1. Computational Biology, Part 22 Biological Imaging II Robert F. Murphy Copyright  1996, 1999, 2000. All rights reserved.

2. Image Display Operations that change display without changing image Operations that change display without changing image  LUT - grayscale or color  Contrast stretching Operations that change image Operations that change image  reversible  non-reversible (majority)

3. Image Display

6. Note that image is identical to original (LUT change is reversible)

7. Image Display

8. After enhancement uses full range Original (before contrast enhancement)

9. Thresholding Thresholding refers to the division of the pixels of an image into two classes: those below a certain value (the threshold) and those at or above it. The two classes are often shown in white and black, respectively. Thresholding refers to the division of the pixels of an image into two classes: those below a certain value (the threshold) and those at or above it. The two classes are often shown in white and black, respectively. Thresholding serves as a means to consider only a subset of the pixels of an images. Thresholding serves as a means to consider only a subset of the pixels of an images.

10. Thresholding The choice of threshold must be made empirically by considering the nature of the sample, the type and number of objects expected in the image, and/or a histogram of pixel values The choice of threshold must be made empirically by considering the nature of the sample, the type and number of objects expected in the image, and/or a histogram of pixel values The threshold can be specified as a multiple of the background value (normally the most common value) for partial automation The threshold can be specified as a multiple of the background value (normally the most common value) for partial automation

11. Thresholding White on black images need to be inverted before some of NIH Image’s operations work as desired

12. Thresholding

13. Once a threshold has been applied, the resulting image may be Once a threshold has been applied, the resulting image may be  displayed in black and white  displayed with above threshold pixels at their original intensities and below threshold pixels in black

14. Thresholding Once a threshold has been applied, the resulting image may be Once a threshold has been applied, the resulting image may be  saved as a new image with only pixels above threshold being retained (others set to 0)  saved as or converted to a binary image (above threshold pixels set to 1, below threshold pixels set to 0)

15. Binary image operations Erosion Erosion  Remove pixels from edges of objects  Set “on” pixel to “off” if four or more of its eight neighbors are white Dilation Dilation  Add pixels to edges of objects  Set “off” pixel to “on” if four or more of its neighbors are black

16. Binary image operations “Make Binary” is necessary before Binary operations can be used

17. Binary image operations

19. This image shows just the pixels that were turned off by the erode operation

20. Binary image operations Open Open  Smooth objects and fill in small holes  Erosion followed by dilation Close Close  Smooth objects and fill in small holes  Dilation followed by erosion

21. Binary image operations Outline Outline  Find “on” pixel, trace around outside until return to first “on” pixel Skeletonize Skeletonize  Remove pixels from the edges of objects until the objects are one pixel wide

22. Binary image operations

24. Object finding (Particle analysis) Principle: Identify a contiguous set of pixels that are all above some threshold Principle: Identify a contiguous set of pixels that are all above some threshold Implementation: Implementation:  Start with a binary (thresholded) image  Find a pixel that is “on” and start a list or map  Recursively search all nearest neighbors for additional pixels that are on and add them to the list or map

25. Object finding (Particle analysis) Remember to start with a thresholded image converted to binary!

26. Object finding (Particle analysis)

29. Export... gives the same result as Save As...

30. Object finding (Particle analysis) Results file can be opened within Simpletext (use fixed width font!) or read by Excel (e.g., for plotting)

31. Object finding (Particle analysis) Uses: Uses:  Counting objects  Obtaining area measurements for objects  Obtaining integrated intensity  Isolating objects for other processing