# Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection
## 2 Materials and Methods
### Colour Reproduction  
Colour reproduction is considered to be one of the most significant image quality parameters of a digital camera. Even slight deviations in colours captured in photographic images from actual colours can be critical in medical photography, especially in the field of dermatology [23]. The majority of human viewers prefer enhanced images to an image with an exact colour representation [31]. To satisfy viewers' desires, some parameters, like saturation, are often modified by camera manufacturers, to increase the intensity of image colours to make final results more \"pleasant\". Therefore, the camera manufacturers characterise their sensor technologies to determine the best final image colour accuracy [30]. There is always a difference between \"true colours\" and colours captured during the photographic process [18]. This colour difference that occurs because of the reproduction quality can be measured.  
The International Commission on Illumination (CIE) has defined a system that classifies colour according to the human visual system [30]. Colour differences can be expressed in any space, but they make practical sense only for more uniform spaces where the resulting numbers can be associated with what the eye perceives. The CIE RGB (R-Red, G-Green, B-Blue) and CIE XYZ (X matches to luminance, while X and Z give colour information) were the first defined colour spaces. CIE XYZ is derived from CIE RGB, where XYZ are the extrapolation of RGB to avoid negative numbers called tristimulus values [41]. These are device-dependent colour spaces, where the values are dependent on the specific sensitivity function of the imaging system. With the device-dependent model, it is challenging to relate differences between colour with the perception on such colour differences by a human [32]. CIE proposed two other colour models, CIE L*a*b* and CIE L*u*v*, which are device-independent models. In the CIE L*a*b* uniform colour space, the perceived luminance L* defines lightness in colour,  
Figure 4: Illustration of slanted edge-based modulation transfer function (MTF) estimation process: (**a**) selection of a region of interest (ROI), (**b**) normalised edge spread function (ESF) and (**c**) estimated MTF.  
where a* represents colour relation in a red-green, and b* represents colour relation in yellow-blue in the chromaticity coordinates [16].  
A standardised test chart with known colour patches and their CIE L*a*b* values is used to capture images. The CIELAB colour space is based on human vision, and allows the computation of a colour difference. The colour accuracy of an individual colour patch from the test chart is then evaluated by the CIELAB colour difference between the encoded colour in the image and the colour reference data. The difference between two colours, colour distance \(\Delta E^{*}_{ab}\), is calculated as the geometric difference between two colour points \(a\) and \(b\) in the CIELAB three-dimensional colour space as follows [42]:  
\[\Delta E^{*}_{ab}=\sqrt{\left(\Delta L^{*}\right)^{2}+\left(\Delta a^{*} \right)^{2}+\left(\Delta b^{*}\right)^{2}}, \tag{2}\]  
where an individual colour is defined with the lightness (\(L^{*}\)) and the hue (\(a^{*}\) and \(b^{*}\)) coordinates. The chroma \(\Delta C^{*}_{ab}\) difference and the lightness difference \(\Delta L^{*}\) between two colours can be determined:  
\[C^{*}_{ab}=\sqrt{\left(a^{*}\right)^{2}+\left(b^{*}\right)^{2}}, \tag{3}\]  
\[\Delta C^{*}_{ab}=C^{*}_{ab1}-C^{*}_{ab2}, \tag{4}\]  
\[\Delta L^{*}=L^{*}_{1}-L^{*}_{2}. \tag{5}\]  
The mean colour distance \(\Delta E_{mean}\) is then calculated from 16 (six patches are used for dermoscopy images) colour patches \(\Delta E^{*}_{ab}\) values.