BUILT-IN MATHEMATICA SYMBOL

# ChanVeseBinarize

ChanVeseBinarize[image]
finds a two-level segmentation of image by computing optimal contours around regions of consistent intensity in image.

ChanVeseBinarize[image, marker]
uses the foreground pixels of marker as the initial contours.

## Details and OptionsDetails and Options

• ChanVeseBinarize implements an iterative active contour method to achieve a two-level segmentation of an image.
• ChanVeseBinarize works with arbitrary grayscale and multichannel images.
• In ChanVeseBinarize[image, marker], marker can be given either as an image, a graphics object, or a list of points in the standard image coordinate system, where x runs from 0 to width and y runs from 0 to height, and position corresponds to the bottom-left corner of the image.
• ChanVeseBinarize uses the Euclidean distance between channel vectors to determine the similarity between pixels inside and outside of the contour.
• ChanVeseBinarize iteratively minimizes a functional that is a weighted sum of the contour length, the enclosed area, and the deviation between the image and the two-level segmentation.
• The weights can be specified with the following options:
•  "AreaPenalty" 0 penalty associated with the area enclosed by the contour "LengthPenalty" 0.03 contour length penalty "LevelPenalty" {1.0,1.0} penalties for the total pixel deviations in the two segments "TargetColor" Automatic target foreground color
• With the setting , both foreground and background colors can be specified.
• The maximum number of iteration steps is given by the MaxIterations option with default setting 100.
• The Chan-Vese segmentation of an image domain into the two segments and with contour minimizes the following functional of image :
• The Chan-Vese functional is parametrized by the length penalty , the area penalty , and level penalties and .
• The Chan-Vese algorithm partitions image such that the first segment will differ as little as possible from constant and the second segment will deviate as little as possible from constant . If constants and are not specified, one assumes c1=Mean[f] in , and c2=Mean[f] in .
• The contour between the two resulting segments and will exhibit a short length for , and for the area of will tend to be small or for tend to be large.
• The weighting coefficients , , , and are accessible through the following options.
• ChanVeseBinarize works with Image3D objects.

## ExamplesExamplesopen allclose all

### Basic Examples (1)Basic Examples (1)

Binary segmentation of a satellite image:

 Out[1]=