Active Contours using Parameteric Curves¶

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This tour explores image segmentation using parametric active contours.

In [2]:

addpath('toolbox_signal')
addpath('toolbox_general')
addpath('toolbox_graph')
addpath('solutions/segmentation_2_snakes_param')

Parameteric Curves¶

In this tours, the active contours are represented using parametric curve $ \ga : [0,1] \rightarrow \RR^2 $.

This curve is discretized using a piewise linear curve with $p$ segments, and is stored as a complex vector of points in the plane $\ga \in \CC^p$.

Initial polygon.

In [3]:

gamma0 = [0.78 0.14 0.42 0.18 0.32 0.16 0.75 0.83 0.57 0.68 0.46 0.40 0.72 0.79 0.91 0.90]' + ...
     1i* [0.87 0.82 0.75 0.63 0.34 0.17 0.08 0.46 0.50 0.25 0.27 0.57 0.73 0.57 0.75 0.79]';

Number of points of the discrete curve.

In [4]:

p = 256;

Shortcut to re-sample a curve according to arc length.

In [5]:

curvabs = @(gamma)[0;cumsum( 1e-5 + abs(gamma(1:end-1)-gamma(2:end)) )];
resample1 = @(gamma,d)interp1(d/d(end),gamma,(0:p-1)'/p, 'linear');
resample = @(gamma)resample1( [gamma;gamma(1)], curvabs( [gamma;gamma(1)] ) );

Initial curve $ \ga_1(t) $.

In [6]:

gamma1 = resample(gamma0);

Display the initial curve.

In [7]:

clf;
h = plot(gamma1([1:end 1]), 'k');
set(h, 'LineWidth', 2); axis('tight'); axis('off');

Shortcut for forward and backward finite differences.

In [8]:

BwdDiff = @(c)c - c([end 1:end-1]);
FwdDiff = @(c)c([2:end 1]) - c;
dotp = @(c1,c2)real(c1.*conj(c2));

The tangent to the curve is computed as $$ t_\ga(s) = \frac{\ga'(t)}{\norm{\ga'(t)}} $$ and the normal is $ n_\ga(t) = t_\ga(t)^\bot. $

Shortcut to compute the tangent and the normal to a curve.

In [9]:

normalize = @(v)v./max(abs(v),eps);
tangent = @(gamma)normalize( FwdDiff(gamma) );
normal = @(gamma)-1i*tangent(gamma);

Move the curve in the normal direction, by computing $ \ga_1(t) \pm \delta n_{\ga_1}(t) $.

In [10]:

delta = .03;
gamma2 = gamma1 + delta * normal(gamma1);
gamma3 = gamma1 - delta * normal(gamma1);

Display the curves.

In [11]:

clf;
hold on;
h = plot(gamma1([1:end 1]), 'k'); set(h, 'LineWidth', 2); 
h = plot(gamma2([1:end 1]), 'r--'); set(h, 'LineWidth', 2); 
h = plot(gamma3([1:end 1]), 'b--'); set(h, 'LineWidth', 2); 
axis('tight'); axis('off');

Evolution by Mean Curvature¶

A curve evolution is a series of curves $ s \mapsto \ga_s $ indexed by an evolution parameter $s \geq 0$. The intial curve $\ga_0$ for $s=0$ is evolved, usually by minizing some energy $E(\ga)$ in a gradient descent $$ \frac{\partial \ga_s}{\partial s} = \nabla E(\ga_s). $$

Note that the gradient of an energy is defined with respect to the curve-dependent inner product $$ \dotp{a}{b} = \int_0^1 \dotp{a(t)}{b(t)} \norm{\ga'(t)} d t. $$ The set of curves can thus be thought as being a Riemannian surface.

The simplest evolution is the mean curvature evolution. It corresponds to minimization of the curve length $$ E(\ga) = \int_0^1 \norm{\ga'(t)} d t $$

The gradient of the length is $$ \nabla E(\ga)(t) = -\kappa_\ga(t) n_\ga(t) $$ where $ \kappa_\ga $ is the curvature, defined as $$ \kappa_\ga(t) = \frac{1}{\norm{\ga'(t)}} \dotp{ t_\ga'(t) }{ n_\ga(t) } . $$

Shortcut for normal times curvature $ \kappa_\ga(t) n_\ga(t) $.

In [12]:

normalC = @(gamma)BwdDiff(tangent(gamma)) ./ abs( FwdDiff(gamma) );

Time step for the evolution. It should be very small because we use an explicit time stepping and the curve has strong curvature.

In [13]:

dt = 0.001 / 100;

Number of iterations.

In [14]:

Tmax = 3 / 100;
niter = round(Tmax/dt);

Initialize the curve for $s=0$.

