Image Enhancement - Medical Image Processing: Advanced Fuzzy Set Theoretic Techniques - page 107

% Atanassov't operator to convert intuitionistic fuzzy
image to fuzzy image
v1=0.0;v2=0.0;
for i=1:dim
for j=1:dim
num= (newhes(i,j)*(1-2*newmem(i,j)))+ v1;
v1=num;
denom=(newhes(i,j)^2)+ v2;
v2=denom;
end
end
alpha_opt1 =num/(2*denom);
if alpha_opt1>0 & alpha_opt1<1
alpha_opt=alpha_opt1;
end
D_alpha_opt=(1-alpha_opt)*newmem + (1-newnonmem)*alpha_opt;
enh_im=uint8(255*D_alpha_opt);
figure,imshow(enh_im); % final enhanced image
5.7 Summary
In this chapter, image enhancement using fuzzy, IF and Type II fuzzy set the-
ories is suggested. Enhancement is the preprocessing of images to enhance
or highlight the image structures and suppress unwanted information in the
image. Fuzzy enhancement does provide better results, but in some cases, IF
and Type II fuzzy enhancement methods provide better results. This may be
due to the fact that these advanced fuzzy sets consider either more number
of uncertainties or different types of uncertainty. Also, MATLAB codes of
various types of image enhancement schemes proposed by different authors
are discussed.
References
1. Acharya, T. and Ray, A.K., Image Processing: Principles and Application , John
Wiley and Sons, 2005.
2. Atanassov, K.T., Intuitionistic Fuzzy Sets: Theory and Applications , Series in
Fuzziness and Soft Computing, Physica-Verlag, Heidelberg, Germany, 1999.
3. Ban, A.I., Intuitionistic Fuzzy Measures: Theory and Applications , Nova Science
Publishers, New York, 2006.