## 3 Material and Methods  
MATLAB software version 2018b licensed by the Universitat Politecnica de Valencia was used to process the images and implement the methodology for the automatic selection of the optimal value of the sensitivity factor. Hardware was PC Lenovo L380 Yoga.  
The processed images were in raw format and the color model was monochromatic. The images were taken by illuminating the skin with ultra-violet light that activated the fluorescence of the lesions [29]. The fluorescence of infected lesions is caused by the bacterium _Propionibacterium Acnes_[30].  
The images were taken with a SCHOTT RG610 high-pass optical filter placed in front of the sensor. The sensor was an IphoneX camera. As we only recorded the signal in the red range, the images were taken in monochromatic mode.  
The groundtruth images used by the manual evaluators to count the points were printed on a Ricoh Aficio MP2550 laser printer with monochrome printing mode and 600 \(\times\) 600 dots per inch resolution.  
The manual dot counting was carried out by five different evaluators from the Centre for Research in Graphic Technologies of the Universitat Politecnica de Valencia.  
The methodology is described with reference to one of the images analyzed, Figure 1.  
A small algorithm has been created that works in the form of a loop that for each image to be processed, applies consecutive sensitivity factors with an increment of 0.01, and for each one of these it registers the number of points detected in the binary image that the function automatically creates to carry out the count. The final output of the algorithm is a file with the number of points associated with each sensitivity factor for which the circular structure count has been performed. The algorithm will work for sensitivity factors between 0.81-0.82 up to a maximum of 0.96. Sensitivity values (S\({}_{\text{factor}}\)) exceeding the limits of the range (0.81-0.96) have in no case (previous tests) given valid counts.  
As mentioned, with the CHT algorithm implemented in MATLAB, the image will be processed by giving the sensitivity factor S values from 0.82 to 0.96 in 0.01 increments. As a result, a binary image (BW) will be obtained from which the number of identified points will be counted.  
To count the number of elements identified by the algorithm, the function implemented in MATLAB bwlabeln is used, the input parameter of the function being the BW image obtained from imfindcircles.  
The number of points detected associated with each sensitivity S\({}_{\text{factor}}\) is recorded. With the different values of the number of points obtained, a new parameter (W) is calculated.  
For each image, the algorithm is evaluated under different S\({}_{\text{factors}}\). Table 1 shows the number of points for different S\({}_{\text{factor}}\). Then, for each S\({}_{\text{factor}}\) that the algorithm evaluates, we obtain a number of points and a W. For each image evaluated, we obtain a set of data that  
Figure 1: Image of detection of infectious presence of _Propionibacterium acnes_.  
are represented in the fitting model and from this model we choose the value of the \(\text{S}_{\text{factor}}\) under which the algorithm obtains a better count of the infected points.  
For example, in Table 2 we can see how for each \(\text{S}_{\text{factor}}\) a number of points and a W are associated.  
\[W=\frac{NP_{i}}{NP_{max}-NP_{i}} \tag{4}\]  
\(NP_{i}\) = number of points for each sensitivity value.  
\(NP_{max}\) = maximum number of points for maximum sensitivity value.