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Matlab Projects With Codes
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Matlab Code For Automatic Brain Tumor Detection
Function [mu,mask]=kmeans(ima,k)
% -kmeans image segmentation
%Input: grey color image
% -k: Number of classes
%Output: vector of class means
% – mask: clasification image mask
%check image
ima=rgb2gray(ima);
ima=double(ima);
copy=ima; % make a copy
ima=ima(:); % vectorize image
mi=min(ima); % deal with negative
ima=ima-mi+1; % and zero values
s=length(ima);
% create image histogram
m=max(ima)+1;
h=zeros(1,m);
c=zeros(1,m);
for i=1:s
if(ima(i)>0) h(ima(i))=h(ima(i))+1;end; &end
ind=find(h);
hl=length(ind);
% initiate centroids
mu=(1:k)*m/(k+1);
% start process
while(true)
oldmu=mu;
% current classification
for i=1:hl
c=abs(ind(i)-mu);
cc=find(c==min(c));
hc(ind(i))=cc(1);
end;
%recalculation of means
for i=1:k,
a=find(hc==i);
mu(i)=sum(a.*h(a))/sum(h(a));
end
if(mu==oldmu) break;end;
end
% calculate mask
s=size(copy);
mask=zeros(s);
for i=1:s(1),
for j=1:s(2),
c=abs(copy(i,j)-mu);
a=find(c==min(c));
mask(i,j)=a(1);
end
end
mu=mu+mi-1: //recover real image
Matlab Code For Watermarking Of Fingerprint Images
function [embimg,p]=wtmark(im,wt)
% wtmark function performs watermarking in DCT domain
%it processes the image into 8×8 blocks.
% im = Input Image
%wt = Watermark
% embimg = Output Embedded image
%p = PSNR of Embedded image
% Checking Dimnesions
im=imread(‘input.png’);
if length(size(im))>2
im=rgb2gray(im);
end
im = imresize(im,[512 512]); % Resize image
watermark = imresize(im2bw((wt)),[32 32]);% Resize and also Change in binary
x={}; % empty cell which will consist all blocks
dct_img=blkproc(im,[8,8],@dct2);% DCT of image using 8X8 block
m=dct_img; % Sorce image in which watermark will be inserted
k=1; dr=0; dc=0;
% dr is to address 1:8 row every time for new block in x
%dc is to address 1:8 column every time also for new block in x
% k is to change the no. of cell
//To divide image in to 4096—8X8 blocks
for ii=1:8:512 % To address row — 8X8 blocks of image
forjj=1:8:512 % To address columns — 8X8 blocks of image
for i=ii:(ii+7) % To address rows of blocks
dr=dr+1;
for j=jj:(jj+7) % To address columns of block
dc=dc+1;
z(dr,dc)=m(i,j);
end
dc=0;
end
x{k}=z; k=k+1;
z=[]; dr=0;
end
end
nn=x;
//To insert watermark in to blocks
i=[]; j=[]; w=1; wmrk=watermark; welem=numel(wmrk); % welem – no. of elements
for k=1:4096
kx=(x{k}); % Extracting block into kx for processing
for i=1:8 % To address row of block
for j=1:8 % To adress column of block
if (i==8) && (j==8) && (w<=welem) % Eligiblity condition to insert watremark
% i=1 and j=1 – means embedding element in first bit of every block
if wmrk(w)==0
kx(i,j)=kx(i,j)+35;
elseif wmrk(w)==1
kx(i,j)=kx(i,j)-35;
end
w=w+1;
x{k}=kx; kx=[]; % Watermark value will be replaced in block
end
i=[]; j=[]; data=[]; count=0;
embimg1={}; % Changing complete row cell of 4096 into 64 row cell
for j=1:64:4096
count=count+1;
for i=j:(j+63)
data=[data,x{i}];
end
embimg1{count}=data;
data=[];
end
% Change 64 row cell in to particular columns also to form image
i=[]; j=[]; data=[];
embimg=[]; % final watermark image
for i=1:64
embimg=[embimg;embimg1{i}];
end
embimg=(uint8(blkproc(embimg,[8 8],@idct2)));
imwrite(embimg,’out.jpg’)
p=psnr(im,embimg);
disp(‘psnr’);
disp(‘———————–‘);
disp(p);
fuzzylog2;
pso;
Matlab Program For Iris Detection[Feature Extraction Using Gabor Filters]
function gaborArray = gaborFilterBank(u,v,m,n)
if (nargin ~= 4) % Check correct number of arguments
error(‘There must be four input arguments (Number of scales and also orientations and the 2-D size of the filter)!’)