In [15]:

gamma = gamma1;

Evolution of the curve.

In [16]:

gamma = gamma + dt * normalC(gamma);

To stabilize the evolution, it is important to re-sample the curve so that it is unit-speed parametrized. You do not need to do it every time step though (to speed up).

In [17]:

gamma = resample(gamma);

Exercise 1

Perform the curve evolution. You need to resample it a few times.

In [18]:

exo1()

In [19]:

%% Insert your code here.

Geodesic Active Contours¶

Geodesic active contours minimize a weighted length $$ E(\ga) = \int_0^1 W(\ga(t)) \norm{\ga'(t)} d t, $$ where $W(x)>0$ is the geodesic metric, that should be small in areas where the image should be segmented.

Create a synthetic weight $W(x)$.

In [20]:

n = 200;
nbumps = 40;
theta = rand(nbumps,1)*2*pi;
r = .6*n/2; a = [.62*n .6*n];
x = round( a(1) + r*cos(theta) );
y = round( a(2) + r*sin(theta) );
W = zeros(n); W( x + (y-1)*n ) = 1;
W = perform_blurring(W,10);
W = rescale( -min(W,.05), .3,1);

Display the metric.

In [21]:

clf;
imageplot(W);

Pre-compute the gradient $\nabla W(x)$ of the metric.

In [22]:

options.order = 2;
G = grad(W, options);
G = G(:,:,1) + 1i*G(:,:,2);

Shortcut to evaluate the gradient and the potential along a curve.

In [23]:

EvalG = @(gamma)interp2(1:n,1:n, G, imag(gamma), real(gamma));
EvalW = @(gamma)interp2(1:n,1:n, W, imag(gamma), real(gamma));

Create a circular curve $\ga_0$.

In [24]:

r = .98*n/2;
p = 128; % number of points on the curve
theta = linspace(0,2*pi,p+1)'; theta(end) = [];
gamma0 = n/2*(1+1i) +  r*(cos(theta) + 1i*sin(theta));

Initialize the curve at time $t=0$ with a circle.

In [25]:

gamma = gamma0;

For this experiment, the time step should be larger, because the curve is in $[1,n] \times [1,n]$.

In [26]:

dt = 1;

Number of iterations.

In [27]:

Tmax = 5000;
niter = round(Tmax/dt);

Display the curve on the back ground;

In [28]:

lw = 2;
clf; hold on;
imageplot(W);
h = plot(imag(gamma([1:end 1])),real(gamma([1:end 1])), 'r');
set(h, 'LineWidth', lw);
axis('ij');

The gradient of the energy is $$ \nabla E(\ga) = -W(\ga(t)) \kappa_\ga(t) n_\ga(t) + \dotp{\nabla W(\ga(t))}{ n_\ga(t) } n_\ga(t). $$

Evolution of the curve according to this gradient.

In [29]:

N = normal(gamma);
g = - EvalW(gamma).*normalC(gamma) + dotp(EvalG(gamma), N) .* N;
gamma = gamma - dt*g;

To avoid the curve from being poorly sampled, it is important to re-sample it evenly.

In [30]:

gamma = resample( gamma );

Exercise 2

Perform the curve evolution.

In [31]:

exo2()

In [32]:

%% Insert your code here.

Medical Image Segmentation¶

One can use a gradient-based metric to perform edge detection in medical images.

Load an image $f$.

In [33]:

n = 256;
f = rescale( sum(load_image('cortex', n), 3 ) );

Display.

In [34]:

clf;
imageplot(f);

An edge detector metric can be defined as a decreasing function of the gradient magnitude. $$ W(x) = \psi( d \star h_a(x) ) \qwhereq d(x) = \norm{\nabla f(x)}. $$ where $h_a$ is a blurring kernel of width $a>0$.

Compute the magnitude of the gradient.

In [35]:

options.order = 2;
G = grad(f,options);
d = sqrt(sum(G.^2,3));

Blur it by $h_a$.

In [36]:

a = 3;
d = perform_blurring(d,a);

Compute a decreasing function of the gradient to define $W$.

In [37]:

d = min(d,.4);
W = rescale(-d,.8,1);

Display it.

In [38]:

clf;
imageplot(W);

Number of points.

In [39]:

p = 128;

Exercise 3

Create an initial circle $\gamma_0$ of $p$ points.

In [40]:

exo3()

In [41]:

%% Insert your code here.

Step size.

In [42]:

dt = 2;

Number of iterations.

In [43]:

Tmax = 9000;
niter = round(Tmax/dt);

Exercise 4

Perform the curve evolution.

In [44]:

exo4()