# Segmentation of Acne Vulgaris Images Techniques: A Comparative and Technical Study
## 4 Discussion  
The present systematic review aimed to analyze the existing segmentation methods for acne vulgaris images, with special emphasis on image modalities used and the validation of results. Although there are four studies [16; 17; 20; 24] which perform segmentation on other image modalities, there is a clearly preferred one--conventional RGB photography. This was used in 80% of included studies.  
The acquisition of conventional photography is easier than the acquisition of the other image modalities used (polarized light photography, multispectral images, and fluorescence imaging). However, this shows a research gap regarding segmentation for other modalities since they could provide relevant information or make segmentation easier. For instance, polarized light photography helps properly distinguish between inflammatory and non-inflammatory lesions [4]. The properties of polarized light photography and its advantages have been shown in several studies [37; 38] and could ease the segmentation process.  
With only three spectral bands, RGB cameras have a poor precision in the reproduction of color; hence, detection and distinction of each type of acne lesion is difficult because they present subtle differences in their spectral attributes. Therefore, the use of a multispectral image is a good option to perform segmentation for this pathology [20].  
Fluorescence photography clearly shows the location of acne lesions, and hence can be used to implement simple and robust color-based segmentation algorithms. The utility of fluorescence images in the evaluation of acne was proven by [PERSON] et al., in [39].  
There is a lack of uniformity in the evaluation of the results for each study. In some cases, this validation is not even provided [14; 15; 17; 20]. In works [12] and [13], quantitative evaluation was not performed.  
The results provided by [PERSON] et al., in [9] cannot be considered. Since this technique is still under development, it is not robust and presents considerable variability in sensitivity and precision amongst the images.  
The majority of authors [6; 7; 10; 11; 16; 19; 21; 22; 23; 25; 26; 28; 29; 33] validate their results by calculating sensitivity, specificity, precision, and accuracy.  
However, the validated aspects of each study differ from the rest because of the different objectives of each work (segmentation itself can be the aim of the publication or one of the necessary steps for achieving automatic lesion counting, for instance). Therefore, there are studies which do not evaluate the segmentation results (the algorithm's ability to detect acne pixels). The authors of [22] thereby evaluated the system's capability to estimate acne severity, whereas in [16], the authors validated automatic acne lesion counting. Detailed information about the evaluation of results for each study can be seen in Tables 2 and 3.  
This lack of uniformity makes the comparison of segmentation methods difficult. In most cases, more information about the validation of the segmentation phase is needed. Moreover, it can be noticed that, currently, there is no preference between segmentation methods based on classical image processing techniques and those based on machine learning algorithms because research groups continue to work with both methodologies, as the publication years of the studies show.