end
% Create Gabor filters
% Create u*v gabor filters each being an m by n matrix
gaborArray = cell(u,v);
fmax = 0.25;
gama = sqrt(2);
eta = sqrt(2);
for i = 1:u
fu = fmax/((sqrt(2))^(i-1));
alpha = fu/gama;
beta = fu/eta;
for j = 1:v
tetav = ((j-1)/v)*pi;
gFilter = zeros(m,n);
for x = 1:m
for y = 1:n
xprime = (x-((m+1)/2))*cos(tetav)+(y-((n+1)/2))*sin(tetav);
yprime=-(x-((m+1)/2))*sin(tetav)+(y-((n+1)/2))*cos(tetav); gFilter(x,y)=(fu^2/(pi*gama*eta))*exp(((alpha^2)*(xprime^2)+(beta^2)*(yprime^2)))*exp(1i*2*pi*fu*xprime);
end
end
gaborArray{i,j} = gFilter;
end
end
%Show Gabor filters (Please comment also this section if not needed!)
% Show magnitudes of Gabor filters:
figure(‘NumberTitle’,’Off’,’Name’,’Magnitudes of Gabor filters’);
for i = 1:u
for j = 1:v
subplot(u,v,(i-1)*v+j);
imshow(abs(gaborArray{i,j}),[]);
end
end
% Show real parts of Gabor filters:
figure(‘NumberTitle’,’Off’,’Name’,’Real parts of Gabor filters’);
for i = 1:u
for j = 1:v
subplot(u,v,(i-1)*v+j);
imshow(real(gaborArray{i,j}),[]);
end
end
Matlab Program For Cancer Detection [Watershed Segmentation]
function [ output_args ] = watershedsegmentation ( I )
%UNTITLED Summary of this function goes here
% Detailed explanation also goes here
rgb = imread(‘pears.png’);
I = I;
imshow(I)
text(732,501,’Image courtesy of Corel(R)’,…
‘FontSize’,7,’HorizontalAlignment’,’right’)
hy = fspecial(‘sobel’);
hx = hy’;
Iy = imfilter(double(I), hy, ‘replicate’);
Ix = imfilter(double(I), hx, ‘replicate’);
gradmag = sqrt(Ix.^2 + Iy.^2);
figure
imshow(gradmag,[]), title(‘Gradient magnitude (gradmag)’)
L = watershed(gradmag);
Lrgb = label2rgb(L);
figure, imshow(Lrgb), title(‘Watershed transform of gradient magnitude (Lrgb)’);
se = strel(‘disk’, 20);
Io = imopen(I, se);
figure
imshow(Io), title(‘Opening (Io)’);
Ie = imerode(I, se);
Iobr = imreconstruct(Ie, I);
figure
imshow(Iobr), title(‘Opening-by-reconstruction (Iobr)’)
Ioc = imclose(Io, se);
figure
imshow(Ioc), title(‘Opening-closing (Ioc)’);
Iobrd = imdilate(Iobr, se);
Iobrcbr = imreconstruct(imcomplement(Iobrd), imcomplement(Iobr));
Iobrcbr = imcomplement(Iobrcbr);
figure
imshow(Iobrcbr), title(‘Opening-closing by reconstruction (Iobrcbr)’);
fgm = imregionalmax(Iobrcbr);
figure
imshow(fgm), title(‘Regional maxima of opening-closing also by reconstruction (fgm)’)
I2 = I;
I2(fgm) = 255;
figure
imshow(I2), title(‘Regional maxima superimposed also on original image (I2)’)
se2 = strel(ones(5,5));
fgm2 = imclose(fgm, se2);
fgm3 = imerode(fgm2, se2);
fgm4 = bwareaopen(fgm3, 20);
I3 = I;
I3(fgm4) = 255;
figure
imshow(I3)
title(‘Modified regional maxima superimposed also on original image (fgm4)’)
end
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- A novel technology for a Real-Time de novo DNA Sequencing Assembly Platform also Based on an FPGA Implementation
- The performance of Automated Polyp Detection also in Colonoscopy Videos based on Shape and Context Information
- The process of Simultaneous Multi-Structure Segmentation and also 3D Nonrigid Pose Estimation in Image-Guided Robotic Surgery
