How Can I Solve this error in Matlab?

function project_Murali_Sandhya(fn)

addpath( '../dataset' );

%extracts tumor from the MRI image

project_cv(fn);

end

function project_cv(fn)

%read image

im=imread(fn);

figure,imagesc(im);

colormap(gray);

pause(2);

%displays the size of image

disp(size(im));

%checks if image is an rgb image

if(size(im,3)>2)

im=rgb2gray(im);

imagesc(im);

colormap(gray);

pause(2);

end

%converts image to double

im_double=im2double(im);

%removes noise by applying median filter

im_med=medfilt2(im);

imshow(im_double);

% smoothing filter matrix

filtr=[1 1 1 1 1 1 1 ;

0 0 0 0 0 0 0];

%texture filter is applied to determine the texture image

im_text=rangefilt(im_double);

%applying smoothing filter on the texture image

im_text=imfilter(im_text,filtr);

imshow(im_text);

%takes coordinates of tumor region

[row,col]=ginput();

tumor_region=[row,col];

%determine the texture values of the tumor region

val_tumor=impixel(im_text,tumor_region(:,1),tumor_region(:,2));

disp('skull');

imshow(im_text);

%take coordinates of the skull region

[rols,cols]=ginput();

skull_region=[rols,cols];

%determine the texture values of the skull region

val_skull=impixel(im_double,skull_region(:,1),skull_region(:,2));

disp(val_skull(:,1));

%target variable is a vector which divides into two classes :0 represents

%skull region and 1 represents tumor region

target_variable=[zeros(numel(val_skull(:,1)),1); ones(numel(val_tumor(:,1)),1)];

%making the dimensions of the target variable and tumor region same

val_tumor=[val_tumor(:,1);zeros(length(target_variable) - length(val_tumor),1)];

%compute cross correlation

correlation=xcorr2(target_variable,val_tumor);

disp(max(correlation(:)));

%Otsu thresholding

%imtophat computes morphological opening using the structuring element as

%specified and subtracts the result from the original image.

im_thresh=imtophat(im_med,strel('disk',40));

imshow(im_thresh);

pause(2);

%improves the contrast of the image

im_adjust=imadjust(im_thresh);

imshow(im_adjust);

pause(2);

%determines the threshold value to perform segmentation

level=graythresh(im_adjust);

%segments the image into two classes: 0 if less than level and 1 if greater

%or equal to level

BW=imbinarize(im_adjust,level);

imshow(BW);

%performs morphological erosion

strel_erode=strel('disk',3);

im_erode=imerode(BW,strel_erode);

imshow(im_erode);

title('Otsu thresholding');

pause(5);

%performs normalized cross correlation

norm_corr=normxcorr2(im_erode,im);

disp(max(norm_corr(:)));

pause(5);

%applying local thresholding

%lower threshold

t0=50;

%upper threshold

th= t0+((max(im_med(:))+min(im_med(:)))./2);

seg_img=zeros(size(im_med,1),size(im_med,2));

for i= 1:1:size(im_med,1)

for j=1:1:size(im_med,2)

if im_med (i,j)>th

%sets to 1 if greater than threshold

seg_img(i,j)=1;

else

%sets to 0 if lesser than threshold

seg_img(i,j)=0;

end

end

end

imshow(seg_img);

pause(2);

%performs morphological erosion with disk as the structuring element

strel_erode=strel('disk',3);

im_erode=imerode(seg_img,strel_erode);

imshow(im_erode);

pause(2);

%performs morphological dilation with disk as the structuring element

str_dilate=strel('disk',3);

im_dilate=imdilate(im_erode,str_dilate);

imshow(im_dilate);

pause(2);

%performs morphological erosion with disk as the structuring element

im_erode1=imerode(im_dilate,strel_erode);

imshow(im_erode1);

title('Local Thresholding');

pause(5);

%performs normalized cross correlation

norm_corr=normxcorr2(im_erode1,im);

disp(max(norm_corr(:)));

pause(5);

%watershed segmentation

%imtophat computes morphological opening using the structuring element as

%specified and subtracts the result from the original image.

im_thresh=imtophat(im_med,strel('disk',40));

imshow(im_thresh);

pause(2);

