Image segmentation

When trying to isolate objects, one strategy might be to use regular morphological operations (opening/closing) to remove small objects that are not of interest. In case small objects are made of a large number of pixels, this operation might impair the remaining objects' contours. An alternative strategy might be to use morphological reconstruction. In short, seed is placed on the image, on objects, then conditional dilation is performed from those seeds.

Here is how to proceed, using MorphoLibJ:

1. Open an image
2. Use the multi-point selection tool and place seeds on objects of interest
3. Create a new image of same size, black background
4. Transfer the selection to the new image (Edit/Selection/Restore selection)
5. Draw (make sure you're using white foreground) the multiple point selection
6. Launch the Morphological reconstruction plugin: Plugins > MorphoLibJ > Morphological reconstruction

ABSnake can segment complex structures in 2D images as well as 3D or temporal images. It uses a new active contour model based on a geometrical approach for correctly following invaginated structures.

MosaicIA is a tool to analyze the spatial distribution of objects in images. It estimates from an observed particle or object distribution what hypothetical interaction between the objects is most likely to have created this distribution.

In this case study, MATLAB, the Image Processing and Signal Processing toolboxes were used to determine the green intensities from a small portion of a microarray image containing 4,800 spots. A 10x10 pattern of spots was detected by averaging rows and columns to produce horizontal and vertical profiles. Periodicity was determined automatically by autocorrelation and used to form an optimal length filter for morphological background removal. A rectangular grid of bounding boxes was defined. Each spot was individually addressed and segmented by thresholding to form a mask. The mask was used to isolate each spot from surrounding background. Individual spot intensity was determined by integrating pixel intensities. Finally, integrated intensities were tabulated and saved to a data file for subsequent statistical analysis to determine which genes matter most.

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Perfect for the beginner, this demo illustrates simple object detection (segmentation, feature extraction), measurement, and filtering. Requires the Image Processing Toolbox (IPT) because it demonstrates some functions supplied by that toolbox, plus it uses the "coins" demo image supplied with that toolbox. If you have the IPT (you can check by typing ver on the command line), you should be able to run this demo code simply by copying and pasting this code into a new editor window, and then clicking the green "run" triangle on the toolbar. First finds all the objects, then filters results to pick out objects of certain sizes. The basic concepts of thresholding, labeling, and regionprops are demonstrated with a simple example.

It's a good tutorial for those users new to MATLAB's image processing capabilities to learn on, before they go on to more sophisticated algorithms.