## 7 Post-Processing Images  
### Skin Thermodynamics  
An attempt to characterize skin lesions using infrared imaging and image processing was carried out by [PERSON] et al. [96]. Using the RGB model, the thermal images were split up into red, green, and blue components. Then, the color frequency was studied using histograms and the K-means segmentation algorithm. An analysis of the results showed that the minimum values of red and green were above 100 and below 100, respectively, while the minimum values of blue were close to zero. The authors concluded that these values can be useful as indicative markers for identifying malignant lesions, or they may be used as thresholds for classification in automated diagnostic systems.  
[PERSON] et al. [97] used image processing techniques to detect the border of skin cancerous lesions from their thermal images. Different edge detection algorithms, such as Sobel, Canny Edge Detector, Canny-Deriche, and Hysteresis thresholding, were investigated. According to the authors, the Canny Edge Detector and Hysteresis thresholding have the potential to correctly identify the lesion edges. However, the availability of larger databases of skin cancer thermal images is crucial for validating their conclusions.  
[PERSON] et al. [98] characterized thermographic images of skin lesions through a digital processing procedure involving color segmentation, color tonality-based quantitative discrimination, and analysis by histograms with the aim of identifying the thermal variations in the skin that are indicators of the presence of neoplasms. It is important to underline that thermal images were acquired using the rainbow palette and properly setting the range of temperature measurements so that temperatures higher than 37.5 \({}^{\circ}\)C were represented in shades of red. The results highlighted that skin lesions exhibit values in the red component above 100 under the RGB color space on a scale of 0 to 255 [98]. By applying the K-means segmentation algorithm to a thermogram containing a melanoma, the authors noted that the area of the lesion is characterized by a higher average value in the red component compared to the other regions. The authors concluded that, following these results, a detection algorithm for the diagnosis of skin cancer can be developed to reduce unnecessary clinical procedures.  
[PERSON] et al. [99] investigated the ability of infrared imaging to identify skin cancer lesions using artificial intelligence algorithms. The authors analyzed a data set of 298 thermograms of malignant and benign lesions to retrieve the thermal parameters to be used as inputs for two machine learning-based strategies, in particular, ensemble learning and deep learning. The deep learning strategy showed the best performances compared to the ensemble learning method (composed of two classifiers: random forest and support vector machine), and it showed good sensitivity and specificity in distinguishing between melanomas/nevi and melanomas/non-melanoma skin cancers. These results prove the potential of IR thermal parameters in the implementation of a diagnostic tool for melanomas detection. However, both tested algorithms did not show good results when non-melanoma skin cancers and benign lesions had to be classified [99]. The authors suggested using the thermal parameters derived from IR thermograms together with dermoscopic images in order to improve the accuracy of the classification method.