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From Images to Answers A Basic Understanding of Digital Imaging and Analysis.

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Presentation on theme: "From Images to Answers A Basic Understanding of Digital Imaging and Analysis."— Presentation transcript:

1 From Images to Answers A Basic Understanding of Digital Imaging and Analysis

2 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

3 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 spatial and intensity information.

4 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

5 Resolution Image Resolution- Overall image quality Spatial Resolution- Pixel size, image magnification Brightness Resolution- Pixel depth Optical Resolution- Lens characteristics How Big Is a Pixel?

6 Spatial Resolution

7 Dynamic Range How many gray levels between the darkest and brightest areas 8-bit 2 8 = 256 gray values 12-bit 2 12 = 4,096 gray values 16-bit 2 16 = 65,536 gray values

8 Dynamic Range 5 Gray Levels256 Gray Levels 8-bit gray scale 2 8 = 256 gray values

9 Limitations of Our Eyes 5 Gray Levels10 Gray Levels 20 Gray Levels40 Gray Levels

10 Limitations of Our Eyes

11 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.

12 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?

13 Acquisition After sample preparation, acquisition is the most important aspect 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

14 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

15 Brightness- Overall amount of “light” in an image Contrast- The degree of difference between lightest and darkest areas Gamma- Enhances ‘midtones’ while leaving extremes unchanged The higher the bit depth, the better the dynamic range of the image – allowing for greater information observance in “sensitive” samples Image Enhancement

16 Histogram Stretch Low Dynamic Range- Medium Contrast Full Dynamic Range- Good Contrast

17 Automatic Best-fit EqualizationBackground Flattening Background Correction Original

18 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

19 Spatial Filter Examples

20 Examples of filter kernels: Horizontal edgevertical edgesharpen edge Spatial Filters

21 Merge Images

22 Red Green Blue Extracting Color Channels

23 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

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

25 Color Segmentation

26 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

27 Area Percentages How much area is covered by the different intensities?

28 Statistical Summaries

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

30 Specialized Measurements Specialized measurements may be made with standard image analysis tools- Colocalization Object Tracking FRET Analysis Ratiometric Imaging

31 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

32 MEDIA CYBERNETICS Inc Georgia Avenue, Suite 200 Silver Spring, MD Leiden, The Netherlands San Diego, California Pittsburgh, Pennsylvania Shanghai, China Singapore Wokingham, United Kingdom


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