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. 255 255 199143 97 18732 12 3423 22 11 244 198 179123 94 19532 43 5213 32 11 253 217 23468 185 9713 12 2711 14 26

3. Pixels are identified by their position in a two-dimensional array, referenced by their 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 represents a gray level. A collection of these gray values is called a bitmap. 255 250 200 220 253 254 249 180 0 0 167 254 220 0 0 0 0 240 210 104 0 0 123 234 243 239 160 190 240 251

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. Bit Depth 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. Bit Depth 5 Gray Levels256 Gray Levels 8-bit gray scale 2 8 = 256 gray values

9. Dynamic Range 5 Gray Levels256 Gray Levels Dynamic Range Bit Depth / Camera Readout Noise

10. Limitations of Our Eyes 5 Gray Levels10 Gray Levels 20 Gray Levels40 Gray Levels

11. Limitations of Our Eyes

12. 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.

13. The Analytical Imaging Process Sample Preparation- Sectioning, staining, labeling, etc. Acquisition- how do we acquire an image? 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?

14. 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

15. 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 via fast fourier transform Morphological transformations such as erode, dilate, etc.

16. 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

17. Histogram Stretch Low Dynamic Range- Medium Contrast Full Dynamic Range- Good Contrast

18. Automatic Best-fit EqualizationBackground Flattening Background Correction Original

19. 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

20. Spatial Filter Examples

21. Examples of filter kernels: -3 -3 -3-3 0 +3 -2 -2 -2 0 0 0-3 0 +3 -2 +9 -2 +3 +3 +3-3 0 +3 -2 -2 -2 Horizontal edgevertical edgesharpen edge Spatial Filters

22. We can take an image, and transform it into a “frequency view” where we can see how the repeated ness/periodicity of attributes in an image can be enhanced or removed. Frequency filtering

23. Merge Images

24. Red Green Blue Extracting Color Channels

25. EDF: Extended Depth of Focus

26. Stitching of Images Automatic Microscope and Stage control with ScopePro Stitching and Tiling

27. 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

28. During the identification process, we may discover that we need to enhance the image further Object splitting – using filters, or manually splitting by drawing lines between touching objects Pseudo-color – adds false color to the image to show changes in gray values not noticeable to the human eye. Pre Measurement Steps

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

30. Color Segmentation

31. 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

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

33. Statistical Summaries

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

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

36. 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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