Segmentation Analysis

Mathematica includes a variety of image segmentation techniques such as clustering, watershed, region growing, and level set as well as a rich set of functions for post-processing and analyzing the result of the segmentation.


Image Preparation

ColorQuantize reduce the number of distinct colors in an image

FillingTransform reduce noise to create smooth regions in an image

GradientFilter, RangeFilter create an edge map from an image

FindThreshold ▪ Threshold ▪ ImageClip

Binary Segmentation

Binarize segmentation by thresholding pixel intensities

MorphologicalBinarize ▪ RegionBinarize ▪ ChanVeseBinarize


ArrayComponents find identical components

MorphologicalComponents find morphologically connected components

ImageForestingComponents image segmentation using various methods

ClusteringComponents segmentation based on cluster analysis

WatershedComponents segmentation based on watershed methods

Component Analysis

ComponentMeasurements shape and color analysis

SelectComponents ▪ DeleteSmallComponents ▪ DeleteBorderComponents

Colorize color every segment differently

HighlightImage highlight region of interest

New to Mathematica? Find your learning path »
Have a question? Ask support »