Image Analysis

Image processing techniques for image analysis

Image analysis involves processing an image into fundamental components in order to extract statistical data. Image analysis can include such tasks as finding shapes, detecting edges, removing noise, counting objects, and measuring region and image properties of an object.

Image analysis is a broad term that covers a range of techniques that generally fit into these subcategories:

Here are examples of these image processing techniques:

Enhancing grayscale images with histogram equalization
Enhancing grayscale images with histogram equalization.
Segmentation using Sobel edge detection
Segmenting images using Sobel Edge detection method.
Correcting nonuniform illumination with morphological operators
Removing noise with morphological operations like image opening.
Extracting statistical data using the Image Region Analyzer.
Extracting statistical data using the Image Region Analyzer.

You can perform image analysis in MATLAB® with the Image Processing Toolbox™, which provides image processing algorithms, tools, and a comprehensive environment for data analysis, visualization, and algorithm development.

Examples and How To

Software Reference

See also: color profile, image thresholding, image enhancement, image reconstruction, image segmentation, image transform, image registration, digital image processing, image and video processing, Steve on Image Processing (blog), affine transformation, lab color