Demo: Open Stack1-5 images “Set properties” Z-stacks, time-stacks, etc. “Set scale”

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Image stacks

Stack to images Images to stack

Batch commands

• Simple

• Prewritten code for certain

functions

• Operates on image file from

certain directories

• Extendable with marcos

Why write a macro?

•Consistency – same procedure for all

images

•Speed – for large numbers of images or

repetitive tasks

•Documentation – for publication of novel

methods

•Sharing – within or between groups

Example macro

General approach: • Record

• Modify

• Execute

Demo: • Open Stack1-5 images

• Record macro

• Define measurement area

• “measure”

• Repeat

Image analysis workshop ICASS 2015 - Université Laval · 2015-05-21 · Part 2 – Human imaging: the eye Similar concepts in human and scientific vision: -Light intensity -Light

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