Analyzing Images

Image Processing Toolbox provides a comprehensive suite of reference-standard algorithms and graphical tools for image analysis tasks such as statistical analysis, feature extraction, and property measurement.

Statistical Functions

Statistical functions let you analyze the general characteristics of an image by:

• Computing the mean or standard deviation
• Determining the intensity values along a line segment
• Displaying an image histogram
• Plotting a profile of intensity values

Edge-Detection Algorithms

Edge-detection algorithms let you identify object boundaries in an image. These algorithms include the Sobel, Prewitt, Roberts, Canny, and Laplacian of Gaussian methods. The powerful Canny method can detect true weak edges without being "fooled" by noise.

Image Segmentation Algorithms

Image segmentation algorithms determine region boundaries in an image. You can explore many different approaches to image segmentation, including automatic thresholding, edge-based methods, and morphology-based methods such as the watershed transform, often used to segment touching objects.

Morphological Operators

Morphological operators enable you to detect edges, enhance contrast, remove noise, segment an image into regions, thin regions, or perform skeletonization on regions. Morphological functions in Image Processing Toolbox include:

• Erosion and dilation
• Opening and closing
• Labeling of connected components
• Watershed segmentation
• Reconstruction
• Distance transform

Advanced Image Analysis

Image Processing Toolbox also contains advanced image analysis functions that let you:

• Measure the properties of a specified image region, such as the area, center of mass, and bounding box
• Detect lines and extract line segments from an image using the Hough transform
• Measure properties, such as surface roughness or color variation, using texture analysis functions