Image Processing Reference
In-Depth Information
% divergence between the maximum and minimum levels of each
window
divmx=mxwin_high-mxwin_low;
divmn=mnwin_high-mnwin_low;
div_mx(i-1,j-1)=divmx;
div_mn(i-1,j-1)=divmn;
end
end
div_im_mx= div_mx/(max(max(div_mx))-min(min(div_mx)));
div_im_mn= div_mn/(max(max(div_mn))-min(min(div_mn)));
div_diff=abs(div_im_mx-div_im_mn); % difference in the
divergence values
figure, imshow(div_diff) % fuzzy edge
8.8 Summary
This chapter discusses different edge detection techniques using fuzzy, intu-
itionistic fuzzy, interval Type II fuzzy and Type II fuzzy set theoretic tech-
niques on medical images. Fuzzy methods consider only one uncertainty
and so are useful in medical image processing. But in many cases, fuzzy
methods do not show better edge images, and in that case, some advanced
fuzzy edge detection techniques are used. Edge enhancement is also shown
especially for medical images when edges are not clearly visible, and in that
case, edges are enhanced before detection. Fuzzy edge image generation
using interval-valued fuzzy set and Type II fuzzy set is also shown. This
will help in selecting appropriate edge detectors. A MATLAB code for the
methods is also provided which will be beneficial to the readers in imple-
menting the methods.
References
1. Barrenchea, E. et al., Construction of interval valued fuzzy relation with appli-
cation to generation of fuzzy edge images, IEEE Transactions on Fuzzy Systems ,
9(5), 819-830, 2011.
2. Becerikli, Y. and Karan, T.M., A new fuzzy approach to edge detection, Lecture
Notes in Computer Science ( LNCS ), 3512, 943-951, June 2005.
3. Canny, J., Computational approach to edge detection, IEEE Transactions on
Pattern Analysis and Machine Intelligence , 8(6), 679-698, 1986.
4. Chaira, T., Image segmentation and color retrieval: A fuzzy and intuitionistic
fuzzy set theoretic approach, PhD Thesis, IIT Kharagpur, India, 2005.
 
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