Overview

This book develops a set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing. Image Processing Toolbox apps let you automate common image processing workflows. You can interactively segment image data, compare image registration techniques, and batch-process large datasets. Visualization functions and apps let you explore images, 3D volumes, and videos; adjust contrast; create histograms; and manipulate regions of interest (ROIs). Image Processing Toolbox supports four methods to generate a binary mask. The binary mask defines a region of interest (ROI) of the original image. Mask pixel values of 1 indicate the image pixel belongs to the ROI. Mask pixel values of 0 indicate the image pixel is part of the background. Any binary image can be used as a mask, provided that the binary image is the same size as the image being filtered. You can create a mask from a grayscale image by classifying each pixel as belonging to either the region of interest or the background. Filtering a region of interest (ROI) is the process of applying a filter to a region in an image, where a binary mask defines the region. For example, you can apply an intensity adjustment filter to certain regions of an image. The blurring, or degradation, of an image can be caused by many factors: Movement during the image capture process (by the camera or, when long exposure times are used, by the subject), Out-of-focus optics (use of a wide-angle lens, atmospheric turbulence, or a short exposure time, which reduces the number of photons captured) and Scattered light distortion in confocal microscopy Based on this model, the fundamental task of deblurring is to deconvolve the blurred image with the PSF that exactly describes the distortion. The Image Processing Toolbox software provides functions that help you work with color image data. This toolbox supports conversions between members of the CIE family of device-independent color spaces. Certain image processing operations involve processing an image in sections, called blocks or neighborhoods, rather than processing the entire image at once. Several functions in the toolbox, such as linear filtering and morphological functions, use this approach. The toolbox includes several functions that you can use to implement image processing algorithms as a block or neighborhood operation. These functions break the input image into blocks or neighborhoods, call the specified function to process each block or neighborhood, and then reassemble the results into an output image. If you have a Parallel Computing Toolbox license, you can take advantage of multiple processor cores on your machine by specifying the blockproc setting 'UseParallel' as true. The Image Processing Toolbox includes many functions that support the generation of efficient C code using MATLAB Coder. To take advantage of the performance benefits offered by a modern graphics processing unit (GPU), certain Image Processing Toolbox functions have been enabled to perform image processing operations on a GPU. This can provide GPU acceleration for complicated image processing workflows.

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