# Segmentation of Acne Vulgaris Images Techniques: A Comparative and Technical Study
## 3 Results
### Segmentation Methods Based on Machine Learning Techniques Algorithms  
K-means is the most common machine learning algorithm used to segment acne vulgaris images. In [21], the authors opt for this algorithm with k = 3. The methodology proposed by [PERSON] et al., in [23] is based on fuzzy c-means (FCM) segmentation on several color spaces and the intelligent selection of the desired cluster by using a function. Results showed that the optimal number of clusters is three, and the best segmentations results were obtained with I3 and Q components. These results are similar for both channels, because the way they are calculated from RGB space is similar. The features that can be extracted when implementing machine learning algorithms are diverse. Usually, information is obtained from the images, but there are also studies that analyze the convenience of using other types of features. In [24], [PERSON] et al., use cross-polarized light images to monitor acne lesions considering both spatial and temporal features (the latest ones are their main contribution). In this method, to distinguish acne lesions from healthy skin, a logistic regression classifier is used. In [25], [PERSON] et al., develop a system that detects different dermatological pathologies, and acne is one of them. They use not only visual information (from conventional images) but also data from the medical record, among other things. The pre-processing and ROI detection phase separates healthy skin from the affected skin. This is performed by processing the color gradient of the image and performing thresholding and k-means. Finally, an ANN is used to classify the lesions. [PERSON] and [PERSON] in [26] propose a segmentation method to detect both acne points and spots. To detect acne lesions, they use the Cr channel from YCbCr color space. Histogram thresholding is performed to detect potential skin defects on images. These defects are classified into acne, spots, or healthy skin by texture feature extraction and SVM classification. As previously mentioned, the authors of [33] published a study wherein they describe three different methodologies for the segmentation of acne vulgaris images. The first one involves color-based segmentation using the k-means clustering code from Matlab [35]. They subsequently convert RGB images to CIELAB color spaces and perform clustering twice. The first execution of k-means has to be k = 2. A second k-means is executed on the resulting image but with k = 3. This is performed to reduce error in the first segmentation since second clustering divides pixels in healthy skin, acne points, and healthy skin that is wrongly assigned to the acne cluster in the first segmentation. The second methodology of [PERSON] et al., [33] is based on texture analysis. The authors affirm that the dynamic range of acne pixels is larger than the one of normal skin pixels. They do not provide more information about this method. The last method is their own version of color blob utility with automatic thresholding and tolerance calculations from Matlab [36]. They use HSV color space to perform color-based segmentation. The authors affirm that the segmentation method that provided best results was the one based on k-means clustering. The results for the other methodologies are not provided. It should be noted that [PERSON] tried to use watershed segmentation and multi thresholding, but the results were not satisfactory. In [27], [PERSON] et al., in collaboration with Microsoft, developed a convolutional neural network algorithm to classify the lesions that appear in selfie images into five classes. They used 4700 images, but ultimately, they only obtained a classification with or without acne skin. Following the same line as the above study, in [29], [PERSON] et al., used a total of 472 color photographs to apply deep learning techniques and demonstrate the potential for automated clinical image analysis and grading.