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Applications of Image Analysis in Thermal Sensors Imaging

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Dear Colleagues,

Thermal remote sensing technology (thermography) is the branch of remote sensing used to determine thermal properties of any objects of interest. Thermal remote sensing uses recorded electromagnetic radiation reflected or emitted from an object recorded as thermal images. Thermal images are used directly or indirectly in many application of biomedical engineering especially as potential indicators of effort or and can provide more sophisticated information about physical activity or Despite extensive research into thermal imaging for disease diagnosis, it cannot be denied that there is still a lack of standardised databases and analysis of thermal images in various disease states that could provide a useful aid to research.

Thermography uses remote sensors to determine the thermal properties of registered objects which may also be the surfase of the human's or animal's body. Additionally, this technology is non-invasive and possible remotely, which makes it advisable to test its usefulness in various applications on as much data as possible. However, besides thermal image acquisition, image processing constitutes an excellent research area, the development of which can significantly expand the available prophylactic, screening, and clinical applications of thermography in both medicine and veterinary practice.

Dr. Marta Borowska
Dr. Malgorzata Domino
Prof. Dr. Yifan Chen
Guest Editors

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Keywords

thermography
biomedical databases
image analysis
machine learning
effort monitoring
disease

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19 pages, 678 KiB

Open AccessArticle

Feature Ranking by Variational Dropout for Classification Using Thermograms from Diabetic Foot Ulcers

by Abian Hernandez-Guedes, Natalia Arteaga-Marrero, Enrique Villa, Gustavo M. Callico and Juan Ruiz-Alzola

Sensors 2023, 23(2), 757; https://doi.org/10.3390/s23020757 - 09 Jan 2023

Cited by 4 | Viewed by 1958

Abstract

Diabetes mellitus presents a high prevalence around the world. A common and long-term derived complication is diabetic foot ulcers (DFUs), which have a global prevalence of roughly 6.3%, and a lifetime incidence of up to 34%. Infrared thermograms, covering the entire plantar aspect of both feet, can be employed to monitor the risk of developing a foot ulcer, because diabetic patients exhibit an abnormal pattern that may indicate a foot disorder. In this study, the publicly available INAOE dataset composed of thermogram images of healthy and diabetic subjects was employed to extract relevant features aiming to establish a set of state-of-the-art features that efficiently classify DFU. This database was extended and balanced by fusing it with private local thermograms from healthy volunteers and generating synthetic data via synthetic minority oversampling technique (SMOTE). State-of-the-art features were extracted using two classical approaches, LASSO and random forest, as well as two variational deep learning (DL)-based ones: concrete and variational dropout. Then, the most relevant features were detected and ranked. Subsequently, the extracted features were employed to classify subjects at risk of developing an ulcer using as reference a support vector machine (SVM) classifier with a fixed hyperparameter configuration to evaluate the robustness of the selected features. The new set of features extracted considerably differed from those currently considered state-of-the-art but provided a fair performance. Among the implemented extraction approaches, the variational DL ones, particularly the concrete dropout, performed the best, reporting an F1 score of 90% using the aforementioned SVM classifier. In comparison with features previously considered as the state-of-the-art, approximately 15% better performance was achieved for classification. Full article

(This article belongs to the Special Issue Applications of Image Analysis in Thermal Sensors Imaging)

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25 pages, 4405 KiB

Open AccessArticle

Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding

by Małgorzata Domino, Marta Borowska, Łukasz Zdrojkowski, Tomasz Jasiński, Urszula Sikorska, Michał Skibniewski and Małgorzata Maśko

Sensors 2022, 22(16), 6052; https://doi.org/10.3390/s22166052 - 13 Aug 2022

Cited by 4 | Viewed by 1958

Abstract

As obesity is a serious problem in the human population, overloading of the horse’s thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse’s back overload will shortly integrate the IRT-based rider-horse fit into everyday equine practice. This study aimed to evaluate the applicability of entropy measures to select the most informative measures and color components, and the accuracy of rider:horse bodyweight ratio detection. Twelve horses were ridden by each of the six riders assigned to the light, moderate, and heavy groups. Thermal images were taken pre- and post-exercise. For each thermal image, two-dimensional sample (SampEn), fuzzy (FuzzEn), permutation (PermEn), dispersion (DispEn), and distribution (DistEn) entropies were measured in the withers and the thoracic spine areas. Among 40 returned measures, 30 entropy measures were exercise-dependent, whereas 8 entropy measures were bodyweight ratio-dependent. Moreover, three entropy measures demonstrated similarities to entropy-related gray level co-occurrence matrix (GLCM) texture features, confirming the higher irregularity and complexity of thermal image texture when horses worked under heavy riders. An application of DispEn to red color components enables identification of the light and heavy rider groups with higher accuracy than the previously used entropy-related GLCM texture features. Full article

(This article belongs to the Special Issue Applications of Image Analysis in Thermal Sensors Imaging)

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