# Image Quality Assessment of Digital Image Capturing Devices for Melanoma Detection
## 1 Introduction
They can be measured by a subjective comparison of images as in [16], or by using a less accurate 1951 USAF resolution test chart [18]. In this research, we used a slanted-edge method of measuring the SFR that is adopted by several international standards, including ISO 12233 and IEEE [19; 20; 21; 22]. Resolution and sharpness were measured on images from a reference target as captured, similar to those acquired in everyday dermatological practice (i.e., full-body, close-up and dermoscopy imaging) [23; 24]. A micro-test target was designed to measure sharpness and resolution according to the eSFR method for the dermoscopic image. Dermoscopy images were acquired using a handheld dermoscopy device attached to either a smartphone or a high-resolution camera. Results were then compared with the results obtained on a digital dermoscopy system, which is considered as the gold standard. Colour assessment is essential for the clinical diagnosis of skin cancers [6; 25; 26; 27; 28; 29]. However, images captured with different cameras and dermoscopes often present problems of accurate colour reproduction [30]. The colours of these images are different from those seen with the naked eye, and usually also vary from camera to camera. The reason for this is that the auto image processing software built into the cameras is usually designed to generate more saturated colours, pleasant to the broadest population of users [31]. However, the wrong colour and too intensive sharpening are likely to produce artefacts or border contour changes, which can result in less effective melanoma screening, longer diagnosis times, and potentially wrong decisions [16]. Since this is not acceptable, the colour accuracy and constancy of cameras must be improved. They can be provided with a colour calibration procedure [18]. The colour calibration is researched broadly in the computer vision community, and several methods have been proposed to solve the problem [32; 33; 34; 35; 36]. It can be solved either with hardware or software-based calibration algorithms. In the dermatology context, the authors in [32] used internal camera parameters (e.g., colour gain, camera aperture, camera offset) to calibrate image colours. The calibration procedure is based on 24 patches with known colour properties. The colour transformation matrix is computed between camera corrected R'G'B' values, and a standard device-independent XYZ colour space, using the values with 24 known colour properties. [PERSON] et al. [33] provide a complete workflow for dermatology image calibration using a Fotofinder video dermoscope, where a process was proposed to correct non-uniform illumination of the instruments. A dermoscope illuminates the central part of the images differently than the edges. The proposed methods perform colour calibration using 24 colour patches from the Greytag Macbeth ColorChecker chart. [PERSON] et al. [34] proposed another method to increase the accuracy of colour reproduction in dermoscopy imaging, based on exploring the spectral information of the dermoscopy lighting source. The method is devised for RAW images, but a simple solution has been presented for digital cameras producing JPEG images. The authors in [35] developed an automated calibration method for dermoscopy images using the HSV colour system. The colour calibration filters were developed based on hue, saturation and intensity, using a set of 319 dermoscopy images. The critical future of the presented method is that colour calibration can be performed on image content alone. [PERSON] et al. [36] investigated the influence of colour calibration on the performance of melanoma detection. To improve the classification of multisource images, they compared four colour constancy algorithms to estimate the influence of different light source illumination options. The colour accuracy for all devices in the test remains an issue, mainly when cameras are operating in auto mode.