The aim of the course is for the student to acquire basic knowledge about the following subjects: 1. digital signals and their acquisition and processing methods, 2. digital images and their acquisition and processing methods. The techniques for representing, storing and managing digital signals and images are presented in detail, while their basic characteristics for signals (formats, types, etc.) for images (color models, resolution, color depth) are studied. Basic image compression techniques are studied and the JPEG and PNG compression algorithms are presented in detail. Basic image analysis and processing methods are also analyzed, including the application of filters, the extraction of texture features and segmentation. Also, an introduction is made to basic machine learning techniques with a view to their use in classification applications. Finally, common applications of image analysis and processing in health and science are presented, while an introduction is made to medical imaging systems and diagnostic assistance systems. The course content includes the following thematic units: Introduction to signals: Definition, Basic principles, Signal categories, Sampling. Digital images: Basic principles, black and white images, color models, Color depth, Noise. Image compression: Basic principles of data compression, The JPEG algorithm, The PNG algorithm. Introduction to image processing: Smoothing filter, Median filter, Histogram equalization, Edge enhancement. Feature extraction from images: Methods for extracting features from images are presented. Feature selection methods: Methods for selecting features are presented, Feature methods. Segmentation and classification: Methods for image segmentation are presented, Classification algorithms, General principles for using supervised machine learning algorithms. Biomedical applications of signal analysis and processing: One-dimensional medical signals, Medical imaging methods, Expert medical systems. Applications to real signals and images with software such as MATLAB and ImageJ are also demonstrated.