## 7 Post-Processing Images
### Breast Thermograms  
[PERSON] et al. [100] in 2008 suggested a new procedure to detect breast cancer from infrared images based on the measurement of the localized temperature increases. In fact, higher temperature increases in a certain region can lead to a higher possibility that it is cancerous tissue. In particular, the authors applied a morphological approach to obtain the temperature increase in each image pixel, starting from the definition of a background temperature distribution. By analyzing the thermograms of 117 patients, they found a significant difference between benign and malignant lesions in terms of the maximum temperature increase. Finally, by setting a threshold of 1 \({}^{\circ}\)C for the temperature increase, the authors detected 44 of the 47 breast cancer cases. [PERSON] et al. [101] used a combination of genetic algorithms and fuzzy neural networks to analyze the infrared breast images of 200 women, of which only 15 had breast cancer diagnosed by mammography. The authors stated that their method was able to extract the optimal parameters from the thermal images to facilitate an accurate diagnosis, and they also declared a sensitivity of 93%. A hybrid multiple classifier system to detect breast cancer based on thermal image feature analysis was suggested by [PERSON] and [PERSON] [102]. These authors analyzed a data set of 146 breast thermograms, of which 29 were confirmed to be malignant. From these images, they extracted 38 features related to thermal asymmetry between the left and right breast regions. The same features were fed into two different models of classifiers, such as neural networks and support vector machines. To combine the individual classifiers between them, a neural network was used as a trained fuser. Additionally, a fuzzy diversity measurement was defined to eliminate redundant classifiers from the ensemble. The authors stated that their approach not only yields excellent classification accuracy and sensitivity but also outperforms both canonical classification methods. [PERSON] et al. [103] suggested a novel extended hidden Markov model to optimize the segmentation process of breast IR images in order to make the analysis of thermal patterns easier. The competitive advantage of their algorithm is its ability to handle random sampling of the IR images without compromising the quality of the segmented images. Moreover, the execution time is reduced compared to other classical image segmentation algorithms, such as Fuzzy C-Means, self-organizing maps, and the standard hidden Markov model. Recently, [PERSON] et al. [104] proposed an improved Generative Adversarial Networks model to produce highly accurate segmentations of breast infrared images. The suggested model was evaluated on three different data sets, yielding high indexes of performance. [PERSON] et al. [105] suggested an unsupervised anisotropic-feature transformation-based classifier for distinguishing normal and abnormal breast thermal images. After image segmentation, statistical features were extracted and used in the classifier. The authors claimed a high level of accuracy when using their procedure. A convolutional neural network was trained by [PERSON] and [PERSON] [106] for both feature extraction from IR breast images and their successive classification as normal or abnormal thermograms. The authors reported a training accuracy of 97.91% on the data set acquired from 140 subjects. Using the Bayes optimization algorithm, the same accuracy increased up to 98.95%. [PERSON] and [PERSON] [107] compared different classifiers, namely support vector machines, decision trees, random forests, K-NN, linear regression, and fuzzy logic, to extract the texture features of 56 breast thermograms. The gray-level run length matrix and gray-level co-occurrence matrix were used for feature extraction. Then the features were selected through unsupervised feature reduction techniques such as principal component analysis and autoencoders.