Generic class for representation of 2D/3D...5D images with Matlab.

This package consists in the Image class, that encapsulates a (possibly multidimensional) data array together with various meta-data used to interpret the data (spatial calibration, look-up table, grayscale extent...).

The Image class can manage up to five dimensions, corresponding to the X, Y, Z, Channels, and Time. Images are asociated to a type that indicates how the content should be interpreted: "color", "intensity", "binary", "label"...

Nearly 200 methods are provided for quickly applying image processing operators on image instances, by keeping relevant meta-data such as spatial calibration, and automatically inferring the type of the result images. A User Manual is available in pdf format.

The Image class is at the basis of the development of the ImageM application (http://github.com/mattools/ImageM). The MatStats package (https://github.com/mattools/matStats) may be necessary for some functions.

The following example performs a segmentation on a grayscale image. It uses computation of gradient, filtering, morphological processing, and management of label images.

% read a grayscale image img = Image.read('coins.png'); % compute gradient as a vector image.  grad = gradient(img); % Compute the norm of the gradient, and smooth gradf = boxFilter(norm(grad), [5 5]); figure; show(gradf, []); % compute watershed after imposition of extended minima emin = extendedMinima(gradf, 20, 4); grad2 = imposeMinima(gradf, emin, 4); lbl = watershed(grad2, 4); % display binary overlay over grayscale image show(overlay(img, lbl==0, 'g')); % cleanup segmentation and convert to RGB image lbl2 = killBorders(lbl); show(label2rgb(lbl2, 'jet', 'w'));

The following example presents various ways to explore and display the content of a 3D image.

% read data, adjust contrast, and specify spatial calibration img = adjustDynamic(Image.read('brainMRI.hdr')); img.Spacing = [1 1 2.5]; % show as three orthogonal planes figure; showOrthoPlanes(img, [60 80 13]); axis equal; % show as three orthogonal slices in 3D figure; showOrthoSlices(img, [60 80 13]); axis equal; view(3); axis(physicalExtent(img)); % display as isosurface figure; isosurface(gaussianFilter(img, [5 5 5], 2), 50); axis equal; axis(physicalExtent(img)); view([145 25]); light;