How can I Resolve this error?

%function [inpput_image,nroi_image,roi_image,fuse_image,dc_roi,dc_nroi,D]=fc(filename,p)
%%
% close all figure windows, plots
 close all;
% clear all variables in the current workspace
 clear all;
% reads image data from the compliant DICOM (4)
p=1
filename='i4.dcm';
if(p==1)
    im = dicomread(filename);
    
    % normalize the dicom image to 0-255 range for fast processing
    max_val = max(max(im));
    z = max_val/255;
    im = im./z;
else
%     read jpeg image
    im=imread(filename);
    I=im;
    im=rgb2gray(im);
    im=imresize(im,[512 512]);
end

inpput_image=im;
org_img=im;

I1=im;
im=im2double(im);
 subplot(2,2,1);
% % % %the second parameter controls the display range of a grayscale image
imshow(im,[]), title('input image');
j=im;
%%
%mask size 5 x 5
N = 5;
%to retain original size of image even after filtering
im_pad = padarray(j,[floor(N/2) floor(N/2)]);
%sort elements in ascending or descending order to get median
%convert each sliding NxN block of A into a column of B, with no zero padding
%NOTE im_col is not a column matrix it is a rearranged matrix with 2D as in im_pad
im_col = im2col(im_pad,[N N],'sliding');
%sorts each column of im_pad in ascending order still retains 2D
sorted_cols = sort(im_col,1);
%finds median and returns a row martix containing medians for all columns of the input
%B(x,y) returns the element in B at location (x,y); if y is not specified
%it returns the element at position x in column 1; : implies all columns
med_vector = sorted_cols(floor(N*N/2)+1,:);
%convert result to matrix form to get the MEDIAN FILTERED image
out1 = col2im(med_vector,[N N],size(im_pad),'sliding');
 subplot(2,2,2);
 imshow(out1,[]), title('median filtered image');
 %%
% Generate Gaussian mask/kernel
sigma = 2; % Define sigma here; small detects fine features
% one method to create a matrix; ind is a row matrix [-2 -1 0 1 2]
ind = -floor(N/2) : floor(N/2);
% ind=exp(ind);
% create X by repeating ind rowwise and Y by repeating ind columnwise
[X Y] = meshgrid(ind, ind);
% implement Gaussian HP filter formula; h is 5x5
h = exp(-(X.^2 + Y.^2) / (2*sigma*sigma));
% GHPF=1-GLPF
h=1-h;

% Convert filter into a column vector
h = h(:);

% Filter our image
I=out1;
% this conversion is needed because inbuilt command requires compatibility with its arguement; 
% bsxfun requires both C and h to be of the same datatype double
% (convert to higher datatype to prevent data loss) 
I = im2double(I);
I_pad = padarray(I, [floor(N/2) floor(N/2)]);
C = im2col(I_pad, [N N], 'sliding');
% Binary Singleton Expansion Function C = bsxfun(FUNC,A,B) applies the 
% element-by-element binary operation specified by the function to A and B
C_filter = sum(bsxfun(@times, C, h), 1);
% rearranges the row vector B into a matrix of size (MM-M+1)-by-(NN-N+1).
% B must be a vector of size 1-by-(MM-M+1)*(NN-N+1).
out2 = col2im(C_filter, [N N], size(I_pad), 'sliding');
 %subplot(2,2,3);
 %imshow(out2,[]), title('Gaussian filtered image');
  %%
 workspace;  % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 24;

%im = dicomread('i9.dcm');
 % normalize the dicom image to 0-255 range for fast processing
    max_val = max(max(im));
    z = max_val/255;
    im = im./z;
% Get the dimensions of the image.
% numberOfColorBands should be = 1.
[rows, columns, numberOfColorBands] = size(im);

% Display the original gray scale image.
figure(5)
subplot(2, 3, 1);
imshow(im,[]);

title('Original Grayscale Image');
% Crop image to get rid of light box surrounding the image
im = im(3:end-3, 4:end-4);
% Threshold to create a binary image
binaryImage = im > 20;
% Get rid of small specks of noise
binaryImage = bwareaopen(binaryImage, 10);
% Display the original gray scale image.
subplot(2, 3, 3);
imshow(binaryImage,[]);

title('Binary Image');
% Seal off the bottom of the head - make the last row white.
binaryImage(end,:) = true;
% Fill the image
binaryImage = imfill(binaryImage, 'holes');
subplot(2, 3, 4);
imshow(binaryImage,[]);

title('Cleaned Binary Image');
% Erode away 15 layers of pixels.
se = strel('disk', 15, 0);
binaryImage = imerode(binaryImage, se);
subplot(2, 3, 5);
imshow(binaryImage, []);

title('Eroded Binary Image');
% Mask the gray image
finalImage = im; % Initialize.
finalImage(~binaryImage) = 0;
figure(1)
 subplot(2,2,3);
 
imshow(finalImage, []);

title('Skull stripped Image');
 %%
%//Fuzzy c MEANS ALGORITHM
%out2 is double with max value 0.03... out3 is double with max value 255
%so to calculate histogram more accurately in a time efficient manner we
%convert to uint8 with gray level range 0-255
  out2=finalImage;

max_val=max(max(out2));
out3=out2.*(240/max_val);
im=uint8(out3);

fim=mat2gray(im);
level=graythresh(fim);
bwfim=im2bw(fim,level);
[bwfim_0,level_0]=fcmthresh(fim,0);
[bwfim_1,level_1]=fcmthresh(fim,1);
figure(4)
subplot(2,2,1);
imshow(fim,[]);title('Original');
subplot(2,2,2);
imshow(bwfim,[]);title(sprintf('median filter,level=%f',level));
subplot(2,2,3);
imshow(bwfim0,[]);title(sprintf('FCM0,level=%f',level0));
subplot(2,2,4);
imshow(bwfim1,[]);title(sprintf('FCM output level=%f',level1));
figure(1)
subplot(2,2,4);
imshow(bwfim1,[]);title(sprintf('thresholding output'));

imtool(bwfim1,[]);
 
