# Segmentation of Acne Vulgaris Images Techniques: A Comparative and Technical Study
## 3 Results  
Figure 1 shows the search results. Most of the publications were excluded because they were not relevant to the topic (no segmentation methods described or segmentation of images of other pathologies). The number of excluded publications can be explained by the search terms used, which were broad to prevent relevant studies from being excluded. The screening was manually performed by reading the title and abstract of articles. Only six of  
\begin{table}
\begin{tabular}{c c} \hline Population & Humans \\ \hline Intervention & Segmentation of face images of acne vulgaris patients \\ \hline Comparison & Manual segmentation \\ \hline Outcome & Diverse results \\ \hline \end{tabular}
\end{table}
Table 1: Searching strategy according to the PICO model.  
the one-hundred records excluded after screening by reading the title and abstract met the exclusion criteria. Special attention was paid to these records and also no segmentation method was described in the abstract. If those publications were considered relevant, the Polytechnic University of Valencia has a special service for specific queries to studies with restricted access.  
Figure 1: PRISMA flow diagram for the conducted research. Additional records refer to those studies found through the systematic research conducted on the references of the studies found in the databases. Both records and full text articles excluded were done so according to one or more exclusion criteria.  
A total of 27 studies were finally included in the systematic review.  
In [5], [PERSON] et al., explain that algorithms based on different techniques are used to segment medical images. Globally, these image segmentation techniques studies can be grouped into Classical Image Segmentation Techniques and Machine Learning Segmentation Techniques.  
Based on this global classification, the studies were summarized in two different tables. One table includes the studies of segmentation methods based on classical image processing and the other table includes the studies of segmentation methods based on machine learning techniques. Both tables had the following attributes: Study Name, Image Modality, Image database, Proposed Segmentation Method, Ground Truth, Samples for the validation, Relevant limitations, and Outcomes.  
As shown in Figure 2, there is a clear definition of Image Segmentation Techniques classification.  
In general, as mentioned above, the analyzed studies can be divided into two groups: those with segmentation methods based on classical image processing techniques [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19] and the ones based on machine learning segmentation techniques [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. The first ones consist of a series of steps or operations (such as contrast adjustment, color space conversion, thresholding) that need to be subsequently applied to the image to segment it.  
There are also a couple of studies [30; 33] that develop three different segmentation methodologies, two of which are based on classical image processing techniques, and the other one uses machine learning algorithms. Some of the publications included in the systematic review not only perform segmentation but also lesion classification with machine learning techniques. In addition, there are studies in which the limit between lesion segmentation and classification is not clearly defined. In those cases, both techniques  
Figure 2: Taxonomy of Image Segmentation Techniques [5].  
are mentioned because classification is also relevant. Except for these studies, in the present work, the methods are grouped exclusively according to the segmentation technique.