Computational Biology, Part 23 Biological Imaging II Robert F. Murphy Copyright  1996, 1999, All rights reserved.

Outline Image Display Image Display Image Processing Image Processing Image Analysis Image Analysis Image Interpretation Image Interpretation

From Images to Knowledge Image Processing Image Image Analysis Image Image Interpretation Image Numbers Knowledge

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)

Image Display - LUT change

Image Display - Enhance contrast

Image Display After enhancement uses full range Original (before contrast enhancement)

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.

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

Thresholding

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

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)

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

Binary image operations Process/Binary/Threshold does auto threshold and applies it to make binary image

Binary image operations

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

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

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

Binary image operations

Binary image operations - outline

Basic Image Processing Operations Image Math Image Math Kernel/Filter Operations Kernel/Filter Operations Image Calculator Image Calculator

Arithmetic Operations Two cases: Two cases:  Perform a single operand operation (e.g., logarithm, square root) on each pixel of an image  Perform a dual operand operation (e.g., add, multiply) on each pixel of an image using a constant as the second operand In both cases, the result is usually stored in the same pixel location (“storing in place”) In both cases, the result is usually stored in the same pixel location (“storing in place”)

Arithmetic Operations

Kernel/Filter Operations Basic idea: Use a matrix (usually square and of odd dimension, e.g., 3x3) in combination with an image to generate a new image Basic idea: Use a matrix (usually square and of odd dimension, e.g., 3x3) in combination with an image to generate a new image Algorithm: Algorithm:  For each pixel in the image (the current pixel)  Align the matrix to center it on that pixel  For each position in the matrix, multiply the corresponding pixel value in the image by the value in the matrix and sum the results  Store the result in the current pixel

Kernel/Filter Operations A matrix used in this fashion is called a kernel or filter A matrix used in this fashion is called a kernel or filter Note that the operation is different from matrix multiplication of the kernel by the image because Note that the operation is different from matrix multiplication of the kernel by the image because  the dimensions don’t match, and  all elements of the matrix are combined to give one result

Common Kernel Operations used in Image Processing Smoothing Smoothing Sharpening Sharpening Edge Finding Edge Finding

Original image Original image

Examples of Kernel Operations using NIH Image Smooth Smooth

Results of one Smooth Results of one Smooth

Results of a second Smooth Results of a second Smooth

Examples of Kernel Operations using NIH Image Close smoothed image, reopen original image, then Sharpen Close smoothed image, reopen original image, then Sharpen

Original image Original image

Image after one Sharpen Image after one Sharpen

Image after a second Sharpen Image after a second Sharpen

Examples of Kernel Operations using NIH Image Close sharpened image, reopen original image, then Find Edges Close sharpened image, reopen original image, then Find Edges

Image after Find Edges Image after Find Edges

Example kernels Smoothing Smoothing

Example kernels Sharpen Sharpen

Example kernels Edge detection (Sobel) Edge detection (Sobel)

Image Math Basic idea: Combine two images using an dual operand operator to generate a new image Basic idea: Combine two images using an dual operand operator to generate a new image Algorithm: Algorithm:  For each pixel in the first image, operate on it using the corresponding pixel in the second image and store the result in the corresponding pixel in a new (output) image

Image Math Any operator can be used Any operator can be used Most common operators: Most common operators:  division: generate ratio image  logical AND: mask one image with another (usually binary) image

Examples of Image Math using NIH Image Open original image and sharpen once (save as Abdomen.sharpen1), reopen original image Open original image and sharpen once (save as Abdomen.sharpen1), reopen original image

Ratio of sharp to original image (shows regions affected by sharpen) Ratio of sharp to original image (shows regions affected by sharpen)

Image Math vs. Arithmetic Operations Note difference between Image Math which does an operation on two images and Arithmetic which does an operation on a single image and a constant Note difference between Image Math which does an operation on two images and Arithmetic which does an operation on a single image and a constant

Summary: Basic Image Processing Operations Arithmetic Operations Arithmetic Operations  Inputs: Image, Constant (optional)  Common use: Subtract background Kernel Operations Kernel Operations  Inputs: Image, Kernel  Common use: Smoothing Image Math Image Math  Inputs: Two images  Common use: Generate ratio image

Image Processing Basic example of image processing: find objects in an image and describe them numerically Basic example of image processing: find objects in an image and describe them numerically

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

Object finding (Particle analysis) Start with a thresholded image (Image/Adjust/Threshold)

Object finding (Particle analysis)

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