%%
% obtaining ROI and NROI
im_bin=im2bw(im);
[r,c]=find(im_bin==1);
rc=[r c];
simg=im;

im=im2uint8(im);
max_val = max(max(simg));
z = max_val/255;
simg = simg./z;
simg=uint8(simg);
orgimg=simg;


for j=1:(numel(rc)/2)
    simg(r(j),c(j))=0;
end
figure, subplot(2,1,1); 
 imshow(simg,[]), title('NROI-Non region of interest');
nroi_image=simg; 
logimg=imsubtract(orgimg,simg);
roi_image=logimg;
 subplot(2,1,2);
 imshow(logimg,[]), title('ROI-region of interest');

%%
%area calculation
[h,v]=size(im_bin);
tumour_area=(1/h)*(1/v)*(sum(sum(im_bin)));
display('area of tumour is :');
display (tumour_area);
D.tumour_area=tumour_area;

%%
%pixel calculation
[h,v]=size(im_bin);
number_pixel=(sum(sum(im_bin)));
display('number of pixel in the tumor area :');
display (number_pixel);
D.number_pixel=number_pixel;
%%
%subjective analysis
var1 = edge(im_bin,'canny');
var2 = imfuse(org_img,var1);
fuse_image=var2;
%figure, imshow(var2), title('subjective analysis');


if(p==1)
    ss=im2uint16(logimg);
else
    
    % for jpg images use im2uint8
    ss=im2uint8(logimg);
end


ss=im2uint8(ss);
seq=reshape(ss,[numel(ss) 1]);
img_hist=zeros(256,1);
for j=1:256
    img_hist(j)=sum(sum(ss==(j-1)));
end
count=img_hist';
count=count+1;
seq1=uint16(seq);
seq1=seq1+1;
code=arithenco(seq1,count);
decode=arithdeco(code,count,length(seq1));
decode=decode-1;
[r c]= size(ss);
img_out=reshape(decode,[r c]);
dc_roi=img_out;
%subplot(2,2,2);
 %imshow(img_out,[]),title('ROI after compression');
y=isequal(img_out,ss);
% display(y);
str3=sprintf('roi_before:roi_after = %f',y);
disp(str3);
%roi compression ratio
r_cr= (r*c*8)/(numel(code));
display(r_cr);
D.r_cr=r_cr;

%%
%roi bits per pixel
r_bpp=8/r_cr;
display(r_bpp);
D.r_bpp=r_bpp;


%%
if(p==1)
    
    % for dicom following conversion is needed
   s1=255/(max(max(simg)));   simg=simg.*s1;
% % %     simg=uint8(simg);

max_val = max(max(simg));
s1 = max_val/255;
simg = simg./s1;
simg=uint8(simg);

else
    simg=uint8(simg);
end



%%
imwrite(simg,'nroi_after.jpg','Mode','lossy','Quality',80);
n_info=imfinfo('nroi_after.jpg');
o=imread('nroi_after.jpg');
dc_nroi=o;
 %subplot(2,2,4);
%imshow(o,[]),title('NROI after compression');
n_cr=(n_info.Height*n_info.Width)/n_info.FileSize;
% str1=sprintf('n_cr is %f',n_cr);
% disp(str1);
display(n_cr);
D.n_cr=n_cr;
n_bpp=8/n_cr;
% str2=sprintf('n_bpp is %f',n_bpp);
% disp(str2);
display(n_bpp);
D.n_bpp=n_bpp;
time=(r*c*8)/(28.4*1024);
D.Time=time;
str3=sprintf('Time taken is %f',time);
disp(str3);
display(time);

figure, imshow(var2), title('subjective analysis');


%%
%feature extraction


if(p==1)
    %     for DICOM
    logimg1= im2uint16(logimg);
    logimg1= uint8(logimg1);
    glcm1 = graycomatrix(logimg1);
    h_entropy = entropy(logimg1);
    
else
    % for jpeg
    glcm1 = graycomatrix(logimg);
    h_entropy = entropy(logimg);
end

%%

feature = graycoprops(glcm1,{'energy','contrast','homogeneity','correlation'});
display(feature);
D.h_entropy=h_entropy;
D.feature=feature;

Error Occured:

Undefined function 'fcmthresh' for input arguments of type 'double'.

Error in finalthresh (line 155)

[bwfim_0,level_0]=fcmthresh(fim,0);

Answer 1

[KALYAN ACHARJYA]

Its clearly says

"Undefined function 'fcmthresh' ....."

To run the code you must have the following function in your current direcctory.

Get the user defined custom function fcmthresh here.

Load to function in the current directory and execute the code

[Syed Zenith Rhyhan]

Thanks a lot,Sir KALYAN ACHARJYA

[KALYAN ACHARJYA]

Welcome