%improves the contrast of the image

im_adjust=imadjust(im_thresh);

imshow(im_adjust);

pause(2);

%determines the threshold value for Otsu's segmentation

level=graythresh(im_adjust);

%segments the image using Otsu's threshold

BW=imbinarize(im_adjust,level);

imshow(BW);

pause(2);

%performs morphological erosion with disk as a structuring element

strel_erode=strel('disk',3);

%performs erosion with a structuring element of shape disk

im_erode=imerode(BW,strel_erode);

imshow(im_erode);

pause(2);

%take the compliment of the result

comp=~im_erode;

imshow(comp);

pause(2);

%compute distance between every pixel to every non-zero pixel

dist=-bwdist(comp);

dist(comp)=-Inf;

%apply watershed segmentation to get the labelled image

label=watershed(dist);

%convert the image to rgb

img_final=label2rgb(label,'gray','w');

imshow(img_final);

title('Watershed segmentation');

pause(5);

%perform %performs normalized cross correlation

norm_corr=normxcorr2(rgb2gray(img_final),im);

disp(max(norm_corr(:)));

pause(5);

%k-means clustering

%converts image to linear shape

img_reshape=reshape(im_med,[],1);

%apply k-means with k value as 4

[imgVecQ,imgVecC]=kmeans(double(img_reshape),4);

%arranging back into image

img_res=reshape(imgVecQ,size(im_med));

imagesc(img_res);

pause(2);

figure,

subplot(3,2,1),imshow(img_res==1,[]);

subplot(3,2,2),imshow(img_res==2,[]);

subplot(3,2,3),imshow(img_res==3,[]);

subplot(3,2,4),imshow(img_res==4,[]);

pause(2);

%perform normalized cross-correlation for each cluster

norm_corr=normxcorr2(img_res==1,im);

disp(max(norm_corr(:)));

pause(2);

norm_corr=normxcorr2(img_res==2,im);

disp(max(norm_corr(:)));

pause(2);

norm_corr=normxcorr2(img_res==3,im);

disp(max(norm_corr(:)));

pause(2);

norm_corr=normxcorr2(img_res==4,im);

disp(max(norm_corr(:)));

pause(2);

%MSER

%determines MSER features for a area range between 200 to 5000 and a

%threshold value of 12

[mserRegions, mserConnComp] = detectMSERFeatures(im_med, ...

'RegionAreaRange',[200 5000],'ThresholdDelta',12);

figure,

imshow(im_med)

hold on

%plot the regions obtained for the features extracted

plot(mserRegions, 'showPixelList', true,'showEllipses',false)

title('MSER regions')

pause(2);

hold off

end

*******************************Error Occured*********************************

>> matlabcode

Warning: Name is nonexistent or not a directory: ..\dataset

> In path at 109

In addpath at 86

In matlabcode at 3

Error using matlabcode (line 5)

Not enough input arguments.

Answer 1

[Jon]

Now you are encountering the problem that I listed as "Second Error:" in my original reply.

Your program includes a function, that needs an argument, specifically the name of an image file.

It look like you have saved all of these functions in one big file that you have called "matlabcode.m" In this case, assuming you had an image file called myimage.png somewhere on your search path then on the command line you would have to type matlabcode('myimage.png'). You will have to modify this for the actual name and extension of your image file.

Longer term, you should save each of the functions within this big block of code as individual files with the filename matching the function name, so you should have files project_Murali_Sandhya.m, project_cv.m etc.

You would then type on the command line something like project_Murali_Sandya('myimage.png')

[Syed Zenith Rhyhan]

Again Thanks a lot ,Jonathan Sir.Please give your email/Skype/Whats' app if possible for further communication for help.

[Jon]

Hi Syed,

I'm glad you were able to get your image processing functions running. Good luck with your project. Regarding email etc, I would prefer to just communicate here within MATLAB Answers. That way other people could help you and or benefit from answers too, so please just post new questions when you have them.

Answer 2

[Jon]

Hi Syed,

You actually have two errors.

