ChanVeseBinarize

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

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 pos_i 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 Chan–Vese segmentation of an image domain into the two segments and with contour minimizes the following functional of image :
$F(c_1,c_2,Gamma)=mu Length[Gamma]+nu Area(D)+lambda_1int_DTemplateBox[{{f, -, {c, _, 1}}}, Abs]^2dxdy+lambda_2int_(Omega\D)TemplateBox[{{f, -, {c, _, 2}}}, Abs]^2dxdy$
The 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.
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.