SUBJECT_TOOL_run_optimisation.m
74ce03b0ec87command_line

SUBJECT_TOOL_run_optimisation.m
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			function REDATA = ELLIPSOID_TOOL_run_optimisation(EOPTDATA, BLDATA)
			% Ribcage Ellipsoid Optimisation Overhead function.
			%--------------------------------------------------------------------------
			% Syntax :
			% REDATA = ELLIPSOID_TOOL_run_optimisation(EOPTDATA, BLDATA)
			%--------------------------------------------------------------------------
			%
			% EPFL - LA - LBO SHOULDER MODEL
			% Created by : David Ingram (LA - EPFL)
			%
			% File Description :
			% This function runs the optimisation procedure for the ribcage ellipsoid.
			% Once a solution is found, the ellipsoid is adapted to the points TS and
			% AI.
			%
			%--------------------------------------------------------------------------

			% Create a wait bar to help the user be patient
			ewait = waitbar(0,'Running Ellipsoid Optimisation (Status can be observed in command window)');

			% Define the Optimization Options
			options = optimset(...
			'Algorithm', EOPTDATA.Algorithm,...
			'Display', 'Iter',...
			'HessUpdate', 'bfgs',...
			'Diagnostics', 'on',...
			'Gradobj', 'off',...
			'TolX', EOPTDATA.TolX,...
			'TolFun', EOPTDATA.TolFun,...
			'TolCon', EOPTDATA.TolCon,...
			'MaxFunEval', EOPTDATA.MaxFunEval,...
			'MaxIter', EOPTDATA.MaxIter);

			% Update the wait bar
			waitbar(0.25,ewait);

			% Run the Optimization with every two points
			[xopt, fopt, exitflag] = fmincon(@ELLIPSOID_TOOL_optimisation_function,...
			EOPTDATA.Initial_Guess,...
			[],[],...
			[],[],...
			EOPTDATA.Lower_Bounds, EOPTDATA.Upper_Bounds,...
			[],...
			options,...
			EOPTDATA.points(1:2:end,:));

			% Update the wait bar
			waitbar(0.85,ewait);

			% Optimisation has been run, the ribcage datastructure is to be updated

			% Create the initial data
			REDATA.Centre = xopt(1,1:3);
			REDATA.Axes = xopt(1,4:6);

			% Position of TS & AI in thorax reference system
			TS = BLDATA.Initial_Points.TS;
			AI = BLDATA.Initial_Points.AI;

			%--------------------------------------------------------------------------
			% The Scaling Factor is one of the roots of a sixth order polyomial. The
			% polynomial is obtained from the equation and must be a positive real
			% root.
			% (TSx - Ex)^2 (TSy - Ey)^2 (TSz - Ez)^2
			% ------------ + ------------ + ------------ - 1 = 0
			% (A + sf)^2 (B + sf)^2 (C + sf)^2
			%
			% Required Data
			TSx = TS(1); TSy = TS(2); TSz = TS(3);
			A = REDATA.Axes(1); B = REDATA.Axes(2); C = REDATA.Axes(3);
			Xo = REDATA.Centre(1); Yo = REDATA.Centre(2); Zo = REDATA.Centre(3);

			% The Polynomial Coefficients are given by
			P6 = - 1;
			P5 = - 2*(A + B + C);
			P4 = ((TSx - Xo)^2 + (TSy - Yo)^2 + (TSz - Zo)^2 - (C^2 + 4CB + B^2) - 4(C + B)A - A^2);
			P3 = (2(TSx - Xo)^2(C + B) + 2(TSy - Yo)^2(A+C) + 2(TSz - Zo)^2(A + B) - 2(C^2B + B^2C) - 2(C^2 + 4CB + B^2)A - 2(C + B)*A^2);
			P2 = ((TSx - Xo)^2(B^2 + C^2 + 4CB) + (TSy - Yo)^2(C^2 + 4AC + A^2) + (TSz - Zo)^2(A^2 + 4AB + B^2) - C^2B^2 - 4(C^2B + CB^2)A - (C^2 + 4CB + B^2)*A^2);
			P1 = (2(TSx - Xo)^2(B^2C + C^2B) + 2(TSy - Yo)^2(A^2C + C^2A) + 2(TSz - Zo)^2(A^2B + B^2A) -2C^2B^2A - 2(C^2B + CB^2)*A^2);
			P0 = (TSx - Xo)^2C^2B^2 + (TSy - Yo)^2A^2C^2 + (TSz - Zo)^2A^2B^2 - A^2B^2C^2;

