Dr. Ricardo Soto Catalan

Pelagic fish have evolved specialized biogenic multilayer reflectors composed of stacks of intracellular anhydrous guanine crystals separated by cytoplasm, giving notorious silvery appearance to their skin. While the reflective properties of guanine crystals and their utility for fish camouflage have been shown in other fish species, this is the first evaluation on fish species from the southern hemisphere, and from the Humboldt current system. This is one of the most productive systems on earth, having particular oceanographic conditions such as upwelling, and thus under strong selection pressures. In this study, we conducted a comparative analysis of four pelagic species, Sardine, Anchovy, and Snoek, known for their silvery characteristics, and Mote sculpin, which lacks silvery features. We aimed to explore the biological mechanisms underlying light reflectivity in fish species and to understand how fish skin microstructures affect whole fish light reflectance and intensity in the visible spectrum. We measured the reflectance of individual fish using hyperspectral imaging and characterized the guanine crystal/cytoplasm layers within the skin of each fish using high-resolution scanning electron microscopy. These Scanning Electron Microscopy (SEM) images were analyzed using the 2D discrete Fourier transform to extract the spatial patterns that govern the light interaction with the guanine crystal structures. A novel spatial frequency analysis approach applied to SEM images explained reflectance differences between species with similar spectral behavior. Furthermore, this study presents the first fish classifiers based on the analysis of spatial frequency features, achieving up to 92.14% accuracy using a K-Nearest Neighbors classifier, highlighting the functional and taxonomic relevance of guanine microstructure organization. Our findings confirm, on pelagic fish species from the Humboldt current system, that silvery species have a chaotic distribution/arrangement of guanine crystals, whereas non-silvery species have a more organized arrangement. Accordingly, Fourier analysis indicated that silvery fish are capable of scattering light uniformly across the visible spectrum. In contrast, the Mote sculpin shows a stronger scattering of red light, distinguishing it from silvery fish.

Skin cancer is a growing global concern, with cases steadily rising. Typically, malignant moles are identified through visual inspection, using dermatoscopy and patient history. Active thermography has emerged as an effective method to distinguish between malignant and benign lesions. Our previous research showed that spatio-temporal features can be extracted from suspicious lesions to accurately determine malignancy, which was applied in a distance-based classifier. In this study, we build on that foundation by introducing a set of novel spatial and temporal features that enhance classification accuracy and can be integrated into any machine learning approach. These features were implemented in a support-vector machine classifier to detect malignancy. Notably, our method addresses a common limitation in existing approaches-manual lesion selection-by automating the process using a U-Net convolutional neural network. We validated our system by comparing U-Net's performance with expert dermatologist segmentations, achieving a 17% improvement in the Jaccard index over a semi-automatic algorithm. The detection algorithm relies on accurate lesion segmentation, and its performance was evaluated across four segmentation techniques. At an 85% sensitivity threshold, expert segmentation provided the highest specificity at 87.62%, while non-expert and U-Net segmentations achieved comparable results of 69.63% and 68.80%, respectively. Semi-automatic segmentation lagged behind at 64.45%. This automated detection system performs comparably to high-accuracy methods while offering a more standardized and efficient solution. The proposed automatic system achieves 3% higher accuracy compared to the ResNet152V2 network when processing low-quality images obtained in a clinical setting.

A novel method, to our knowledge, for monitoring weightlifting exercises based on infrared imaging is proposed in this work. For the infrared workout weightlifting recorded scenes, radiometry and artificial intelligence were employed for in-scene temperature and biomechanical athletes’ body parts position mapping. Our method was effective in monitoring muscle exertion during high-performance athletic exercises, as evidenced by the results obtained from real athletic datasets. The method generates a color-labeled sequence of thermal images and reports on body part positions, which can be used by judges and trainers to guide athletes toward safer and more efficient practices.

Infrared thermography is considered a useful technique for diagnosing several skin pathologies but it has not been widely adopted mainly due to its high cost. Here, we investigate the feasibility of using low-cost infrared cameras with microbolometer technology for detecting skin cancer. For this purpose, we collected infrared data from volunteer subjects using a high-cost/high-quality infrared camera. We propose a degradation model to assess the use of lower-cost imagers in such a task. The degradation model was validated by mimicking video acquisition with the low-cost cameras, using data originally captured with a medium-cost camera. The outcome of the proposed model was then compared with the infrared video obtained with actual cameras, achieving an average Pearson correlation coefficient of more than 0.9271. Therefore, the model successfully transfers the behavior of cameras with poorer characteristics to videos acquired with higher-quality cameras. Using the proposed model, we simulated the acquisition of patient data with three different lower-cost cameras, namely, Xenics Gobi-640, Opgal Therm-App, and Seek Thermal CompactPRO. The degraded data were used to evaluate the performance of a skin cancer detection algorithm. The Xenics and Opgal cameras achieved accuracies of 84.33% and 84.20%, respectively, and sensitivities of 83.03% and 83.23%, respectively. These values closely matched those from the non-degraded data, indicating that employing these lower-cost cameras is appropriate for skin cancer detection. The Seek camera achieved an accuracy of 82.13% and a sensitivity of 79.77%. Based on these results, we conclude that this camera is appropriate for less critical applications.

Skin cancer is one of the most common types of cancer, whose number of cases is constantly increasing. The most used method to detect skin cancer is the biopsy. It is relevant to reduce the number of biopsies, since it is an invasive and expensive procedure, and it has limited availability in some locations. Among the most successful approaches that aim to improve skin cancer detection are the algorithms that process active infrared thermography.Here, a skin cancer detection scheme is proposed, which extracts key features from active thermography videos, and uses them in the following five classifiers: K-Nearest Neighbors, Decision tree, Random forest, Support vector machine (SVM) and eXtreme Gradient Boosting (XGBoost). Under a minimization error design criteria, the best result was performed by a SVM classifier, reaching 84.14% of accuracy and 78.92% of precision. Modifying the classifiers to ensure that all the malignant cases are detected, the best performance was also achieved by the SVM classifier, with 72.85% of accuracy and 63.95% of presicion.The proposed scheme is 15% less accurate than the best detection algorithm. However, it is easier to implement and deploy and provides a framework with key preprocessing aspects to address this detection problem using active thermography. As future work, a further exploration of features will be carried out, with the aim of improving the performance of the classifier.