# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
## 3 Image Acquisition, Preprocessing, and Postprocessing  
Most CW images are taken with a digital camera, smartphone, or RGB-D camera. New methods should be robust enough so that the images could be acquired under normal lighting conditions, without special adjustment of the wound, special instruments, or some special capturing conditions. In a study by [PERSON] et al. [46], the authors developed an image capturing device in the shape of a box that consisted of two mirrors placed inside the box at the angle of 90\({}^{\circ}\). The foot was placed on the surface of the box for image capturing. The aforementioned image acquisition is not suitable for wounds on other parts of the body and without image capturing experience it cannot be used elsewhere. As a rule, the user should not be particularly familiar with the proposed analysis method so he can be objective in wound acquisition [37]. The preprocessing stage is fundamental for the majority of the methods because images must be properly adjusted in order for wound segmentation or tissue classification to be more effective. The most used methods are:  
* Conversion of color spaces Images captured by a camera or smartphone are typically saved in RGB color space which is not an ideal color space for analyzing color features. Two visually close colors for the human eye may be more separated in the color space than two other colors that are visually more distant [47]. Most of the considered research convert to some of the following color spaces:
* HSV or HIS Hue, saturation, and value, or intensity color space were used in [37; 48; 49; 50]. YCbCr Luminosity component and blue-difference and red-difference chroma components. [PERSON] et al. [51] concluded that there is a clear difference between the skin and the background in the Cr channel. * YDbDr Similar in composition to YCbCr. [PERSON] et al. [52] and [PERSON] [11] showed that in this color space contrast between wound and background is highest. CIELab Color space where numerical change in its channels values roughly corresponds to the same amount of visually perceived change. Used by e.g., [46; 53; 54]. RYKW Red-Yellow-Black color space for determining the tissue types used in [55]. * also called CIELUV Color space that attempts perceptual uniformity, defined by the CIE as a transformation of the 1931 CIE XYZ color space, used by [56]. * Grayscale Conversion from RGB image to grayscale is typically performed by the Equation (6) and has been used in [41; 57; 58]. * \[Grayscale=0.2126R+0.7152G+0.0722B\] (6)  
Research like that of [PERSON] et al. [53] combines multiple color spaces, CIELab color space for wound detection, and HSV color space for healing evaluation. In [PERSON] et al. [59] authors apply a white balance method in which 5% of the brightest and darkest parts of the image are removed, while [PERSON] et al. [60] use 4 different color spaces for color feature extraction RGB, HSV, YCbCr, and YIQ. Dark red granulation tissue and black necrosis tissue have similar hue characteristics therefore, in [5] and [37] the authors modified all three channels in HSV color space for better segmentation. * Choosing the right color channel for further analysis Researchers always take the most convenient channel from color space to achieve the best performance in the proposed method. Some of the used channels are:
* Db channel--YDbDr color space [PERSON] et al. [8] chose this color channel by measuring the mean value of contrast between wound and background for 14 color spaces and all of their channels, so as [11] who evaluated that the Db channel has maximum contrast between wound and background. * Cr channel--YCbCr color space Value for skin of 135-160 with certain modifications can be easily used for wound segmentation as in [51]. * S channel--HSV color space Used by [PERSON] [49]. * Noise removal and image blurring Image denoising is an important task in digital image analysis as its role is removing white and black pixels from the image caused by shadows, camera flash, etc. while preserving the edges.