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_run_job1585.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 cat_run_job1585(job,tpm,subj)
% run CAT 
% ______________________________________________________________________
%
% Initialization functions for the CAT preprocessing
%  * creation of the subfolder structure (if active)
%  * check of image resolution (avoid scans with very low resolution)
%  * interpolation 
%  * affine preprocessing (APP)
%    >> cat_run_job_APP_init
%    >> cat_run_job_APP_final
%  * affine registration
%  * initial SPM preprocessing
%
%   cat_run_job1585(job,tpm,subj)
% 
%   job  .. SPM job structure with main parameter
%   tpm  .. tissue probability map (hdr structure)
%   subj .. file name
% ______________________________________________________________________
% Christian Gaser
% $Id: cat_run_job.m 1577 2020-03-09 17:36:03Z dahnke $

%#ok<*WNOFF,*WNON>

    % if there is a breakpoint in this file set debug=1 and do not clear temporary variables 
    dbs   = dbstatus; debug = 0; for dbsi=1:numel(dbs), if strcmp(dbs(dbsi).name,mfilename); debug = 1; break; end; end
    
    clearvars -global cat_err_res;
    global cat_err_res; % for CAT error report
    cat_err_res.stime = clock; 
    
    stime  = clock; 
    stime0 = stime; % overall processing time

    % create subfolders if not
    
function [Ym,Yt,Ybg,WMth] = APPmini(obj,VF)
%% very simple affine preprocessing (APP)
%  ------------------------------------------------------------------------
%  Creates an intensity normalized image Ym by the average higher tissue
%  intensity WMth estimated in the mask Yt. Moreover, it estimates the
%  background region Ybg. 
%
%  [Ym,Yt,Ybg,WMth] = APPmini(obj,VF)
%  ------------------------------------------------------------------------
%  Robert Dahnke 2019/01

  Ysrc = single(obj.image.private.dat(:,:,:)); 

  % remove outlier and use SPM for intensity normalization to uint8 
  % empirical division by 200 to get WM values around 1.0
  Ysrc = cat_stat_histth(Ysrc,99.9);
  VF0  = cat_spm_smoothto8bit(VF,0.1); 
  Ym   = single(VF0.dat)/200; clear VG0 
  
  % find the larges object and estimate the averag intensity
  % keep in mind that this will may inlcude the head (and in MP2RAGE/MT/R1
  % images also the background), i.e. highest intensity is may head,
  % blood vessels or WM or CSF in T1/PD
  Yt   = cat_vol_morph(Ym>cat_stat_nanmean(Ym(Ym(:)>0.1)),'l',[100 1000])>0.5;
  WMth = kmeans3D( Ysrc(Yt(:)) , 1); 
  
  % rescale Ym and roughly estimate the background (not in MP2Rage/MT/R1)
  Ym   = Ysrc ./ WMth;
  Ybg  = cat_vol_morph(Ym<0.2,'l',[100 1000])>0;
  
return

function APP_RMSE = checkAPP(Ym,Ysrc) 
%% check Ym
%  ------------------------------------------------------------------------
%  Function to compare the normalized gradient maps of two input images 
%  that should be nearly identical.
%
%  APP_RMSE = checkAPP(Ym,Ysrc) 
%  ------------------------------------------------------------------------
%  Robert Dahnke 2019/01

  % remove strongest outlier
  Ym   = cat_stat_histth(Ym,99.9);
  Ysrc = cat_stat_histth(Ysrc,99.9);

  % avoid division by zeros
  Ym   = Ym   + min(Ym(:));
  Ysrc = Ysrc + min(Ysrc(:)); 
  
  % normalized gradient maps
  Ygm = cat_vol_grad(Ym)   ./ (Ym + eps);     
  Ygs = cat_vol_grad(Ysrc) ./ (Ysrc + eps);

  % use only the central region and values in the expected tissue range
  sYm  = round(size(Ym) / 5);
  Ymsk = false(size(Ym) ); Ymsk(sYm(1):end