# Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection
## 2 Materials and Methods
### Image Sharpness and Resolution  
Sharpness determines the amount of detail an imaging system can reproduce, and is a critical image quality factor. Sharpness is defined by the boundaries between zones of different tones. The slanted-edge method of measuring the SFR, as an approximation of the MTF, has become a popular image quality testing method, and multiple international standards have adopted this method, including ISO 12233 and IEEE. The eSFR is based on the frequency response of a slanted edge. With the 10-90% rise distance technique, the sharpness of a slanted edge can be determined by the distance of a pixel level between 10% and 90% of its intensity transition. The limitation of this method rests in the absence of the possibility to decompose the rise distance of a complete imaging system into the rise distances of its components. This is possible using the frequency domain method, where the frequency is measured in cycles or line pairs per distance. The relative contrast at a given spatial frequency is called MTF, which is the same as the SFR, and is a standard method of measuring sharpness. The shape of the SFR depends on the transfer function of the optical system, sensor technology and, therefore, on the image processing in the camera. A schematic diagram of the edge-based method is presented in Figure 4. The slanted edges in the eSFR ISO test chart have 5\({}^{\circ}\)-7\({}^{\circ}\) angles, and the region of interests (ROI) was represented by the rectangle with a slanted edge passing through the short sides, as is illustrated in Figure 4a. The edge angle in a vertical ROI was estimated with a linear regression on the row-to-row edge locations by taking a one-dimensional derivative of the edge gradient in each row. The ROI pixels were then projected onto the horizontal axis. The four-times oversampling was used for minimising anti-aliasing filter problems. The result is a one-dimensional edge spread function (ESF), as presented in Figure 4b. The approximation of the derivation of the ESF was realised with the finite-difference filter, and, after applying a Hamming window, we were able to calculate the discrete Fourier transform (DFT). Finally, the MTF presented in Figure 4c was estimated as the normalised value of the complex modulus of a discrete Fourier transform. MTF is the Fourier transform of the impulse response that is the derivative of the edge response. The MTF of the sampled image system is a product of the MTF's system components, and is a trustworthy measure for image resolution and sharpness. Spatial frequency is measured in cycles or line pairs per distance, and is the measurement used to determine how much detail a camera can reproduce. The authors used different units for spatial frequency, for example,  
Figure 3: (**a**) Colours of six patches represent human reference skin colours. (**b**) A slanted edge micro-test chart as applied when sharpness is measured from the target. cycles/pixel, cycles/mm and line widths/picture height (LW/PH). The last choice is appropriate when the digital sensor sizes vary widely from small in camera phones to large for full-frame professional cameras. In [46], the author has argued that the MTF50 value of the frequency response correlates well with the perceived sharpness. The MTF50 is the spatial frequency where contrast drops to half (50 percent) of its low-frequency value [47]. Similarly, MTF50P is the spatial frequency where contrast drops to half of its peak value. The authors used both parameters for comparing the sharpness. MTF50P is better than MTF50 for highly sharpened images. Over-sharpening or under-sharpening is the degree to which an image is sharpened relative to a standard sharpening model. Image sharpening is realised mostly with image processing camera software. We calculated the over/under-sharpening degrees following the Imatest recommendations [40]:  
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