# Segmentation of Acne Vulgaris Images Techniques: A Comparative and Technical Study
## 3 Results
### Segmentation Methods Based on Classical Image Processing Techniques
YCbCr model is also utilized in the segmentation methodology implemented in [10], where a hardware image capture system for Smartphones and an Android application are developed. This application detects and classifies several dermatological conditions, including the following: spots, wrinkles, and acne. To identify acne, a thresholding usingCb values (which was observed by the authors to be high for acne lesions) is performed. The proportion of acne points in ROI is calculated. It should be noted that the system can acquire fluorescence images, but they are not used in segmentation. There are some studies that do not use any color space conversion regarding the segmentation of acne vulgaris images. In 2012, the authors of [9] used template matching on acne vulgaris images for the first time. This technique consists of overlapping a template and an image region and calculating their correlation using different statistical parameters (in this case, the chi-squared test was calculated) so that it is possible to locate acne lesions by scanning the image with the template. The template is generated by training an algorithm using acne images (average images). In addition, in [11], a segmentation method with the aim of counting acne points automatically is developed. The authors only use the RGB channels of the image. The first step of the algorithm involves the estimation of the relation between the image pixels and the real length (in mm) they represent. Then, the identification of lesions can be performed as follows:  
Detection of papules and nodules is performed by filtering and binarizing B-R and G-R components of the original images. The resulting objects are classified into papules or nodules according to the length of their main axis. Closed comedones are an elevation on the skin surface, and this results in illumination changes in the images. Hence, templates based on illumination changes are created to detect them. Then, the correlation between these templates and the B component of the images is analyzed. Finally, an artificial neural network (ANN) is used to determine which of the points of the binarized image are true closed comedones. Open comedones are detected using B-R and G-R components and a series of operations, including the following: contrast increase, image complement, hair elimination, image opening (to eliminate artifacts), contrast adjustment, smoothing, binarization, pruning, and cropping. An ANN is used as in the previous case. The methodology used for the detection and segmentation of papules and nodules determined the classification of the work in Classical Image Processing Technique. In [12], [PERSON] proposed a segmentation method for conventional photography based on an iterative algorithm that minimizes energy function. This is color-based segmentation. The morphology of the objects is analyzed to eliminate non-acne formic points. By means of normalized maps of the distribution of skin chromophores, lesions can be classified into inflammation and hyperpigmentation. There are two segmentation methodologies implemented for fluorescence images. [PERSON] et al., [17] used a Wood's lamp in order to obtain fluorescence images and proposed a color-based segmentation method. In a later study [16], they used fluorescence images to perform automatic acne lesion counting. In this method, images are acquired using VISIA-CR commercial system (Canfield Scientific, Inc.; Parsippany, NJ, USA). Subsequently, images are converted to grayscale, and an increase in contrast via histogram equalization is observed. Then, an extended maxima transform is applied. The authors evaluate the ability of the algorithm to count the lesions, but not the previous image processing. In [18], they captured images of a small region of skin with a phone LED microscope. The images captured were color images. First, they detected the disease lesions using color image processing techniques and a color gradient segmentation. In a second step, they applied morphological operators to extract the blackheads shape.