2.4 Image Processing by Filters 
It includes different applications of filtering to reveal 
automatically the detection of skin disorders. 
2.5 Quantitative Analysis 
Determining the true space the scar covers and gives to the 
decision maker the tool measure it. This step is processed by 
using two separate tools in Photoshop (which are the magic 
wand and the magnetic lasso tool). 
3. DETERMINING THE CONDITIONS FOR MOST 
REAL LIKE IMAGES 
According to Boersma (1998), if the camera is very sensitive to 
light, very strong illumination might not be necessary; on the 
other hand, common lighting should respond the need of 
providing sufficient depth of field. White fluorescent light was 
used in the experimental stage. As a primary test, a set of 
examination was run in order to consider which camera 
parameters provide most real-like vision. A colourful paper was 
attached on the wall to take snapshots. 82 images were taken in 
different shutter speed, aperture mode, metering mode, ISO 
Speed and White balance specifications. After the evaluation of 
images, the criteria below were determined the most suitable to 
collect the most natural images. 
* ISO Speed: 400 
s Shutter Speed: 273 
* Aperture Value: 32 
*  Metering Mode: Evaluative 
* . White Balance: AWB (Automatic White Balance) 
Figure 3.1 shows different test snapshots with different criteria. 
    
(c) (d) 
Figure 1. Test snapshots with different parameters: (a) S S: 2”, 
AV:32, WB: AWB; (b) SS: 275, AV:: 32, WB: AWB; (c) SS: 
2", AV: 32, WB: White Fluorescent; (d) SS: 2"5, AV: 32, WB: 
White Fluorescent 
All snapshots were acquired in Aristotle University of 
Thessaloniki Laboratory with Canon EOS Digital Rebel XTi 
Camera at 55mm focal length, no flash, one-shot AF Mode. All 
the images were stored as Jpeg format at large image size (3888 
x 2592). Other camera specifications can be found at Canon 
Digital Rebel XTi White Paper (URL 1). 
After, conditions were tested with colorful irrelevant image to 
the medical application; another two different test images that 
depict the same skin disorder at different sizes were also 
acquired. Figure 3.2 shows two different shutter speed 
conditions on “Test Image#1”. 
     
    
    
   
   
   
   
   
   
   
   
   
   
   
    
   
   
    
    
  
   
    
    
    
   
  
  
   
  
    
   
  
  
    
   
    
   
    
    
     
  
    
    
  
  
  
Figure 2. Medical test images#l (a) SS: 275, AV:32, WB: 
AWB; (b) S S: 2”, AV: 32, WB: AWB 
4. IMAGE ENHANCEMENT 
Since the illumination condition has the major effect on the 
appearance of the subject, light conditions need to be 
standardized in order to use the method in every environment. 
For this reason, in order to reduce the effect of the illumination, 
homomorphic filter was applied to images. It is a multiplicative 
filter that affects a lot with the images’ intensity. Homomorphic 
filter is based on the idea that optical images have two 
components which are luminance and reflectance. Poor contrast 
images can be enhanced by straining the light source and 
increasing the reflectance at the same time (Al-Amri et al, 
2010). Since the Fourier Transform is suitable to be used when 
the noise can be modelled as additive term to the original image 
values, defects like uneven lighting, needs to be modelled as 
multiplicative term. As a combination of illumination and 
reflectance an image can be modelled below (Matthys, 2001). 
f(x,y) = i(x,y).1(x,y) (1) 
Adelmann (1998) states that frequency-domain filtering of 
images serves as both multilateral and strong tool but, 
illumination and reflectance components of an image cannot be 
operated differently in the frequency domain, because as seen in 
equation (1) above the two mentioned components are in 
multiplicative form and not separable. In order to apply Fourier 
Transform, multiplicative equation must be converted to an 
additive form. 
Figure 3 shows the flow chart of homomorfic filter which are a 
logarithmic operation for converting equation (1) into additive 
form, taking the FFT of both sides of logarithmic equation, 
applying the suitable filter function (H(u,v)), then taking the 
inverse of FFT and lastly taking the exponential of both sides 
respectively. 
f zd Fafa) pr JE? £y) 
Figure 3. Flowchart of homomorphic filter (Adelmann, 1998). 
  
  
  
  
  
  
  
  
  
  
Homomorphic filter was applied with “Astra Image 3.0 Pro" 
Software. Figure 4.2 shows the homomorphic filter applied with 
Astra Image 3.0 Pro with original data. As seen from the figure 
4.2, homomorphic filter provides clearer image around skin 
artifact. 
   
  
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