From Images to Answers

A Basic Understanding of Digital Imaging and Analysis

What is an Image?

Computers store data and understand data in numerical form. We can say that a digital image is a numerical representation of a “picture” – a set of numbers interpreted by the computer which creates a visual representation that is understood by humans.

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244 198 179123 94 19532 43 5213 32 11

253 217 23468 185 9713 12 2711 14 26

Pixels are identified by their position in a grid (two-dimensional array), referenced by its row (x), and column (y).

The Pixel Array

Pixel- A “picture element”.Each element contains spatialand intensity information.

Bitmaps

At each pixel position the image is sampled and quantified. An integer representing the brightness or darkness of the image is generated for each pixel This integer. A collection of these gray values is called a bitmap.

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210 104 0 0 123 234

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Resolution

Image Resolution- Overall image qualitySpatial Resolution- Pixel size, image magnificationBrightness Resolution- Pixel depthOptical Resolution- Lens characteristics

How Big Is a Pixel?

Spatial Resolution

Dynamic Range

How many gray levels between the darkest and brightest areas

8-bit 28 = 256 gray values

12-bit 212 = 4,096 gray values

16-bit 216 = 65,536 gray values

Dynamic Range

5 Gray Levels 256 Gray Levels

8-bit gray scale 28 = 256 gray values

Limitations of Our Eyes

5 Gray Levels 10 Gray Levels

20 Gray Levels 40 Gray Levels

Limitations of Our Eyes

What components are involved in imaging?

Input device- the source of the images; camera, microscope, etc.

Interface hardware- the connection between the input device and the computer.

Imaging software- the user interface to all the imaging components.

Output devices- printers, image storage devices, monitors.

The Analytical Imaging Process

Sample Preparation- Sectioning, staining, labeling, etc.

Acquisition- how do we acquire an image into the computer?

Enhancement- how do we make it look better to extract information?

Identification- which attributes of the image are we interested in?

Measurement- what information can we obtain?

Report Generation- how can we present this information?

Archive- how can we store the information?

Acquisition

After sample preparation, acquisition is the most importantaspect of image analysis.

Has the specimen been prepared properly?

Is the imaging apparatus properly set up?• Kohler Illumination for Microscopes• Clean Optics• Color cameras should be color-balanced• Monochrome cameras should have dark-field subtraction• Good dynamic range should be established in the image• For fluorescence images- turn OFF autoexposure

Image Enhancement

There are basic ways to enhance an image: • Modify its intensity index: brightness, contrast, gamma• Background correction• Apply a spatial filter or arithmetic operation

And advanced enhancement methods:• Manipulate the image frequencies: fast fourier transform• Morphological transformations: erode, dilate

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Histogram Stretch

Low Dynamic Range- Medium Contrast

Full Dynamic Range- Good Contrast

Automatic Best-fit Equalization Background Flattening

Background Correction

Original

Spatial filters change the look of an image and are divided into two categories.

Convolution Filters:

• Low-pass- Blurs or smoothes an object

• Sharpen- Enhances all intensity transitions

• Median- Removes random impulse noise

Morphological Filters:

• Edge Detection- highlights edges

• Erosion- Makes objects smaller

• Dilation- Makes objects larger

Spatial Filtering

Spatial Filter Examples

Merge Images

Red

Green

Blue

Extracting Color Channels

Once the attributes of an image are enhanced and clearly visible, identification can be done as follows:

• Thresholding techniques- allowing the software to identify objects, based on intensity variations from background or other objects using either grayscale or color intensities

• Area of Interest (AOI)- manually defining the objects

Object Identification

Grey Scale Threshold

The overall objective of thresholding is to extract the objects of interest- to distinguish them from other objects or background.

Color Segmentation

• Size

• Shape

• Intensity/Integrated Optical Density

• Populations

• Statistics

Once objects are identified, we are dealing with a set of pixels, which are a set of numbers. We are then able to measure anything we need such as

Measurement Parameters

Area Percentages

How much area is covered by the different intensities?

Statistical Summaries

Using intensity transitions it is possible to measure thicknesses of objects. Edge tracing tools may be employed to do this:

Thickness Measurements

Specialized Measurements

Specialized measurements may be made with standard image analysis tools-

• Colocalization

• Object Tracking

• FRET Analysis

• Ratiometric Imaging

Dynamic Data Exchange (DDE)- Sending data to Excel for further statistical analysis

Data Collection- Collection of analysis data from multiple images into a single space, which can then be sent elsewhere or used to create reports

Report Generation- Custom templates used to create standardized reports.

Data Output

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