## 3 Image Processing Approaches Used for EAT and PAT Segmentation
### Machine Learning  
Some methods for image classification rely on extracting pre-determined features from images. This vector of image features is fed into a machine learning model that  
Figure 6: A diagram of the registration process. First, input and target images are selected. On the Unregistered image, both the target and input images are superimposed, with the input image in green and the target image in red. During registration, the input image is deformed to match the target image, while the target stays fixed. The Registered image shows the two images superimposed, but this time it shows the deformed input image as a result of the registration algorithm.  
classifies each pixel as part of a class. In the case of EAT segmentation, the pixels are classified as either EAT or background. The machine learning model is a statistical model that maps the input features into the output class, but the exact parameters of the model are not determined by hand [33]. Instead, the classifier parameters are learned through a process called machine learning. The model learns how to correctly classify the inputs either by learning inherent statistical relationships in the inputs (unsupervised learning) or by being exposed to manually classified examples and extrapolating based on them (supervised learning).  
In the case of supervised image segmentation, manually-determined features are extracted from the images and fed into the model. To train the model, inputs for which a manual classification is already determined are used. The model's output is then compared to the known ground truth, and the model tunes its parameters in such a way as to output the expected ground-truth value for that input. This is then repeated for all of the pre-classified inputs. The process of tuning the model's parameters in this way is called training, and the data with already classified ground truth is called training data. An example of a supervised machine learning process for segmenting images is shown in Figure 7.  
The specific features that are used as inputs for machine learning models are selected by the model designer based on how well they describe different aspects of the segmented structures. In the case of images, these are usually the pixel values and locations, mean  
Figure 7: A diagram of the machine learning process using a supervised linear classifier. The top portion of the diagram shows the training process. The features are colored based on their known class from the training data, in red and blue. The parameter of the line separating the areas of the two classes (shown in light red and grey) is learned during the training process. The bottom portion of the figure shows the inference process, where the same features are extracted and the trained model is used to classify each pixel of the image. In the segmented image, the red area is EAT while the green area is paracardial adipose tissue.  
pixel values in a neighborhood, geometric moments, and other higher-level descriptions of image features. The model can then determine, for instance, that having a CT pixel around the center of the image with a value of \(-150\) HU has a high likelihood of being EAT, while a pixel of 30 HU and on the edge of the scan is less likely to be EAT.  
There are also different methods of parameter optimization via machine learning. One such method used for pericardium delineation is genetic algorithms. In general, genetic algorithms are a method for parameter optimization that simulates an evolutionary process. Initially, parameters are randomly determined. Then, the parameters are randomly changed (i.e., mutated) and evaluated using a fitness function, that evaluates the parameter's performance on some task. Mutations that lead to an increase in fitness are carried over to the next iteration when the parameters are mutated again. This process is repeated until the fitness function reaches an optimal value.