You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: cat_main_LAS1585.m
    path of file: ./repos/ExploreASL/External/SPMmodified/toolbox/cat12/cat_run1585
    
    the code of the file until where you have to start completion: function [Yml,Ymg,Ycls,Ycls2,T3th] = cat_main_LAS1585(Ysrc,Ycls,Ym,Yb0,Yy,T3th,res,vx_vol,extopts,Tth) 
% This is an exclusive subfunction of cat_main.
% ______________________________________________________________________
%

    
function [Yml,Ymg,Ycls,Ycls2,T3th] = cat_main_LAS1585(Ysrc,Ycls,Ym,Yb0,Yy,T3th,res,vx_vol,extopts,Tth) 
% This is an exclusive subfunction of cat_main.
% ______________________________________________________________________
%
% Local Adaptive Segmentation (LAS):
%
% This version of the local adaptive intensity correction includes a  
% bias correction that based on a maximum filter for the WM and a mean
% filter of the GM to stabilize the correction in region with less WM.
%
% The extension based mostly on the assumption that the tissue next to 
% the CSF (and high divergence sulci) has to be WM (maximum, high 
% divergence) or GM. For each tissue a refined logical map is generated 
% and used to estimate the local intensity threshold.
%
% It is important to avoid high intensity blood vessels in the process, 
% because they will push down local WM and GM intensity. 
%
% There are further regionwise correction, e.g. , to avoid overfitting in 
% cerebellum, or adapt for age specific changes, e.g. enlarged ventricle.
%
% Based on this values a intensity transformation is used. Compared to 
% the global correciton this has to be done for each voxel. To save time
% only a rough linear transformation is used.
% ______________________________________________________________________
%
%   [Yml,Ycls,Ycls2,T3th] = ...
%     cat_main_LAS1585(Ysrc,Ycls,Ym,Yb0,Yy,T3th,res,vx_vol,PA,template)
%
%   Yml    .. local intensity correct image
%   Ycls   .. corrected SPM tissue class map
%   Ycls2  .. ?
%   T3th   .. tissue thresholds of CSF, GM, and WM in Ysrc
%
%   Ysrc   .. (bias corrected) T1 image
%   Ym     .. intensity corrected T1 image (BG=0,CSF=1/3,GM=2/3,WM=1)
%   Ycls   .. SPM tissue class map
%   Yb0    .. brain mask
%   Yy     .. deformation map
%   res    .. SPM segmentation structure
%   vx_vol .. voxel dimensions
%   PA     .. CAT atlas map
%   template .. ?
% ______________________________________________________________________
% 
% internal maps:
%
%   Yg   .. gradient map   - edges between tissues
%   Ydiv .. divergence map - sulci, gyris pattern, and blood vessels
%   Yp0  .. label map      - tissue classes (BG=0,CSF=1,GM=2,WM=3) 
%
%   Ysw  .. save WM tissue map
%   Ybv  .. blood vessel map
%   Ycp  .. CSF / background area for distances estimation
%   Ycd  .. CSF / background distance
%   Ycm  .. CSF
%   Ygm  .. GM
%   Ywm  .. WM 
%   Yvt  .. WM next to the ventricle map 
%   Ygmt .. cortical thickness map
%   Ypp  .. cortical percentage position map
% ______________________________________________________________________
%
%   Robert Dahnke (robert.dahnke@uni-jena.de)
%   Structural Brain Mapping Group (http://dbm.neuro.uni-jena.de/)
%   Department of Neurology
%   University Jena
% ______________________________________________________________________
% $Id: cat_main_LAS.m 1036 2016-10-18 14:26:32Z dahnke $

  % set this variable to 1 for simpler debuging without reduceBrain
  % function (that normally save half of processing time)
  debug   = extopts.debug; % debug = 1;
  verb    = extopts.verb-1;
  vxv     = 1/ mean(vx_vol);
  dsize   = size(Ysrc);
  NS      = @(Ys,s) Ys==s | Ys==s+1;             % function to ignore brain hemisphere coding
  LASstr  = max(eps,min(1,extopts.LASstr));      % LAS strenght (for GM/WM threshold)3
  LAB     = extopts.LAB;                         % atlas labels
  cleanupstr  = min(1,max(0,extopts.gcutstr));   % required to avoid critical regions
  cleanupdist = min(3,max(1,1 + 2*cleanupstr));
    
  
  
%% ---------------------------------------------------------------------
%  First, we have to optimize the segments using further information that 
%  SPM do not use, such as the gradient, divergence and distance maps. 
%  The gradient map (average of the first derivate of the T1 map) is an 
%  edge map and independ