First Error: You get an error from the command, addpath( '../dataset' ) . This command tells MATLAB to include the directory named dataset in the list of directories where it searches for files (the search path). The ../ is telling MATLAB that this file is located one directory up from the one you are currently in. The system is telling you that there is no directory named dataset one level above the one where you are running this function. So, for example if you were running this function in a directory called C:\matlab\myproject, then you are telling MATLAB it should include the directory C:\matlab\dataset in the search path, and apparently it is not there. Also you may want to check that you are using slashes like this \ if you are in Windows, maybe the other way for some other OS. MATLAB is pretty forgiving about this, but still it is good to be consistent with your OS.

Second Error: You are trying to run the function project_Murali_Sandhya as a script, but it is a function that needs one argument. I think you are either just typing and entering the name of the function, project_Murali_Sandhya, on the command line, or you are in the editor with the file open and clicking the Run icon. To run this function, you must provide it with an input argument, which in this case is the name of an image file. So for example if you had an image file named myimage.png somewhere on your search path, then on the command line you could type project_Murali_Sandhya('myimage.png'). You could also call it from within another function, or assign the name of a file to a variable and pass it that way, but in any case you must provide your function with the name of a file that exists on the current search path.

Finally for future postings, when you include MATLAB code, it is better to click the code icon in the MATLAB answers interface. This will allow the code to be displayed with nice formatting and colors as if in the MATLAB editor and also allows people to extract it directly as code.

[Syed Zenith Rhyhan]

Thanks for ur answer.But i can't solve my errors still.please help I try on this way but i can't understand how can i solve this error #Jonathan Sir please help

function project_Murali_Sandhya(fn)
    
    addpath( 'C:\Users\4szrn\Dropbox\Brain_Tumor_Thesis\MRI-scan-segmentation-master\Brain-Tumor-Extraction-From-MRI-images-master\dataset' );
    %extracts tumor from the MRI image
    project_cv(fn);
end

function project_cv(fn)
%read image
im=imread(fn);
figure,imagesc(im);
colormap(gray);
pause(2);

%displays the size of image
disp(size(im));

%checks if image is an rgb image
if(size(im,3)>2)
im=rgb2gray(im);
imagesc(im);
colormap(gray);
pause(2);
end

%converts image to double
im_double=im2double(im);
%removes noise by applying median filter
im_med=medfilt2(im);

imshow(im_double);

% smoothing filter matrix
filtr=[1 1 1 1 1 1 1 ;
       0 0 0 0 0 0 0];
   
   %texture filter is applied to determine the texture image
  im_text=rangefilt(im_double);
  %applying smoothing filter on the texture image
  im_text=imfilter(im_text,filtr);
  imshow(im_text);
  
  %takes coordinates of tumor region
  [row,col]=ginput();
tumor_region=[row,col];
%determine the texture values of the tumor region
val_tumor=impixel(im_text,tumor_region(:,1),tumor_region(:,2));

disp('skull');
imshow(im_text);
%take coordinates of the skull region
[rols,cols]=ginput();
skull_region=[rols,cols];

%determine the texture values of the skull region
val_skull=impixel(im_double,skull_region(:,1),skull_region(:,2));
disp(val_skull(:,1));

%target variable is a vector which divides into two classes :0 represents
%skull region and 1 represents tumor region
target_variable=[zeros(numel(val_skull(:,1)),1); ones(numel(val_tumor(:,1)),1)];

%making the dimensions of the target variable and tumor region same
val_tumor=[val_tumor(:,1);zeros(length(target_variable) - length(val_tumor),1)];

%compute cross correlation
correlation=xcorr2(target_variable,val_tumor);
disp(max(correlation(:)));


%Otsu thresholding

%imtophat computes morphological opening using the structuring element as
%specified and subtracts the result from the original image.
im_thresh=imtophat(im_med,strel('disk',40));
imshow(im_thresh);
pause(2);
%improves the contrast of the image
im_adjust=imadjust(im_thresh);
imshow(im_adjust);
pause(2);
%determines the threshold value to perform segmentation
level=graythresh(im_adjust);
%segments the image into two classes: 0 if less than level and 1 if greater
%or equal to level
BW=imbinarize(im_adjust,level);
imshow(BW);
%performs morphological erosion
strel_erode=strel('disk',3);
im_erode=imerode(BW,strel_erode);
imshow(im_erode);
title('Otsu thresholding');
pause(5);

