Chris Campbell

LondonR - 13th July 2010

Image Analysis Using R

Steps to image analysis • Image capture

• Clean image/reduce noise

• Extract information

• Analyze information

Image Capture

Light

Photography

Light microscopy

Fluorescence microscopy

gels

cells

tissue samples

http:// ... western blot

http:// ... cells

Image Capture

Light

Photography

Light microscopy

Fluorescence microscopy

gels

cells

tissue samples

X-ray Radiography

Computed tomography (CT)

bones

tumours

http:// ... x-ray

http:// ... cat scan

Image Capture

Light

Photography

Light microscopy

Fluorescence microscopy

gels

cells

tissue samples

X-ray Radiography

Computed tomography

bones

tumours

Magnetism Magnetic resonance imaging (MRI) patients

http:// ... MRI

Image Capture

Light

Photography

Light microscopy

Fluorescence microscopy

gels

cells

tissue samples

X-ray Radiography

Computed tomography

bones

tumours

Magnetism Magnetic resonance imaging patients

Electrons Scanning electron microscopy

Transmission electron microscopy

insects

viruses

http:// ... SEM insect

http:// ... TEM virus

Image Capture

Light

Photography

Light microscopy

Fluorescence microscopy

gels

cells

tissue samples

X-ray Radiography

Computed tomography

bones

tumours

Magnetism Magnetic resonance imaging patients

Electrons Scanning electron microscopy

Transmission electron microscopy

insects

viruses

Positrons Positron emission tomography

(PET)

tumours

http:// ... positron emission tomography

Image Capture

Light

Photography

Light microscopy

Fluorescence microscopy

gels

cells

tissue samples

X-ray Radiography

Computed tomography

bones

tumours

Magnetism Magnetic resonance imaging patients

Electrons Scanning electron microscopy

Transmission electron microscopy

insects

viruses

Positrons Positron emission tomography

(PET)

tumours

Intermolecular

forces

Atomic force microscopy inorganic surfaces http://pico.iis.u-tokyo.ac.jp/media/16/20060621-QuenchedSi-AFM.jpg

Generally… • Use large numbers of images

• Use all images

• Use whole image, not crop

• Random selection not "typical region"

• i.e. avoid subjectivity

Image Processing Libraries in CRAN biOps Image processing and analysis

dcemri A Package for Medical Image Analysis

dpmixsim Dirichlet Process Mixture model simulation for clustering & image segmentation

edci Edge Detection and Clustering in Images

epsi Edge Preserving Smoothing for Images

FITSio FITS (Flexible Image Transport System) utilities

PET Simulation and Reconstruction of PET Images

R4dfp 4dfp MRI Image Read & Write Routines

rimage Image Processing Module for R

RImageJ R bindings for ImageJ

ripa R Image Processing & Analysis

tractor.base A package for reading, manipulating & visualising magnetic resonance images

adimpro Adaptive Smoothing of Digital Images

Libraries in CRAN biOps Image processing and analysis

dcemri A Package for Medical Image Analysis

dpmixsim Dirichlet Process Mixture model simulation for clustering & image segmentation

edci Edge Detection and Clustering in Images

epsi Edge Preserving Smoothing for Images

FITSio FITS (Flexible Image Transport System) utilities

PET Simulation and Reconstruction of PET Images

R4dfp 4dfp MRI Image Read & Write Routines

rimage Image Processing Module for R

RImageJ R bindings for ImageJ

ripa R Image Processing & Analysis

tractor.base A package for reading, manipulating & visualising magnetic resonance images

adimpro Adaptive Smoothing of Digital Images

• Open source

• Java

• Image analysis software http://rsbweb.nih.gov/ij/

package:RImageJ • Authors: Romain Francois & Philippe Grosjean

• Bindings between R and ImageJ

Subjectivity vs. Objectivity • Hypothesis: blue blobs are always larger than yellow blobs

Subjectivity • Hypothesis: blue blobs are always larger than yellow blobs

Manual

measurements

Subjectivity • Hypothesis: blue blobs are always larger than yellow blobs

It’s easy to accept

manual

measurements

when they make

sense, but it’s

tempting to

repeat them if

they seem wrong

Subjectivity • Hypothesis: blue blobs are always larger than yellow blobs

Subjective

observer accepts

expected

hypothesis

Objectivity • Hypothesis: blue blobs are always larger than yellow blobs

Automatically

threshold

Objectivity • Hypothesis: blue blobs are always larger than yellow blobs

Objective observer

automates analysis

and rejects

hypothesis

Automate Procedures • Identify objects without making subjective decisions

Run ImageJ from R • Open connection to

an image

• Use IJ$run() to

access macros

• Great potential for

automating image

processing from R

Run ImageJ from R • However, some key macros not yet implemented

(e.g. setAutoThreshold, imageCalculator)

package:rimage • Author: Nikon

• Reads jpegs into

RGB arrays

• Plot function defined

for objects of class

"imagematrix"

Analyze information • Plots and statistical summaries of particles from image

Single image

Multiple images

Conclusions • Images available?

• Ensure quality/validate method

• Choose useful measures

• Use analysis to make predictions

Acknowledgements • Mango Solutions www.mango-solutions.com

• L. R. Contreras-Rojas, R. H. Guy

http://www.bath.ac.uk/pharmacy/staff/rhg.html

• NAPOLEON http://www.ehu.es/napoleon/