			% Get the Roots
			TS_Scale_Roots = roots([P6, P5, P4, P3, P2, P1, P0]);

			% Extract the Positive Root only
			idx = find(abs(imag(TS_Scale_Roots)) < 1.e-6);
			idxx = find(real(TS_Scale_Roots(idx)) > 0);
			TSsf = TS_Scale_Roots(idx(idxx),1);

			% Save The Data
			REDATA.TSscale = TSsf;
			REDATA.TSaxes = [A + TSsf; B + TSsf; C + TSsf];

			%--------------------------------------------------------------------------
			% The Scaling Factor is one of the roots of a sixth order polyomial. The
			% polynomial is obtained from the equation and must be a positive real
			% root.
			% (AIx - Ex)^2 (AIy - Ey)^2 (AIz - Ez)^2
			% ------------ + ------------ + ------------ - 1 = 0
			% (A + sf)^2 (B + sf)^2 (C + sf)^2
			%
			% Required Data
			AIx = AI(1); AIy = AI(2); AIz = AI(3);

			% The Polynomial Coefficients are given by
			P6 = - 1;
			P5 = - 2*(A + B + C);
			P4 = ((AIx - Xo)^2 + (AIy - Yo)^2 + (AIz - Zo)^2 - (C^2 + 4CB + B^2) - 4(C + B)A - A^2);
			P3 = (2(AIx - Xo)^2(C + B) + 2(AIy - Yo)^2(A+C) + 2(AIz - Zo)^2(A + B) - 2(C^2B + B^2C) - 2(C^2 + 4CB + B^2)A - 2(C + B)*A^2);
			P2 = ((AIx - Xo)^2(B^2 + C^2 + 4CB) + (AIy - Yo)^2(C^2 + 4AC + A^2) + (AIz - Zo)^2(A^2 + 4AB + B^2) - C^2B^2 - 4(C^2B + CB^2)A - (C^2 + 4CB + B^2)*A^2);
			P1 = (2(AIx - Xo)^2(B^2C + C^2B) + 2(AIy - Yo)^2(A^2C + C^2A) + 2(AIz - Zo)^2(A^2B + B^2A) -2C^2B^2A - 2(C^2B + CB^2)*A^2);
			P0 = (AIx - Xo)^2C^2B^2 + (AIy - Yo)^2A^2C^2 + (AIz - Zo)^2A^2B^2 - A^2B^2C^2;

			% Get the Roots
			AI_Scale_Roots = roots([P6, P5, P4, P3, P2, P1, P0]);

			% Extract the Positive Root only
			idx = find(abs(imag(AI_Scale_Roots)) < 1.e-6);
			idxx = find(real(AI_Scale_Roots(idx)) > 0);
			AIsf = AI_Scale_Roots(idx(idxx),1);

			% Save The Data
			REDATA.AIscale = AIsf;
			REDATA.AIaxes = [A + AIsf; B + AIsf; C + AIsf];

			% Update the wait bar and close it
			waitbar(1, ewait);
			delete(ewait);

			% Display a warning if the optimisation procedure was not successful!
			if exitflag < 1
			warndlg('The Optimisation was Unsuccessful. See MATLAB command line for details');
			end
			return;

msm/TOOLBOX_Subject_Specific/SUBJECT_TOOL_run_optimisation.m74ce03b0ec87command_line

SUBJECT_TOOL_run_optimisation.mView Options

msm/TOOLBOX_Subject_Specific/SUBJECT_TOOL_run_optimisation.m
74ce03b0ec87command_line

SUBJECT_TOOL_run_optimisation.m
View Options