%performs normalized cross correlation 
norm_corr=normxcorr2(im_erode,im);
disp(max(norm_corr(:)));
pause(5);

%applying local thresholding

%lower threshold
t0=50;
%upper threshold
th= t0+((max(im_med(:))+min(im_med(:)))./2);
seg_img=zeros(size(im_med,1),size(im_med,2));
for i= 1:1:size(im_med,1)
    for j=1:1:size(im_med,2)
        if im_med (i,j)>th
            %sets to 1 if greater than threshold
            seg_img(i,j)=1;
        else
            %sets to 0 if lesser than threshold
            seg_img(i,j)=0;
        end
    end
end

imshow(seg_img);
pause(2);

%performs morphological erosion with disk as the structuring element
strel_erode=strel('disk',3);
im_erode=imerode(seg_img,strel_erode);
imshow(im_erode);
pause(2);

%performs morphological dilation with disk as the structuring element
str_dilate=strel('disk',3);
im_dilate=imdilate(im_erode,str_dilate);
imshow(im_dilate);
pause(2);

%performs morphological erosion with disk as the structuring element
im_erode1=imerode(im_dilate,strel_erode);
imshow(im_erode1);
title('Local Thresholding');
pause(5);


%performs normalized cross correlation 
norm_corr=normxcorr2(im_erode1,im);
disp(max(norm_corr(:)));
pause(5);

%watershed segmentation

%imtophat computes morphological opening using the structuring element as
%specified and subtracts the result from the original image.
im_thresh=imtophat(im_med,strel('disk',40));
imshow(im_thresh);
pause(2);
%improves the contrast of the image
im_adjust=imadjust(im_thresh);
imshow(im_adjust);
pause(2);
%determines the threshold value for Otsu's segmentation
level=graythresh(im_adjust);
%segments the image using Otsu's threshold
BW=imbinarize(im_adjust,level);
imshow(BW);
pause(2);
%performs morphological erosion with disk as a structuring element
strel_erode=strel('disk',3);
%performs erosion with a structuring element of shape disk
im_erode=imerode(BW,strel_erode);
imshow(im_erode);
pause(2);
%take the compliment of the result
comp=~im_erode;
imshow(comp);
pause(2);
%compute distance between every pixel to every non-zero pixel
dist=-bwdist(comp);
dist(comp)=-Inf;
%apply watershed segmentation to get the labelled image
label=watershed(dist);
%convert the image to rgb
img_final=label2rgb(label,'gray','w');
imshow(img_final);
title('Watershed segmentation');
pause(5);

%perform %performs normalized cross correlation 
norm_corr=normxcorr2(rgb2gray(img_final),im);
disp(max(norm_corr(:)));
pause(5);

%k-means clustering

%converts image to linear shape
img_reshape=reshape(im_med,[],1);
%apply k-means with k value as 4
[imgVecQ,imgVecC]=kmeans(double(img_reshape),4); 
%arranging back into image
img_res=reshape(imgVecQ,size(im_med)); 
imagesc(img_res);
pause(2);
figure,
subplot(3,2,1),imshow(img_res==1,[]);
subplot(3,2,2),imshow(img_res==2,[]);
subplot(3,2,3),imshow(img_res==3,[]);
subplot(3,2,4),imshow(img_res==4,[]);
pause(2);

%perform normalized cross-correlation for each cluster
norm_corr=normxcorr2(img_res==1,im);
disp(max(norm_corr(:)));
pause(2);


norm_corr=normxcorr2(img_res==2,im);
disp(max(norm_corr(:)));
pause(2);


norm_corr=normxcorr2(img_res==3,im);
disp(max(norm_corr(:)));
pause(2);


norm_corr=normxcorr2(img_res==4,im);
disp(max(norm_corr(:)));
pause(2);


%MSER

%determines MSER features for a area range between 200 to 5000 and a
%threshold value of 12
[mserRegions, mserConnComp] = detectMSERFeatures(im_med, ...
    'RegionAreaRange',[200 5000],'ThresholdDelta',12);

figure,
imshow(im_med)
hold on
%plot the regions obtained for the features extracted
plot(mserRegions, 'showPixelList', true,'showEllipses',false)
title('MSER regions')
pause(2);

hold off

end

Error Occured this time:

Warning: Name is nonexistent or not a directory:

C:\Users\4szrn\Dropbox\Brain_Tumor_Thesis\MRI-scan-segmentation-master\Brain-Tumor-Extraction-From-MRI-images-master\dataset

> In path at 109

In addpath at 86

In matlabcode at 3

Error using matlabcode (line 5)

Not enough input arguments.

