Active Contours without Edges Tony Chan Luminita Vese

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Active Contours without Edges Tony Chan Luminita Vese Peter Horvath – University of Szeged

Active Contours without Edges Tony Chan Luminita Vese Peter Horvath – University of Szeged 29/09/2006

Introduction • Variational approach – The main problem is to minimise an integral functional

Introduction • Variational approach – The main problem is to minimise an integral functional (e. g. ): – In the case f: , f’=0 gives the extremum(s) – In the case of functionals similary F’=0, where F’=( F/ u) is the first variation. – Most of the cases the solution is analyticly hard, in these cases we use gradient descent to optimise.

Introduction • Active Contour (Snake Model) – Kass, Witkin and Terzopoulos [Kas 88] –

Introduction • Active Contour (Snake Model) – Kass, Witkin and Terzopoulos [Kas 88] – – - tension - rigidity Eext – external energy Problem is: infx. E + Fast evaluation - But difficult to handle topological changes

Introduction • A typical external energy coming from the image: – Positive on homogeneous

Introduction • A typical external energy coming from the image: – Positive on homogeneous regions – Near zero on the sharp edges

Intoduction • Level Set methods – S. Osher and J. Sethian [Set 89] –

Intoduction • Level Set methods – S. Osher and J. Sethian [Set 89] – Embed the contour into a higher dimensional space +Automatically handles the topological changes - Slower evaluation • (. , t) level set function • Implicit contour ( =0) • The contour is evolved implicitly by moving the surface

Introduction • The curve is moving with an F speed: • The geometric active

Introduction • The curve is moving with an F speed: • The geometric active contour, based on a mean curvature (length) motion:

Chan and Vese model position Important to distinguish the model and the representation •

Chan and Vese model position Important to distinguish the model and the representation • Model: describing problems from the real world with equations Energy functionals for image segmentation Chan and Vese model • Representation: type of the description • Optimization: solving the equations Representation/optimization Contour based gradient descent Level set based gradient descent

Chan and Vese model • The model is based on trying to separate the

Chan and Vese model • The model is based on trying to separate the image into regions based on intensities • The minimization problem:

Chan and Vese model • c 1 and c 2 are the average intensity

Chan and Vese model • c 1 and c 2 are the average intensity levels inside and outside of the contour • Experiments:

Relation with the Mumford. Shah functional • The Chan and Vese model is a

Relation with the Mumford. Shah functional • The Chan and Vese model is a special case of the Mumford Shah model (minimal partition problem) – =0 and 1= 2= – u=average(u 0 in/out) – C is the CV active contour • “Cartoon” model

Level set formulation • Considering the disadvantages of the active contour representation the model

Level set formulation • Considering the disadvantages of the active contour representation the model is solved using level set formulation • level set form -> no explicit contour

Replacing C with Φ • Introducing the Heaviside (sign) and Dirac (PSF) functions

Replacing C with Φ • Introducing the Heaviside (sign) and Dirac (PSF) functions

Replacing C with Φ • The intensity terms

Replacing C with Φ • The intensity terms

Average intensities • We can calculate the average intensities using the step function

Average intensities • We can calculate the average intensities using the step function

Level set formulation of the model Combining the above presented energy terms we can

Level set formulation of the model Combining the above presented energy terms we can write the Chan and Vese functional as a function of Φ. Minimization F wrt. Φ -> gradient descent The corresponding Euler-Lagrange equation:

Approximation of the Curvature

Approximation of the Curvature

The algorithm • Initialization n=0 • repeat – n++ – Computing c 1 and

The algorithm • Initialization n=0 • repeat – n++ – Computing c 1 and c 2 – Evolving the level-set function • until the solution is stationary, or n>nmax

Initialization • We set the values of the level set function – outside =

Initialization • We set the values of the level set function – outside = -1 – inside = 1 • Any shape can be the initialization shape init() for all (x, y) in Phi if (x, y) is inside Phi(x, y)=1; else Phi(x, y)=-1; fi; end for

Computing c 1 and c 2 • The mean intensity of the image pixels

Computing c 1 and c 2 • The mean intensity of the image pixels inside and outside colors() out = find(Phi < 0); in = find(Phi > 0); c 1 = sum(Img(in)) / size(in); c 2 = sum(Img(out)) / size(out);

Finite differences for all (x, y) fx(x, y) = (Phi(x+1, y)-Phi(x-1, y))/(2*delta_s); fy(x, y)

Finite differences for all (x, y) fx(x, y) = (Phi(x+1, y)-Phi(x-1, y))/(2*delta_s); fy(x, y) =… fxx(x, y) =… fyy(x, y) =… fxy(x, y) =… delta_s recommended between 0. 1 and 1. 0

Curvature grad = (fx. ^2. +fy. ^2); curvature = (fx. ^2. *fyy + fy.

Curvature grad = (fx. ^2. +fy. ^2); curvature = (fx. ^2. *fyy + fy. ^2. *fxx 2. *fx. *fy. *fxy). / (grad. ^1. 5); Be careful! Grad can be 0!

Force gradient_m = (fx. ^2. +fy. ^2). ^0. 5; force = mu * curvature.

Force gradient_m = (fx. ^2. +fy. ^2). ^0. 5; force = mu * curvature. * gradient_m - nu – lambda 1 * (image - c 1). ^2 + lambda 2 * (image - c 2). ^2; We should normalize the force. abs(force) <= 1! Main step: Phi=Phi+delta. T*force; delta. T is recommended between 0. 01 and 0. 9. Be careful delta. T<1!

Narrow band It is useful to compute the level set function not on the

Narrow band It is useful to compute the level set function not on the whole image domain but in a narrow band near to the contour. Abs( )<d Decreasing the computational complexity.

Narrow band • Initialization n=0 • repeat – – n++ Determination of the narrow

Narrow band • Initialization n=0 • repeat – – n++ Determination of the narrow band Computing c 1 and c 2 Evolving the level-set function on the narrow band – Re-initialization • until the solution is stationary, or n>nmax

Re-initialization • Optional step H is a normalizing term recommended between 0. 1 and

Re-initialization • Optional step H is a normalizing term recommended between 0. 1 and 2. delta. T time step see above!

Stop criteria • Stop the iterations if: – The maximum iteration number were reached

Stop criteria • Stop the iterations if: – The maximum iteration number were reached – Stationary solution: • The energy is not changing • The contour is not moving • …

Demonstration of the program • Thanks for your attention

Demonstration of the program • Thanks for your attention

MATLAB tutorial • • Imread, imwrite. *, . ^, . / Find Size, length,

MATLAB tutorial • • Imread, imwrite. *, . ^, . / Find Size, length, max, min, mod, sum, zeros, ones • A(x: y, z: v) • Visualization: figure, plot, surf, imagesc