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**Matlab Projects With Codes**

**Matlab Projects With Codes**

Generally, Matlab Projects with Codes offer you the best code, mined as an outcome of our technocrats and developers’ efforts. Our code can make you feel our standard and quality due to the coding efficiency and technical stuff it contains. Our technocrats update themselves with all the latest tools and techniques, which make them efficient in developing codes with high quality and standards. You can approach us anytime through online; we will provide complete guidance for implementing your project. We have provided a few sample projects along with the code for students to understand our code efficiency.

**Matlab Code For Automatic Brain Tumor Detection**

**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**

**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]**

**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]**

**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

**You Can Understand Our Standard And Quality Better, When You Work With Us. To Give You An Idea For Your Matlab Projects, We Have Provided Few Sample Topics Below,**

**You Can Understand Our Standard And Quality Better, When You Work With Us. To Give You An Idea For Your Matlab Projects, We Have Provided Few Sample Topics Below,**

- 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