Answer 3

[Jon]

Hi Syed,

MATLAB is telling you that you don't have a directory called: C:\Users\4szrn\Dropbox\Brain_Tumor_Thesis\MRI-scan-segmentation-master\Brain-Tumor-Extraction-From-MRI-images-master\dataset

I suspect that either you have some typographical error in the path name that you have typed (something mispelled) or this directory really doesn't exist. Are you on a windows computer? If so, I suggest going to the windows file explorer, navigate to where you think the dataset directory is and confirm that the path you specify exactly matches the actual directory. Otherwise do the equivalent in your Operating System.

I would also suggest that you relocate (copy) the dataset directory to someplace with a much shorter and easier to spell and remember path, e.g. make a directory called C:\Matlab\mywork\dataset and put your images in there

[Syed Zenith Rhyhan]

Thanks a lot,Jonathan Sir.Now the path problem solved and shows the following error

>> matlabcode

Error using matlabcode (line 5)

Not enough input arguments.

>>

function project_Murali_Sandhya(fn)
    
    addpath( 'C:\Users\4szrn\Dropbox\Brain_Tumor_Thesis\Brain-Tumour-Detection-master\dataset' );
    %extracts tumor from the MRI image
    project_cv(fn);
end

function project_cv(fn)
%read image
im=imread(fn);
figure,imagesc(im);
colormap(gray);
pause(2);

%displays the size of image
disp(size(im));

%checks if image is an rgb image
if(size(im,3)>2)
im=rgb2gray(im);
imagesc(im);
colormap(gray);
pause(2);
end

%converts image to double
im_double=im2double(im);
%removes noise by applying median filter
im_med=medfilt2(im);

imshow(im_double);

% smoothing filter matrix
filtr=[1 1 1 1 1 1 1 ;
       0 0 0 0 0 0 0];
   
   %texture filter is applied to determine the texture image
  im_text=rangefilt(im_double);
  %applying smoothing filter on the texture image
  im_text=imfilter(im_text,filtr);
  imshow(im_text);
  
  %takes coordinates of tumor region
  [row,col]=ginput();
tumor_region=[row,col];
%determine the texture values of the tumor region
val_tumor=impixel(im_text,tumor_region(:,1),tumor_region(:,2));

disp('skull');
imshow(im_text);
%take coordinates of the skull region
[rols,cols]=ginput();
skull_region=[rols,cols];

%determine the texture values of the skull region
val_skull=impixel(im_double,skull_region(:,1),skull_region(:,2));
disp(val_skull(:,1));

%target variable is a vector which divides into two classes :0 represents
%skull region and 1 represents tumor region
target_variable=[zeros(numel(val_skull(:,1)),1); ones(numel(val_tumor(:,1)),1)];

%making the dimensions of the target variable and tumor region same
val_tumor=[val_tumor(:,1);zeros(length(target_variable) - length(val_tumor),1)];

%compute cross correlation
correlation=xcorr2(target_variable,val_tumor);
disp(max(correlation(:)));


%Otsu thresholding

%imtophat computes morphological opening using the structuring element as
%specified and subtracts the result from the original image.
im_thresh=imtophat(im_med,strel('disk',40));
imshow(im_thresh);
pause(2);
%improves the contrast of the image
im_adjust=imadjust(im_thresh);
imshow(im_adjust);
pause(2);
%determines the threshold value to perform segmentation
level=graythresh(im_adjust);
%segments the image into two classes: 0 if less than level and 1 if greater
%or equal to level
BW=imbinarize(im_adjust,level);
imshow(BW);
%performs morphological erosion
strel_erode=strel('disk',3);
im_erode=imerode(BW,strel_erode);
imshow(im_erode);
title('Otsu thresholding');
pause(5);

