THE IMPORTANCE OF QUANTITATIVE VS. QUALITATIVE

IMAGES FOR LIFE SCIENCES APPLICATIONS

Eric Ramsden Lumenera

INTRODUCTION

• Quantitative contains the word “quantity” – referencing something that can be counted

• Quantitative research includes methods that produce data derived from reliable measurement or observation

• Qualitative properties are observed and can generally not be measured with a numerical result. Interpretation is subjective.

IMPORTANCE IN LIFE SCIENCES

Example : • Biochemical and cell based assays using a

microplate reader provide quantitative data on ex vivo cell behavior

• Viewing cells with a microscope allows researchers to see cellular and intra-cellular processes via fixed cells or with live cell imaging

Both methods are equally important to life science research, but there is a stronger shift towards automated quantitative analysis

QUANTITATIVE

What can we digitize for quantitative data analysis? • Dimensions • Locations • Intensities • Wavelengths • Colors • Time

CONTRAST

• The term Contrast is typically used in life sciences to describe the differentiation between 2 objects

• It can also be used to describe the differences between levels of intensity recorded by a pixel

• 8-bit digitization has a binary range of 256 (2 x 108) possible values while 16-bit digitization has 65,536 (2 x 1016) possible values.

CONTRAST

Can you differentiate between the lower 2 gradients?

10 gradient levels 20 gradient levels Analog intensities

CONTRAST

Bit depth is critical for sensitive measurements that go beyond what the human eye can resolve

CONTRAST

Pulsed-field gel electrophoresis (PFGE) is a highly discriminative molecular typing technique that is used in epidemiological studies worldwide.

ACCURATE VS PRETTY

• Many cameras are configured to provide pretty pictures • If too aggressive in setting the black level offset, then

important data might be missed • Nothing can be done in software if the data is missing. Once

clipped, it’s gone. Can you see the missing cell in Figure 2?

Figure 1. Image with small BLO Figure 2. Image with stronger BLO

DATA LOSS

Overexposure and saturation will alter imaging results. Once again, data is lost.

lost data

COLOR RENDERING

• Many debates in Ophthalmology imaging regarding accurate color reproduction

• Orange, yellow and red colors are predominant • Many cameras are improperly white balanced and/or apply

post processing to ‘beautify’, but render the image inaccurate

Images from: Analysis of Color Consistency in Retinal Fundus Photography: Application of Color Management and Development of an Eye Model Standard. Christye P. Sisson,1 Susan Farnand,1 Mark Fairchild,1 and Bill Fischer2

COLOR DISCRIMINATION

You can’t always trust your eyes to provide accurate color discrimination – especially if color blind.

RGB = 0, 200, 150

RGB = 0, 200, 150

LIGHTING

• A variety of lighting conditions, not always consistent, not as much control as with machine vision

• Many different lighting applications: Bright-field, Dark-field, Phase contrast, Differential Interference, Fluorescence etc.

• Cameras require tuned CCMs to match lighting

EYE, MONITOR, OR AUTOMATE

Subjectivity is slowly being removed via automation

AUTOMATION

Example: Automated, Quantitative Analysis of Histopathological Staining in Nuclei • Analysis of tissue sections after staining is subjective and labor

intensive • Typically, a pathologist must manually scan through a series of slides

to estimate the strength of staining using a discrete scoring system • For large studies, this process may involve multiple pathologists, which

leads to challenges with subjectivity, inter-rater reliability, and fatigue • Automated analysis of stained slides results

in an objective, repeatable and quantitative assessment of the staining level of a particular protein in the nuclei of cancer cells

CONCLUSION

• There is a strong shift towards better, quantifiable analysis to ensure accurate, repeatable results

• Depending on the application and work flow, there are many areas where calibration and proper use of equipment needs to be taken into consideration for accurate measurements

• Automation can help speed up the quantification process and remove subjective analysis which can result in errors

CONTACT INFORMATION

Eric Ramsden Director of Product Management Lumenera Corporation Ottawa, Ontario, Canada +1 (613) 736-4077 eric.ramsden@lumenera.com