Image Processing Toolbox
Perform image processing, visualization, and analysis
Image Processing Toolbox™ provides a comprehensive 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 data sets. Visualization functions and apps let you explore images, 3D volumes, and videos; adjust contrast; create histograms; and manipulate regions of interest (ROIs).
You can accelerate your algorithms by running them on multicore processors and GPUs. Many toolbox functions support C/C++ code generation for desktop prototyping and embedded vision system deployment.
Acquiring and Importing Data
Import images and video generated by a wide range of devices, including webcams, digital cameras, satellite and airborne sensors, medical imaging devices, microscopes, telescopes, and other scientific instruments.
Support for a number of specialized image file formats. For medical images, it supports DICOM files, including associated metadata, as well as the Analyze 7.5 and Interfile formats.
Apps for Exploration and Discovery
Use apps to explore and discover various algorithmic approaches. With the Color Thresholder app, you can segment an image based on various color spaces. The Image Viewer app lets you interactively place and manipulate ROIs, including points, lines, rectangles, polygons, ellipses, and freehand shapes.
Enhance contrast, remove noise, thin regions, or perform skeletonization on regions.
Correct blurring caused by out-of-focus optics, movement by the camera or the subject during image capture, atmospheric conditions, short exposure time, and other factors.
Explore a 3D volume by using different visualization methods to explore the structure of the data. You can map the pixel intensity of a 3D volume to opacity to highlight a specific region within the volume.
Use many 3D-specific functions in addition to ND functions that enable complete image processing workflows with 3D data.
Use programmatic functions and interactive apps to perform 3D segmentation. You can use thresholding, active contours, semantic segmentation and other techniques to perform segmentation of 3D Data.
Identify object boundaries in an image using pre-built algorithms. These algorithms include the Sobel, Prewitt, Roberts, Canny, and Laplacian of Gaussian methods.
Image Region Analysis
Calculate the properties of regions in images, such as area, centroid, and orientation. Use the Image Region Analysis App to automatically count, sort, and remove regions based on properties.
Hough Transform, Statistical Functions, and Color Space Conversions
Find line segments, line endpoints, and circles. Statistical functions let you analyze the characteristics of an image. Color-space conversion accurately represents color independently from devices.
Image Segmentation Techniques
Determine region boundaries in an image and explore different approaches to image segmentation. Use segmentation apps to explore these techniques interactively.
Use watershed segmentation to separate touching objects in an image. The watershed transform is often applied to this problem.
Image Registration Methods
Use intensity-based image registration, which automatically aligns images using relative intensity patterns. Perform multimodal 3D registration and non-rigid registration, and visually inspect results by creating composite images that highlight misalignments.
Automatically generate C, C++, and HDL code. Many image processing functions support code generation, so you can run image processing algorithms on PC hardware, FPGAs, ASICs, and embedded hardware.
Use GPUs and multicore processors to improve your application and model performance.
Support for class balancing, labeled data, and additional TIFF compression schemes
Image Quality Metrics
Measure multi-scale structural similarity (MS-SSIM) index
Perform mode filtering on a 2-D image or 3-D volume for filtering categorical or labeled data
Extract ROI contour data from DICOM-RT structure set
Process images that are too large to fit in memory
Deep Learning Data Preprocessing
Perform additional image augmentations
With just a few lines of MATLAB code, you can apply deep learning techniques to your work whether you’re designing algorithms, preparing and labeling data, or generating code and deploying to embedded systems.