Medical Image Processing - Digital Signal Processing for Medical Imaging Using Matlab - page 75

Fig. 4.5 a Logirthmic low pass spectrum. b Image corresponding to low pass spectrum. c Logirth-
mic high pass spectrum. d Image corresponding to high pass spectrum
shown in the figure. It is noted that the spatial domain obtaining using the hankel
transformation requires lesser number of computation to obtain the spatial domain.
5. Note that in case of continous hankel transformation, the 2D-filter specification
is perfectly circular symmetric and hence 2D-FFT consists of only the real com-
ponents.
6. Ideally (1) and (4) subplots of the Fig. 4.6 must be identical. The variation is due
to the imperfection in the circular symmetry while realizing in discrete form.
4.2.4.2 hankeltransformation.m
%Demonstration of Hankel transformation
A=zeros(31,31);
a=ones(1,15);
for r=0:1:9
for theta=0:(2*pi)/360:(2*pi);
x=round(r*cos(theta));
y=round(r*sin(theta));
A(x+16,y+16)=a(r+1);