Deformable Segmentation Algorithm via Distributed Discriminative Dictionary and Ensemble Learning.

Input: Testing image ${\mathit{A}}_0$

Output: Segmented binary image ${\mathit{B}}_0$

Initialization: t = 0

Estimate the initial shape parameter $({\mathit{\alpha }}_{\mathrm{s}}^0,{\psi }^0,{\mathit{e}}^0)$ and obtain the initial deformable surface ${\mathit{v}}^0$ by solving sparse learning problem in Eq. (13) with β1 = 5 and β2 = 1.

while 1 ⩽ t ⩽ T and $\Vert {\mathit{v}}^t-{\mathit{v}}^{t-1}{\Vert }_2^2>0.001$ do

“M” Step:

Evolve the deformable model ${\mathit{v}}^t$ by minimizing the external energyfunction Eext [Eq. (11)] and the smoothness internal energy functionEsmooth [Eq. (15)];

“E” Step:

a. Estimate the parameters $\left({\mathit{\alpha }}_{\mathrm{s}}^t,{\psi }^t\right)$ for the shape refinement bysolving optimization problem [Eq. (13)] with β1 = 5 and β2 = 0;

b. Refine the deformed shape ${\mathit{v}}^t$ by minimizing the shape internalenergy function [Eq. (14)] based on the computed parameters$\left({\alpha }_{\mathrm{s}}^t,{\psi }^t\right)$.

t = t + 1

end while

Convert the output shape ${\mathit{v}}^T$ to a binary image ${\mathit{B}}_0$

Return: Segmented binary image ${\mathit{B}}_0$