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

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

Source: http://hosting.soonet.ca/eliris/remotesensing/bl130lec10.html

Same Size, Different Pixel Sizes

• 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

A B

C D

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

Source: http://hosting.soonet.ca/eliris/remotesensing/bl130lec10.html

Original Image 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

Source: http://hosting.soonet.ca/eliris/remotesensing/bl130lec10.html

Original Image Low-pass filter applied

Cells can be selected & quantified according to different parameters

• cell physical characteristics (diameter, area, elongation etc)

• cell fluorescence intensity

Diameter

measurement

Software cell

recognition using

diameter

criteria

Cell recognition

using fluorescence

intensity

Criteria (thresholds)

ID Particle Mean Green

1 211

2 209

3 170

4 219

5 213

6 196

ID Particle Mean Green

7 225

8 222

9 205

10 210

11 214

12 222

ImageJ generated

fluorescence intensity

values

(excel spreadsheet

format)

Overview of the Lab Exercises –

automated quantification routines

Undifferentiated cells

Differentiated cells

Regions Of

Interest

(ROI)

Generated

data using

specified

parameters

(excel

spreadsheet

format)

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

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

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

Overview of the Lab Exercises –

automated quantification routines

Example of Biomedical Image Processing

Tjia and Moghe, J. Biomed. Mater. Res (Appl Biomat) 43: 291, 1998

1. Shading Correction

2. Segmentation

3. Open4. Scrap

& Fill5. Measurements

• 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)