BIOL 643: Quantitative Imaging Techniques

Study Notes

BIOL 643 course taught by Dr. Noura S. Abou Zeinab, Assistant Professor in Cell Biology and Histology
Course focuses on quantitative imaging from cells to molecules

Quantitative image analysis uses computational algorithms to extract information, features, measurements and patterns from digital images
Useful for analyzing large image datasets
Algorithms and manual intervention are used to extract quantitative data
Example: Developing Arabidopsis flower expressing mCitrine-ATML1 fluorescent protein
Processed image detects each sepal nucleus and quantifies mCitrine-ATML1 fluorescence

Microscopy: Initial image acquisition using a microscope
Original image: Raw image captured through microscopy
Pre-processing: Initial adjustments to the image to prepare it for analysis (e.g. noise removal, contrast enhancement). This stage generally fixes issues in the original image that can produce artifacts when doing analysis.
Segmentation: Dividing the image into different parts or regions of interest (e.g., separating cells or tissues in the image).
Post-processing: Further image adjustments for optimal analysis (e.g., color enhancement)
Data analysis: Extracting meaningful information and quantitative data from the processed image

Contrast: High contrast between objects of interest and the background is important
Color: Images with objects in one color and a black background provide the best results.
Structure: The structure of interest should be clearly separated

Optical Microscopy: The most common microscopy type using light.
Conventional Light Microscopy: Uses visible light to observe samples
Fluorescence Microscopy: Uses fluorescent dyes to visualize specific molecules
Confocal Microscopy: Uses a laser to illuminate a thin plane of the sample, eliminating out-of-focus light.
Phase-Contrast Microscopy: Converts phase shifts in light to brightness changes in the image, useful for transparent samples.

History: Invented by Robert Hooke in 1665
Types:
Simple microscopes: Uses a single lens for magnification (e.g., magnifying glass)
Compound microscopes: Uses multiple lenses to enhance magnification

Light passing through a thin (transparent) specimen produces phase shifts.
Microscope converts these phase shifts into brightness differences in the image.
Allows observation of transparent or colorless samples (e.g., living cells) without staining

3D image created from a series of 2D images (optical section images) captured at different depths of a sample.
Collected from a single biological sample.

Basic units of an image
Pixels are 2D
Voxels are 3D anaog to pixels
Each has intensity values measured by a detector, ranging from 0 to 255 for 8-bit images

Resolution is the ability to distinguish features in an image, limited by imaging system properties.
DPI is the number of dots per inch (pixels per unit distance);
Journals often require a 300 DPI for images.
Resampling for decreasing pixels is common, but not increasing resolution.

Two sufficiently close fluorophores can’t be resolved using traditional methods.
Super-resolution techniques overcome this limitation.

For each pixel, the algorithm selects the brightest z-stack slice.
This produces an image emphasizing the overall three-dimensional structure of the specimen, particularly its outer surface.