LecturesPart23

LecturesPart23 - Computational Biology, Part 23 Biological...

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Unformatted text preview: Computational Biology, Part 23 Biological Imaging II Robert F. Murphy Copyright © 1996, 1999, 2000-2006. Copyright All rights reserved. Outline s Image Display s Image Processing s Image Analysis s Image Interpretation From Images to Knowledge Image Image Processing Image Image Analysis Image Image Interpretation Image Numbers Knowledge Image Display s Operations that change display without Operations changing image changing x LUT - grayscale or color x Contrast stretching s Operations that change image x reversible x non-reversible (majority) Image Display - LUT change Image Display - LUT change Image Display - Enhance contrast Image Display Original (before contrast After enhancement) enhancement uses full range Thresholding s Thresholding refers to the division of the Thresholding pixels of an image into two classes: those below a certain value (the threshold) and threshold and those at or above it. The two classes are often shown in white and black, respectively. respectively. s Thresholding serves as a means to consider Thresholding only a subset of the pixels of an images. subset Thresholding s The choice of threshold must be made The 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 pixel s The threshold can be specified as a multiple The of the background value (normally the most common value) for partial automation common Thresholding Thresholding s Once a threshold has been applied, the Once resulting image may be resulting x displayed in black and white x displayed with above threshold pixels at their original intensities and below threshold pixels in black in Thresholding s Once a threshold has been applied, the Once resulting image may be resulting x saved as a new image with only pixels above threshold being retained (others set to 0) threshold x saved as or converted to a binary image (above converted threshold pixels set to 1, below threshold pixels set to 0) set Binary image operations s Erosion x Remove pixels from edges of objects x Set “on” pixel to “off” if four or more of its Set eight neighbors are white eight s Dilation x Add pixels to edges of objects x Set “off” pixel to “on” if four or more of its Set neighbors are black neighbors Binary image operations Process/Binary/Threshold does auto threshold and applies it to make binary image Binary image operations Binary image operations This image shows just the pixels that were turned off by the erode operation Binary image operations s Open x Smooth objects and fill in small holes x Erosion followed by dilation s Close x Smooth objects and fill in small holes x Dilation followed by erosion Binary image operations s Outline x Find “on” pixel, trace around outside until Find return to first “on” pixel return s Skeletonize x Remove pixels from the edges of objects until Remove the objects are one pixel wide the Binary image operations Binary image operations - outline Basic Image Processing Operations s Image Math s Kernel/Filter Operations s Image Calculator Arithmetic Operations s Two cases: x Perform a single operand operation (e.g., Perform single logarithm, square root) on each pixel of an image image x Perform a dual operand operation (e.g., add, Perform dual multiply) on each pixel of an image using a constant as the second operand constant s In both cases, the result is usually stored in In the same pixel location (“storing in place”) the Arithmetic Operations Kernel/Filter Operations s Basic idea: Use a matrix (usually square and Basic of odd dimension, e.g., 3x3) in combination with an image to generate a new image with s Algorithm: x For each pixel in the image (the current pixel) For current pixel x Align the matrix to center it on that pixel x For each position in the matrix, multiply the For corresponding pixel value in the image by the value in the matrix and sum the results value x Store the result in the current pixel Store current pixel Kernel/Filter Operations s A matrix used in this fashion is called a matrix kernel or filter or s Note that the operation is different from Note matrix multiplication of the kernel by the image because image x the dimensions don’t match, and x all elements of the matrix are combined to give all one result one Common Kernel Operations used in Image Processing s Smoothing s Sharpening s Edge Finding s Original Original image image Examples of Kernel Operations using NIH Image s Smooth s Results Results of one Smooth s Results Results of a second Smooth Examples of Kernel Operations using NIH Image s Close smoothed image, reopen original image, then Sharpen s Original Original image image s Image Image after one Sharpen Sharpen s Image Image after a second Sharpen Sharpen Examples of Kernel Operations using NIH Image s Close sharpened image, reopen original image, then Find Close Edges Edges s Image Image after Find Edges Edges Example kernels s Smoothing 111 141 111 Example kernels s Sharpen -1 -1 -1 -1 1 2 -1 -1 -1 -1 Example kernels s Edge detection (Sobel) 121 000 -1 -2 -1 Image Math s Basic idea: Combine two images using an Basic dual operand operator to generate a new image image s Algorithm: x For each pixel in the first image, operate on it For using the corresponding pixel in the second image and store the result in the corresponding pixel in a new (output) image pixel Image Math s Any operator can be used s Most common operators: x division: generate ratio image x logical AND: mask one image with another logical mask (usually binary) image (usually Examples of Image Math using NIH Image s Open original image and sharpen once (save as Open Abdomen.sharpen1), reopen original image Abdomen.sharpen1), s Ratio of Ratio sharp to original image (shows regions affected by sharpen) sharpen) Image Math vs. Arithmetic Operations s Note difference between Image Math which does Note an operation on two images and Arithmetic which does an operation on a single image and a constant does Summary: Basic Image Processing Operations s Arithmetic Operations x Inputs: Image, Constant (optional) x Common use: Subtract background s Kernel Operations x Inputs: Image, Kernel x Common use: Smoothing s Image Math x Inputs: Two images x Common use: Generate ratio image Image Processing s Basic example of image processing: find Basic objects in an image and describe them numerically numerically Object finding (Particle analysis) s Principle: Identify a contiguous set of pixels Principle: that are all above some threshold that s Implementation: x Start with a binary (thresholded) image x Find a pixel that is “on” and start a list or map x Recursively search all nearest neighbors for Recursively additional pixels that are on and add them to the list or map list Object finding (Particle analysis) Start with a thresholded image (Image/Adjust/Threshold) Object finding (Particle analysis) Object finding (Particle analysis) Save as Excel file using Save As... Object finding (Particle analysis) s Uses: x Counting objects x Obtaining area measurements for objects x Obtaining integrated intensity x Isolating objects for other processing ...
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This note was uploaded on 01/13/2012 for the course BIO 101 taught by Professor Staff during the Fall '10 term at DePaul.

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