## 3 Thermographic Techniques for Skin Cancer Diagnosis
### Steady-State Thermography  
The examination of eleven subjects affected by BCC through steady-state thermography was reported by [PERSON] et al. [26]. The infrared images were recorded using an SAT-S160 infrared camera with a low optical resolution (160 \(\times\) 120 pixels), a temperature accuracy of \(\pm\)2%, and a temperature resolution of 0.1 \({}^{\circ}\)C. The camera was placed 1 m away from the patient's skin. However, no information about the acclimatization time was reported in [26]. Dimensionless temperature gradients, defined as [27], were measured between two symmetric regions of the patient's body (see Figure 1) in order to compensate the temperature readings with respect to variations in both the metabolism and ambient temperature. The authors concluded that their methodology was capable of detecting BCC in all examined cases, despite the low resolution of the instrumentation used. A clinical study aimed at establishing the feasibility of distinguishing between benign and malignant lesions using IR thermography was reported by [PERSON] et al. [28] in which 74 patients above the age of 18 were involved. The lesion diameter was used to classify 251 lesions, previously identified using different diagnostic tools, such as clinical diagnosis and tissue biopsy. The sensitivity (percentage of detected true positive) and specificity (percentage of detected true negative) of infrared imaging were computed for each class of diameters. Lesions of a diameter greater than or equal to 1.5 cm yielded the highest sensitivity, while smaller lesions had the lowest sensitivity [28]. [PERSON] et al. also reported  
Figure 1: Infrared image of a patient exhibiting a lesion on his face, from Ref. [26]. Note that two symmetric regions were marked with blue circles. (permission asked and obtained from Elsevier, Amsterdam, The Netherlands). high specificity values for all lesion sizes. However, no information about the image acquisition procedure was described in this work. In 2015, [PERSON] et al. [29] showed the feasibility of identifying malignant skin lesions through steady-state thermography by observing the thermal images of 58 patients affected by neoplasms (see Figure 2). The subjects were left to reach a sufficient thermal equilibrium with the air contained in an acclimatized room at 21 \({}^{\circ}\)C and a relative humidity less than 50% for 15 min before the thermal acquisition. An FLIR A325 sc camera having a resolution of 320 \(\times\) 240 and a precision of 70 mK, placed 80 cm away from the patient, was used to record the thermal images. The authors were able to distinguish benign from malignant skin cancers by computing the thermal contrast between two rectangular regions of interest over the neoplasm region. To discriminate between benign and malignant skin lesions, [PERSON] et al. [30] registered and analyzed steady-state thermal images of clinically similar lesions. The authors compared 100 IR images of BCC with 100 intradermal nevus, 35 images of SCC with 35 actinic keratosis, and 20 melanomas with 20 pigmented seborrheic keratosis. The infrared images were recorded through a FlukeVR FLK-Ti400 IR camera with a resolution of 320 \(\times\) 240 pixels, a thermal sensitivity of less than 0.05 \({}^{\circ}\)C, and precision of \(\pm\) 2 \({}^{\circ}\)C. The IR camera was placed 15 cm away from the lesion, and thermal images were recorder after the patient was hosted for 10 min in an examination room at temperature of 22 \({}^{\circ}\)C. Each thermal frame was analyzed in a MATLAB(r) environment to compute several metrics to be used within a support vector machine classifier to discriminate the lesions. Thermal images showed that actinic keratoses and SCC exhibit clearly distinct average temperatures, while the other pairs of lesions show similar temperatures. However, these similar lesions were successfully discriminated against by the support vector machine classifier using their thermal images.