These remaining methods also have their distinguishing characteristics, as seen by the median projection which gives the clearest rendering with the trade-off of less of filtering out more of the image in the projection. In the case of the MRI stack, maximum intensity is a preferable method to view the external head rendering while other methods of projection may be used to examine the internal brain components. As seen below, by applying a z transform to an MRI stack using different projection methods, the results can be very different. The appropriate projection type will vary depending on the type of data being represented. There are six different projection types to choose from: average intensity, maximum intensity, minimum intensity, sum slices, standard deviation, and median. The default for these values is the endpoint slices of the stack. When Z Project opens, it prompts for a start and stop slice, which will determine the range of the stack that will be included in the z projection. This process may be used to highlight specific data from the stack and is accessed using Image › Stacks › Z Project… Z Project is a method of analyzing a stack by applying different projection methods to the pixels within the stack. There are several ways to “flatten” the 3D stack. A montage will allow the 3-D dataset to be visualized in 2-D, but results in each frame being very small. If you’d like to help, check out the how to help guide! Stack-ProjectionsĪ z-series is generally difficult to represent as a 2-D image for publication purposes.
So as you see the image looks 3D but the data is actually 2D.The content of this page has not been vetted since shifting away from MediaWiki. Or we can use any other colormap of our choice.Ĭolormap(jet(200)) %// The size can be different than the original colormap For instance, we can use the original colormap (stored in map) in order to get the following: image(X) If we wish to display this 2D data as a colorful we need to provide a suitable colormap. So a 2D variable, even though the original image ( a) was 3D (rgb). Let's read and use the rgb2ind function to store the image data in variable X and its associated colormap in variable map. So if the colormap is a 256x3 array, for example, the image will be made of indices ranging from 0 to 255.įor example, let's consider the peppers.png demo image that ships with Matlab. In this case the colormap will be a Nx3 array where each row corresponds to an index present (or not) in the image data. If I understood right you can store the image data as a 2D array and use a colormap to assign it colors, displaying it as a "colorful" image.
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