Image Analysis Using R

Transcription

1 Image Analysis Using R Chris Campbell LondonR - 13th July 2010

2 Steps to image analysis Image capture Clean image/reduce noise Extract information Analyze information

3 Image Capture Light Photography Light microscopy Fluorescence microscopy gels cells tissue samples western blot cells

4 Image Capture Light X-ray Photography Light microscopy Fluorescence microscopy Radiography Computed tomography (CT) gels cells tissue samples bones tumours x-ray cat scan

5 Image Capture Light X-ray Photography Light microscopy Fluorescence microscopy Radiography Computed tomography gels cells tissue samples bones tumours Magnetism Magnetic resonance imaging (MRI) patients MRI

6 Image Capture Light X-ray Photography Light microscopy Fluorescence microscopy Radiography Computed tomography gels cells tissue samples bones tumours Magnetism Magnetic resonance imaging patients Electrons Scanning electron microscopy Transmission electron microscopy insects viruses SEM insect TEM virus

7 Image Capture Light X-ray Photography Light microscopy Fluorescence microscopy Radiography Computed tomography gels cells tissue samples bones tumours Magnetism Magnetic resonance imaging patients Electrons Positrons Scanning electron microscopy Transmission electron microscopy Positron emission tomography (PET) insects viruses tumours positron emission tomography

8 Image Capture Light X-ray Photography Light microscopy Fluorescence microscopy Radiography Computed tomography gels cells tissue samples bones tumours Magnetism Magnetic resonance imaging patients Electrons Positrons Scanning electron microscopy Transmission electron microscopy Positron emission tomography (PET) insects viruses tumours Intermolecular forces Atomic force microscopy inorganic surfaces

9 Generally Use large numbers of images Use all images Use whole image, not crop Random selection not "typical region" i.e. avoid subjectivity

10 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 ripa tractor.base adimpro R bindings for ImageJ R Image Processing & Analysis A package for reading, manipulating & visualising magnetic resonance images Adaptive Smoothing of Digital Images

11 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 ripa tractor.base adimpro R bindings for ImageJ R Image Processing & Analysis A package for reading, manipulating & visualising magnetic resonance images Adaptive Smoothing of Digital Images

12 package:rimagej Authors: Romain Francois & Philippe Grosjean Bindings between R and ImageJ Open source Java Image analysis software

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

14 Subjectivity Hypothesis: blue blobs are always larger than yellow blobs Manual measurements

15 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

16 Subjectivity Hypothesis: blue blobs are always larger than yellow blobs Subjective observer accepts expected hypothesis

17 Objectivity Hypothesis: blue blobs are always larger than yellow blobs Automatically threshold

18 Objectivity Hypothesis: blue blobs are always larger than yellow blobs Objective observer automates analysis and rejects hypothesis

19 Automate Procedures Identify objects without making subjective decisions

20 Run ImageJ from R Open connection to an image Use IJ$run() to access macros Great potential for automating image processing from R

21 Run ImageJ from R However, some key macros not yet implemented (e.g. setautothreshold, imagecalculator)

22 package:rimage Author: Nikon Reads jpegs into RGB arrays Plot function defined for objects of class "imagematrix"

23 Analyze information Plots and statistical summaries of particles from image Single image Multiple images

24 Conclusions Images available? Ensure quality/validate method Choose useful measures Use analysis to make predictions

25 Acknowledgements Mango Solutions L. R. Contreras-Rojas, R. H. Guy NAPOLEON