%performs normalized cross correlation 
norm_corr=normxcorr2(im_erode,im);
disp(max(norm_corr(:)));
pause(5);

%applying local thresholding

%lower threshold
t0=50;
%upper threshold
th= t0+((max(im_med(:))+min(im_med(:)))./2);
seg_img=zeros(size(im_med,1),size(im_med,2));
for i= 1:1:size(im_med,1)
    for j=1:1:size(im_med,2)
        if im_med (i,j)>th
            %sets to 1 if greater than threshold
            seg_img(i,j)=1;
        else
            %sets to 0 if lesser than threshold
            seg_img(i,j)=0;
        end
    end
end

imshow(seg_img);
pause(2);

%performs morphological erosion with disk as the structuring element
strel_erode=strel('disk',3);
im_erode=imerode(seg_img,strel_erode);
imshow(im_erode);
pause(2);

%performs morphological dilation with disk as the structuring element
str_dilate=strel('disk',3);
im_dilate=imdilate(im_erode,str_dilate);
imshow(im_dilate);
pause(2);

%performs morphological erosion with disk as the structuring element
im_erode1=imerode(im_dilate,strel_erode);
imshow(im_erode1);
title('Local Thresholding');
pause(5);


%performs normalized cross correlation 
norm_corr=normxcorr2(im_erode1,im);
disp(max(norm_corr(:)));
pause(5);

%watershed segmentation

%imtophat computes morphological opening using the structuring element as
%specified and subtracts the result from the original image.
im_thresh=imtophat(im_med,strel('disk',40));
imshow(im_thresh);
pause(2);
%improves the contrast of the image
im_adjust=imadjust(im_thresh);
imshow(im_adjust);
pause(2);
%determines the threshold value for Otsu's segmentation
level=graythresh(im_adjust);
%segments the image using Otsu's threshold
BW=imbinarize(im_adjust,level);
imshow(BW);
pause(2);
%performs morphological erosion with disk as a structuring element
strel_erode=strel('disk',3);
%performs erosion with a structuring element of shape disk
im_erode=imerode(BW,strel_erode);
imshow(im_erode);
pause(2);
%take the compliment of the result
comp=~im_erode;
imshow(comp);
pause(2);
%compute distance between every pixel to every non-zero pixel
dist=-bwdist(comp);
dist(comp)=-Inf;
%apply watershed segmentation to get the labelled image
label=watershed(dist);
%convert the image to rgb
img_final=label2rgb(label,'gray','w');
imshow(img_final);
title('Watershed segmentation');
pause(5);

%perform %performs normalized cross correlation 
norm_corr=normxcorr2(rgb2gray(img_final),im);
disp(max(norm_corr(:)));
pause(5);

%k-means clustering

%converts image to linear shape
img_reshape=reshape(im_med,[],1);
%apply k-means with k value as 4
[imgVecQ,imgVecC]=kmeans(double(img_reshape),4); 
%arranging back into image
img_res=reshape(imgVecQ,size(im_med)); 
imagesc(img_res);
pause(2);
figure,
subplot(3,2,1),imshow(img_res==1,[]);
subplot(3,2,2),imshow(img_res==2,[]);
subplot(3,2,3),imshow(img_res==3,[]);
subplot(3,2,4),imshow(img_res==4,[]);
pause(2);

%perform normalized cross-correlation for each cluster
norm_corr=normxcorr2(img_res==1,im);
disp(max(norm_corr(:)));
pause(2);


norm_corr=normxcorr2(img_res==2,im);
disp(max(norm_corr(:)));
pause(2);


norm_corr=normxcorr2(img_res==3,im);
disp(max(norm_corr(:)));
pause(2);


norm_corr=normxcorr2(img_res==4,im);
disp(max(norm_corr(:)));
pause(2);


%MSER

%determines MSER features for a area range between 200 to 5000 and a
%threshold value of 12
[mserRegions, mserConnComp] = detectMSERFeatures(im_med, ...
    'RegionAreaRange',[200 5000],'ThresholdDelta',12);

figure,
imshow(im_med)
hold on
%plot the regions obtained for the features extracted
plot(mserRegions, 'showPixelList', true,'showEllipses',false)
title('MSER regions')
pause(2);

hold off

end