Download Image Analysis primer - The Rutgers

Cellular Bioengineering
Boot Camp
Image Analysis
Overview of the Lab Exercises
Microscopy and Cellular Imaging
The purpose of this laboratory exercise is to develop an understanding of
the measurements of cellular property via biomedical imaging and
microscopy techniques.
After completing this laboratory experiment, you should be able to:
1. Obtain total cell population and percentage dead cells and quantify on
the parameter basis of color and cell diameter using the ImageJ
software.
2. Quantify, compare and relate different morphological cell
configurations to function.
Electromagnetic Spectrum
The wavelength
required to “see”
an object must be
the same size or
smaller than the
object
Cells ~10-100 um,
Larger than visible
light wavelengths
(400-800 nm)
Source: Wikipedia
Biomedical Image Processing - Pixels
Source: http://hosting.soonet.ca/eliris/remotesensing/bl130lec10.html
Images are comprised of pixels: 2-D Picture
Elements.
Each pixel contains intensity information: Gray-scale
Intensity (0-256 for 8-bit)
0 is black, 1 is white
Same Size, Different Pixel Sizes
A
C
B
D
• As A  B  C  D:
– Decreasing resolution
– Increasing pixel size
– Decreasing number of
pixels
– Image looks more
“pixelated”
– Reduction in image quality
and information
• In order to obtain the most
information from a digital
image, it is imperative to
obtain the highest digital
resolution possible
Biomedical Image Processing –
Segmentation & Thresholding
• A gray level histogram - graphical representation of the
grayscale levels that comprise an image.
• Can set thresholds as desired, ex: red line in the histogram
below to create binary images (all pixels either 0 or 1)
–
–
Reduces data storage
Images easier to analyze
Source: http://www.olympusmicro.com/primer/java/digitalimaging/processing/automaticthresholding/
Biomedical Image Processing –
Filtering
• Fourier transform decomposes an input image in the spatial domain to its
sine and cosine components. The transformed output image corresponds to
the frequency domain. At this point, filters can be applied to isolate
frequencies of interest.
•
•
•
High pass filters: used to filter out low frequencies (for edge enhancement)
Low pass filters: used to filter out high frequencies (for smoothing)
Band pass filters: screen out low and high frequencies
Original Image
Source: http://hosting.soonet.ca/eliris/remotesensing/bl130lec10.html
High-pass filter applied
Biomedical Image Processing –
Filtering
• Fourier transform decomposes an input image in the spatial domain to its
sine and cosine components. The transformed output image corresponds to
the frequency domain. At this point, filters can be applied to isolate
frequencies of interest
•
•
•
High pass filters: used to filter out low frequencies (for edge enhancement)
Low pass filters: used to filter out high frequencies (for smoothing)
Band pass filters: screen out low and high frequencies
Original Image
Source: http://hosting.soonet.ca/eliris/remotesensing/bl130lec10.html
Low-pass filter applied
Overview of the Lab Exercises –
automated quantification routines
Cells can be selected & quantified according to different parameters
• cell physical characteristics (diameter, area, elongation etc)
• cell fluorescence intensity
Diameter
measurement
Cell recognition
using fluorescence
intensity
Criteria (thresholds)
ID Particle
Mean Green
ID Particle
Mean Green
1
211
7
225
2
209
8
222
3
170
9
205
4
219
10
210
5
213
11
214
6
196
12
222
ImageJ generated
fluorescence intensity
values
(excel spreadsheet
format)
Software cell
recognition using
diameter
criteria
Quantifying morphology of cells
• By selecting specific cells (using Regions Of Interest – ROI), different parameters
can be assessed:
• cell area, cell diameter, elongation, cell orientation etc
Regions Of
Interest
(ROI)
Undifferentiated cells
Differentiated cells
Area
Mean
Major
Minor
Circ.
1
604.0
154.3
62.3
12.3
0.3
2
218.0
173.8
29.1
9.5
0.4
3
496.0
153.3
52.6
12.0
0.4
4
315.0
155.9
36.7
10.9
0.4
5
236.0
161.6
21.8
13.8
0.4
6
263.0
163.7
42.8
7.8
0.3
7
198.0
153.8
27.7
9.1
0.6
Generated
data using
specified
parameters
(excel
spreadsheet
format)
Overview of the Lab Exercises –
automated quantification routines
In order to visualize what is happening inside of a cell, phase contrast images
(defining the outline of a cell) can be merged with fluorescent images (defining
internal cytoskeletal structures).
Phase Contrast Image
Fluorescent Image
(Phalloidin – cytoskeleton
actin filaments)
Merged Image
Example of Biomedical Image Processing
• Biomedical polymer sponges were saturated with a fluorophore
(fluorescein isothiocyanate - FITC) & imaged on a microscope using
fluorescence microscopy.
• Images of each porous field were digitized and stored on the computer.
• Digitized images were analyzed using biomedical image processing to
identify the number, shape, and location of pores. (Similar approaches can
be used to identify fluorescently labeled cells and cell number, cell viability,
cell morphology)
3. Open
1. Shading
Correction
2. Segmentation
4. Scrap
& Fill
5. Measurements
Tjia and Moghe, J. Biomed. Mater. Res (Appl Biomat) 43: 291, 1998