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In the field of medicine, thermal image processing and analysis play a significant role in the diagnosis, monitoring, and treatment of diseases. For example, during the last decade, several studies have been performed based on thermal image processing for ocular disease diagnosis. This research proposes a unique approach for the classification of subgroups of two retinal vascular diseases, namely diabetic eye disease and age-related macular degeneration (AMD). The class imbalance problem is a well-known issue when working with medical data, where one class is significantly less represented than another class in the dataset. To deal with the class imbalance issue, an ensemble decision tree classifier with a random under-sampling and adaptive boosting (RUSBoost) technique is proposed. The performance of the proposed classifier is compared with various traditional machine learning-based classifiers. Experimental results show that the proposed ensemble tree outperforms other classifiers through high accuracy, $F$-score, and Mathews correlation coefficient (MCC) values in classifying diabetic eye diseases and AMD diseases. The proposed ensemble decision tree distinguishes dry AMD and wet AMD over healthy controls with 95% average accuracy. Also, it classifies diabetic retinopathy (DR) with diabetic macular edema (DME) and DR without DME with 94% average accuracy. The classifier could distinguish dry and wet AMD which did not work around in temperature analysis on the manual temperature measurement. The performance of the automated classification model is on par with the performance of the temperature analysis of OST for DME and DR without DME.

A characterization of cylindrical periodic subsurface defects of different sizes by means of pulsed thermography is presented in the paper. To ensure a uniform thermal flux distribution, the test samples were heated in lab conditions using two photographic flashes. Surface temperature was intentionally recorded at an angle to the normal of the sample surface. Recorded temperatures were compared with simulated temperatures and the differences in temperature peak values and temperature peak positions were noted. The thermal image was transformed based on known positions of four noncollinear points, in order to cancel out errors resulting from image recording at an angle. The uniformity of surface heating and the effect of the positions of the defects on the results were tested by means of a simulation model. The positions did not affect defect characterization. It was also found that in spite of nonuniform heating, if the reference points were selected properly, the difference in temperature contrast was negligible.

In this paper, robust human detection is investigated by fusing the stereo and infrared thermal images for effective interaction between humans and socially interactive robots. A scale-adaptive filter is first designed for the stereo vision system to detect human candidates. To eliminate the difficulty of the vision system in distinguishing human beings from human-like objects, the infrared thermal image is used to solve the ambiguity and reduce the illumination effect. Experimental results show that the fusion of these two types of images gives an improved vision system for robust human detection and identification, which is the most important and essential component of human robot interaction.

Breast cancer is one of the main causes of women’s death. Thermal breast imaging is one the non-invasive method for cancer at early stage diagnosis. In contrast to mammography this method is cheap and painless and it can be used during pregnancy while ionized beams are not used. Specialists are seeking new ways to diagnose the cancer in early stages. Segmentation of the breast tissue is one of the most indispensable stages in most of the cancer diagnosis methods. By the advancement of infrared precise cameras, new and fast computers and nouvelle image processing approaches, it is feasible to use thermal imaging for diagnosis of breast cancer at early stages. Since the breast form is different in individuals, image segmentation is a hard task and semi-automatic or manual methods are usual in investigations. In this research the image data base of DMR-IR has been utilized and a now automatic approach has been proposed which does not need learning. Data were included 159 gray images used by dynamic protocol (132 healthy and 27 patients). In this study, by combination of different image processing methods, the segmentation of thermal images of the breast tissues have been completed automatically and results show the proper performance of recommended method.

Breast cancer is a common cancer in female. Accurate and early detection of breast cancer can play a vital role in treatment. This paper presents and evaluates a thermogram based Computer-Aided Detection (CAD) system for the detection of breast cancer. In this CAD system, the Random Subset Feature Selection (RSFS) algorithm and hybrid of minimum Redundancy Maximum Relevance (mRMR) algorithm and Genetic Algorithm (GA) with RSFS algorithm are utilized for feature selection. In addition, the Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) algorithms are utilized as classifier algorithm. The proposed CAD system is verified using MATLAB 2017 and a dataset that is composed of breast images from 78 patients. The implementation results demonstrate that using RSFS algorithm for feature selection and kNN and SVM algorithms as classifier have accuracy of 85.36% and 75%, and sensitivity of 94.11% and 79.31%, respectively. In addition, using hybrid GA and RSFS algorithm for feature selection and kNN and SVM algorithms as classifier have accuracy of 83.87% and 69.56%, and sensitivity of 96% and 81.81%, respectively, and using hybrid mRMR and RSFS algorithms for feature selection and kNN and SVM algorithms as classifier have accuracy of 77.41% and 73.07%, and sensitivity of 98% and 72.72%, respectively.