which technique used in the preprocessing ?

main.m file
clc;
clear all;
close all;

while(1)
 ch = menu('BRAIN TUMOUR SEGMENTATION','Input Image','Preprocessing','Feature Extraction','Segmentation','Exit');
 if(ch==1)
        [I,path]=uigetfile('*.jpg','select a input image');
        str=strcat(path,I);
        s=imread(str);   
        figure,imshow(s),title('Input image');
        [m1 m2 m3]=size(s);
 end

   if(ch==2)
      num_iter = 10;
      delta_t = 1/7;
      kappa = 15;
      option = 1;
      ede=im2bw(s);
      imie=edge(ede,'canny');
      disp('Preprocessing image please wait . . .');
      ad = process(s,num_iter,delta_t,kappa,option);  
      adj = uint8(ad);
      figure, 
      subplot 121, imshow(s,[]),title('Input image');
      subplot 122, imshow(adj,[]),title('Filtered image');
   end

process.m file
function diff_im = process(im, num_iter, delta_t, kappa, option)
persistent cN cS cW cE cNE cSE cNW cSW;
cN = double(0);
cS = double(0);
cW = double(0);
cE = double(0); 
cNE = double(0);
cSE = double(0);
cNW = double(0);
cSW = double(0);

%UNTITLED2 Summary of this function goes here
%   Detailed explanation goes here
fprintf('Removing noise\n');
fprintf('Filtering Completed !!\n'); 
% Convert input image to double.
im = double(im);

% PDE (partial differential equation) initial condition.
diff_im = im;

% Center pixel distances.
dx = 1;
dy = 1;
dd = sqrt(2);

% 2D convolution masks - finite differences.
hN = [0 1 0; 0 -1 0; 0 0 0];
hS = [0 0 0; 0 -1 0; 0 1 0];
hE = [0 0 0; 0 -1 1; 0 0 0];
hW = [0 0 0; 1 -1 0; 0 0 0];
hNE = [0 0 1; 0 -1 0; 0 0 0];
hSE = [0 0 0; 0 -1 0; 0 0 1];
hSW = [0 0 0; 0 -1 0; 1 0 0];
hNW = [1 0 0; 0 -1 0; 0 0 0];

for t = 1:num_iter

          % Finite differences. [imfilter(.,.,'conv') can be replaced by conv2(.,.,'same')]
          nablaN = imfilter(diff_im,hN,'conv');
          nablaS = imfilter(diff_im,hS,'conv');   
          nablaW = imfilter(diff_im,hW,'conv');
          nablaE = imfilter(diff_im,hE,'conv');   
          nablaNE = imfilter(diff_im,hNE,'conv');
          nablaSE = imfilter(diff_im,hSE,'conv');   
          nablaSW = imfilter(diff_im,hSW,'conv');
          nablaNW = imfilter(diff_im,hNW,'conv'); 

          % Diffusion function.
          if option == 1
              cN = exp(-(nablaN/kappa).^2);
              cS = exp(-(nablaS/kappa).^2);

              cW = exp(-(nablaW/kappa).^2);
              cE = exp(-(nablaE/kappa).^2);
              cNE = exp(-(nablaNE/kappa).^2);
              cSE = exp(-(nablaSE/kappa).^2);
              cSW = exp(-(nablaSW/kappa).^2);
              cNW = exp(-(nablaNW/kappa).^2);
          elseif option == 2
              cN = 1./(1 + (nablaN/kappa).^2);
              cS = 1./(1 + (nablaS/kappa).^2);
              cW = 1./(1 + (nablaW/kappa).^2);
              cE = 1./(1 + (nablaE/kappa).^2);
              cNE = 1./(1 + (nablaNE/kappa).^2);
              cSE = 1./(1 + (nablaSE/kappa).^2);
              cSW = 1./(1 + (nablaSW/kappa).^2);
              cNW = 1./(1 + (nablaNW/kappa).^2);
          end

          % Discrete PDE solution.
          diff_im = diff_im + delta_t*((1/(dy^2))*cN.*nablaN + (1/(dy^2))*cS.*nablaS +(1/(dx^2))*cW.*nablaW +(1/(dx^2))*cE.*nablaE +(1/(dd^2))*cNE.*nablaNE + (1/(dd^2))*cSE.*nablaSE +(1/(dd^2))*cSW.*nablaSW + (1/(dd^2))*cNW.*nablaNW );

        end

end