How can I write the first function as a part of the second?

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I have been given a peculiar task by my instructor. There are two K means code, I need to write a K means function which captures the functionality of both. The first was used for image segmentation and the second to K means cluster data points. I understand the algo in both but I dont understand how to use the X in function 1 by using the 2nd function.

Please help. New to MATLAB.

% function 1
function X = my_kmeans(F,K,iter)
 CENTS = F( ceil(rand(K,1)*size(F,1)) ,:);             
  DAL   = zeros(size(F,1),K+2);                         
  for n = 1:iter
     for i = 1:size(F,1)
      for j = 1:K  
          DAL(i,j) = norm(F(i,:) - CENTS(j,:));      
       end
       [Distance, CN] = min(DAL(i,1:K));               % 1:K are Distance from Cluster Centers 1:K
      DAL(i,K+1) = CN;                                % K+1 is Cluster Label
       DAL(i,K+2) = Distance;                          % K+2 is Minimum Distance
    end
     for i = 1:K
       A = (DAL(:,K+1) == i);                          % Cluster K Points
       CENTS(i,:) = mean(F(A,:));                      % New Cluster Centers
       if sum(isnan(CENTS(:))) ~= 0                    % If CENTS(i,:) Is Nan Then Replace It With Random Point
           NC = find(isnan(CENTS(:,1)));           % Find Nan Centers
           for Ind = 1:size(NC,1)
          CENTS(NC(Ind),:) = F(randi(size(F,1)),:);
           end
       end
     end
  end
   X = zeros(size(F));
  for i = 1:K
 idx = find(DAL(:,K+1) == i);
X(idx,:) = repmat(CENTS(i,:),size(idx,1),1); 
  end
end
% Function 2
function [means,Nmeans,membership] = my_kmeans_data(X,K,maxerr)
[Ndata, dims] = size(X);
dist = zeros(1,K);
means = zeros(K,dims);
if (nargout > 2)
    membership = zeros(Ndata,1);
end
% Initial prototype assignment (arbitrary)
for i=1:K-1
   means(i,:) = X(i,:);
end
means(K,:) = mean(X(K:Ndata,:));
cmp = 1 + maxerr;
while (cmp > maxerr)
   % Sums (class) and data counters (Nclass) initialization
   class = zeros(K,dims);
   Nclass = zeros(K,1);
   % Groups each elements to the nearest prototype
   for i=1:Ndata
      for j=1:K
         % Euclidean distance from data to each prototype
         dist(j) = norm(X(i,:)-means(j,:))^2;
      end
      % Find indices of minimum distance
      index_min = find(~(dist-min(dist)));
      % If there are multiple min distances, decide randomly
      index_min = index_min(ceil(length(index_min)*rand));
      if (nargout > 2)
          membership(i) = index_min;
      end
      class(index_min,:) = class(index_min,:) + X(i,:);
      Nclass(index_min) = Nclass(index_min) + 1;
   end
   for i=1:K
      class(i,:) = class(i,:) / Nclass(i);
   end
   % Compare results with previous iteration
   cmp = 0;
   for i=1:K
      cmp = norm(class(i,:)-means(i,:)); 
   end
   % Prototype update
   means = class;
end
Nmeans = Nclass;
end