Skip to main content

Stretch the dynamic range of the given 8-bit grayscale image using MATL...

take largest connected components in 2D slices and then reconstruct the 3D by stacking them

 Hello,

I am doing some morphological image processing. I want to remove the 4 unwanted blobs from the binary object(image below)
If I axially slice the image, I can take the largest connected component and exclude others like the image below:
I have tried the following codes which results binary size mismatch issues.
ind = 1;
sz = size(bi) % bi is the 3D mask with dimension: 170x256x100
final_mask = zeros(sz) 
for i = 1:sz(3) %taking the 2D axial slice iterations->  1:100
    cc = bwconncomp(bi(:,:,i)); 
%     disp(cc)
    S = regionprops(cc, 'Area', 'PixelIdxList');%'Centroid');
    ax_slice = zeros(sz(1), sz(2));
    numPixels = cellfun(@numel, cc.PixelIdxList);
%     disp(numPixels)
    [biggest,idx] = max(numPixels); % taking the largest component number of pixels and their indices
    ax_slice(cc.PixelIdxList{idx})= 1;
    final_mask(:,:,i) = ax_slice;
end

Please let me know how to deal with it. 


 NOTE:-


Matlabsolutions.com provide latest MatLab Homework Help,MatLab Assignment Help for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research.

  • erode image
  • dilate image

 

clc                         % clear command window
cla                         % clear axes
bi11 = bi;                  % create copy of 'bi'
bi1 = bi(:,:,170:end);      % get top part
R = 2;                      % radius of structuring element
[x,y,z] = meshgrid(-R:R);   % X Y Z 3d matrices
el = x.^2+y.^2+z.^2 <= R^2; % sphere of radius 'R'
bi2 = imerode(bi1, el);     % erode image
bi3 = imdilate(bi2, el);    % dilate image
bi11(:,:,170:end) = bi3;    % replace original top part

isosurface(bi11,.95)        % display 3d image
axis vis3d
light

Comments

Popular posts from this blog

https://journals.worldnomads.com/scholarships/story/70330/Worldwide/Dat-shares-his-photos-from-Bhutan https://www.blogger.com/comment.g?blogID=441349916452722960&postID=9118208214656837886&page=2&token=1554200958385 https://todaysinspiration.blogspot.com/2016/08/lp-have-look-at-this-this-is-from.html?showComment=1554201056566#c578424769512920148 https://behaviorpsych.blogspot.com/p/goal-bank.html?showComment=1554201200695 https://billlumaye.blogspot.com/2012/10/tagg-romney-drops-by-bill-show.html?showComment=1550657710334#c7928008051819098612 http://blog.phdays.com/2014/07/review-of-waf-bypass-tasks.html?showComment=1554201301305#c6351671948289526101 http://www.readyshelby.org/blog/gifts-of-preparedness/#comment_form http://www.hanabilkova.svet-stranek.cz/nakup/ http://www.23hq.com/shailendrasingh/photo/21681053 http://blogs.stlawu.edu/jbpcultureandmedia/2013/11/18/blog-entry-10-guns-as-free-speech/comment-page-1443/#comment-198345 https://journals.worldnomads.com

USING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR DETECTING SPAM AND NON-SPAM EMAILS

    ABSTRACT We know the increasing volume of unwanted volume of emails as spam. As per statistical analysis 40% of all messages are spam which about 15.4 billion email for every day and that cost web clients about $355 million every year. Spammers to use a few dubious techniques to defeat the filtering strategies like utilizing irregular sender addresses or potentially add irregular characters to the start or the finish of the message subject line. A particular calculation is at that point used to take in the order rules from these email messages. Machine learning has been contemplated and there are loads of calculations can be used in email filtering. To classify these mails as spam and non-spam mails implementation of machine learning algorithm  such as KNN, SVM, Bayesian classification  and ANN  to develop better filtering tool.   Contents ABSTRACT 2 1. INTRODUCTION 4 1.1 Objective : 5 2. Literature Review 5 2.1. Existing Machine learning technique. 6 2.2 Existing

Why are Fourier series important? Are there any real life applications of Fourier series?

A  Fourier series  is a way of representing a periodic function as a (possibly infinite) sum of sine and cosine functions. It is analogous to a Taylor series, which represents functions as possibly infinite sums of monomial terms. A sawtooth wave represented by a successively larger sum of trigonometric terms. For functions that are not periodic, the Fourier series is replaced by the Fourier transform. For functions of two variables that are periodic in both variables, the trigonometric basis in the Fourier series is replaced by the spherical harmonics. The Fourier series, as well as its generalizations, are essential throughout the physical sciences since the trigonometric functions are eigenfunctions of the Laplacian, which appears in many physical equations. Real-life applications: Signal Processing . It may be the best application of Fourier analysis. Approximation Theory . We use Fourier series to write a function as a trigonometric polynomial. Control Theory . The F