# Segmentation of Acne Vulgaris Images Techniques: A Comparative and Technical Study
## 3 Results
### Segmentation Methods Based on Classical Image Processing Techniques  
It is common for most of the segmentation methods based on classical image processing to convert original RGB color spaces into another one, usually CIELAB or YCbCr. In [7], the authors worked with standard photographs that were converted into gray images (since they were easier to process). To obtain the region of interest (ROI), the authors subtracted the V component of HSV model (obtained from RGB original image) to the normalized gray image. The resulting image enhances the contrast between healthy skin and acne lesions. Afterwards, thresholding with an experimentally obtained threshold value was performed. Later, the authors introduced the use of a Bayes classifier to reduce the errors by training this classifier to distinguish between lesions and noise [34]. HSV color space is also used in [13]. The authors of this study developed a Windows application that detects acne lesions on conventional photographs. To achieve this, the input image needs to be converted to grayscale in order to detect the subject's face and eliminate the image background. Once the ROI is obtained, the application generates a heat map in HSV color space using the G component of the original RGB image. The G (green) channel is chosen because the authors maintain that, with this component, the contrast between acne lesions and healthy skin is maximized. Then, adaptive thresholding on the G component is performed to demarcate lesions using blob detection. Determining the proper threshold value is crucial since it influences the segmentation results. Hence, HSV space is used to create a heat map that shows acne lesions, but this is not necessary to perform the segmentation. In [6], [PERSON] et al., uses CIELAB color space. The authors support that this is the most convenient color space to analyze color distribution on the skin. Therefore, the first step of their segmentation method involves the conversion of conventional RGB photographs to CIELAB. Then, [PERSON]'s method is applied to the image to classify pixels into three groups: normal skin, acne lesions, and scars. In another study [15], the authors implemented a method based on the generation of a heat map, on which (after failing to apply global thresholding) adaptive thresholding is applied to distinguish acne and healthy skin. The heat map is created using a* component of CIELAB space (this component emphasizes red areas, enhancing the visualization of inflamed areas above healthy skin). The algorithm offers the possibility to manually choose the threshold value, because adaptive thresholding did not work in every image. Finally, blob detection is performed using the Laplacian of a Gaussian filter. The authors affirm that the algorithm localizes and counts the lesions. However, these results are neither shown in the publication nor quantified. In the study developed in [8], they opt to increase dynamic range on YCbCr images in order to augment contrast and enhance lesions visualization. This is achieved by modifying the luminance component. Y. Segmentation is performed on RGB modality so that the sequence is as follows: RGB to YCbCr, contrast adjustment, processed YCbCr to RGB. The mapping of RGB image pixels to a visible spectrum is proposed as a way of visualizing little color changes (spectrum 380-720 nm range is obtained with a band separation of 1 nm). This is the only study that suggests RGB to visible spectrum mapping. Finally, the classification of pixels is performed: red, purple, and brown lesions and healthy tissue or specular reflection. Classification is based on Mahalanobis distance--each pixel is assigned to the nearest class (based on spectrum wavelength). This could be considered machine learning, but this study has been included in the classical methods group because the final objective is to obtain a binary mask to demark acne lesions using thresholding. Moreover, in [8], no machine learning algorithm was applied.