Image Analysis with Matlab

Image Analysis with Matlab - Image analysis for biology MBL...

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Image analysis for biology MBL Physiology Course 2008 Thanks to Hao Yuan Kueh, Eugenio Marco, Mike Springer and Sivaraj Sivaramakrishnan 1) Why Image Analysis?. ................................................................................................ 2 Image Analysis Strategies. .............................................................................................. 2 Layout of this tutorial. ..................................................................................................... 4 2) Basics. ......................................................................................................................... 5 Image types, data classes and image classes. .................................................................. 7 Basic Segmentation using Thresholding. ........................................................................ 9 Image histograms. ....................................................................................................... 9 Metamorph Stack Files . ............................................................................................ 13 EXERCISES . ................................................................................................................ 14 3) Contrast adjustments. ................................................................................................ 15 4) Spatial Filtering. ........................................................................................................ 18 Smoothing filters. .......................................................................................................... 20 Edge detection filters . ................................................................................................... 21 Laplacian filter. ............................................................................................................. 22 Median filter. ................................................................................................................. 23 EXERCISES . ................................................................................................................ 23 5) Morphological image processing. ............................................................................. 24 Dilation . ........................................................................................................................ 24 EXERCISE . .............................................................................................................. 26 Erosion. ......................................................................................................................... 28 EXERCISE . .............................................................................................................. 29 Opening and closing . .................................................................................................... 29 EXERCISE . .............................................................................................................. 29 Additional useful image processing tools. .................................................................... 30 Filling holes . ............................................................................................................. 30 Clearing border objects. ............................................................................................ 31 6) Image Segmentation. ................................................................................................. 31 Edge detection. .............................................................................................................. 31 Morphological Watersheds. .......................................................................................... 34 EXERCISES . ................................................................................................................ 37 7) Analysis of motion in biological images . ................................................................. 40 Designing Graphical User Interfaces in MATLAB. ..................................................... 40 Kymographs. ................................................................................................................. 45 Difference Images, Maximum Intensity Projections . ................................................... 46 Image Cross-correlation. ............................................................................................... 48 EXERCISES . ............................................................................................................ 49 Particle Tracking. .......................................................................................................... 50 8) REFERENCES . ........................................................................................................ 51
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1) Why Image Analysis? Biological images contain a wealth of objects and patterns, which may convey information about underlying mechanism in biology. Take a look at the following microscopy images: The left microscopy image shows a field of view of tissue-culture cells. One can ask: how many cells are there in this field of view? What is the average size? How much DNA is in each of the cells? How are the microtubule and actin cytoskeletons organized spatially? For the movie of the speckled spindle on the right, one can ask: What is the distribution of polymer mass in the spindle? What is the flux rate? Does it depend on the position along the spindle? Where is monomer getting incorporated and lost? Image processing and analysis provides a means to extract and quantify objects and patterns in image data and obtain answers to meaningful biological questions. It offers two advantages over traditional more manual methods of analysis: 1) Human vision, while highly sensitive, can be easily biased by pre-conceived notions of objects and concepts; automated image analysis provides an unbiased approach to extracting information from image data and testing hypotheses. 2) Once an image-analysis routine is devised, it can be applied to a large number of microscopy images, facilitating the collection of large amounts of data for statistical analysis. Image Analysis Strategies Image analysis involves the conversion of features and objects in image data into quantitative information about these measured features and attributes. Microscopy images in biology are often complex, noisy, artifact-laden and consequently require multiple image processing steps for the extraction of meaningful quantitative information. An outline of a general strategy for image analysis is presented below: 1) The starting point in image analysis typically involves a digital image acquired using a CCD camera. Raw microscopy images obtained on digital CCD cameras are subject to various
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Image Analysis with Matlab - Image analysis for biology MBL...

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