# 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 marker to create an initial contour.

ChanVeseBinarize[image,marker,{μ,ν,λ1,λ2}]

specify the Chan-Vese weights μ, ν, λ1, and λ2.

# Details and Options

• ChanVeseBinarize implements an iterative active contour method to achieve a two-level segmentation of image.
• ChanVeseBinarize works with arbitrary 2D as well as 3D images.
• The target region marker can be any of the following:
•  markerimage a marker image {pos1,pos2,…} a list of positions fgcolor foreground color {{fgcolor,bgcolor}} foreground and background colors
• Positions posi are assumed to be in the standard image coordinate system.
• ChanVeseBinarize uses the Euclidean distance between channel vectors to determine the similarity between pixels inside and outside of the contour.
• The ChanVese segmentation of an image domain into the two segments and with contour minimizes the following functional of image :
• The functional is parametrized by the length penalty , the area penalty , and level penalties and .
• The ChanVese 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.
• 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 maximum number of iteration steps is given by the MaxIterations option with default setting 100.

# Examples

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## Basic Examples(2)

Binary segmentation of a color image:

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Segmentation of a 3D volume:

 In[1]:=
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