Aim of the course is to enable students to master signal and image processing methods and to acquire capabilities to the application in the biomedical field. The Course covers biomedical signal processing, biomedical signal generation, medical images and analysis from the theory to the practical applications.

The Course is organized into two parts: the first one [1] is dedicated to Biomedical Signal Analysis.

The objective is to introduce the most used methods of information processing for biomedical signals and to describe some significant applications in Medicine and Biology, in both physiological studies and in clinical applications for diagnosis, therapy and rehabilitation.

The second part of the Course [2] deals with Medical Images.

The goal is providing basic concepts on biomedical images by introducing the related clinical and diagnostic problems.  Principles on which techniques for processing and reconstruction are based on are presented.

The Course is composed by frontal lessons, exercise sessions, seminars and laboratory exercises aiming at deeply analyzing topics related to signal and image processing making use of MatLab Tools. The attendance to the Course is strongly advised both at frontal lessons and exercise sessions whose content is also part of the final evaluation.

The student will acquire methodological knowledge about  Signal and Medical Images processing and analysis as well as applications and their main processing tools.

Students will be able to work on biomedical signal processing and medical image with analysis and processing Matlab tools.

Moreover, through the knowledge of advanced methods and parameters measuring physiological system changes, they should be able to work autonomously in application problems different from those addressed in the lessons and/or in the practical exercises.

COURSE PROGRAMME

Part [1] Biomedical Signal Processing

Introduction to biomedical signal processing. General block diagram of biomedical signal processing operations from analog preprocessing to digital conversion of the signals. Recall of analog filters. Numerical signals, numerical filters (FIR and IIR), and their application in biomedical field.

Cardiovascular System. ECG signal: additive noise model and main superimposed noises. Most significant configurations from clinical standpoint. High resolution ECG. Ventricular late potentials: pathophysiological aspects and processing methods.

Heart Rate Variability (HRV). Study of Autonomic Nervous System (ANS) by signal processing in short- and long-term bases. Analysis of ANS branches to HRV regulation. Pathophysiological aspects: neural control mechanisms. Fetal ECG: processing methods and enhancement of useful clinical parameters. Fetal monitoring.

Arterial blood pressure signal: detection systems and main clinical parameters. Interaction models encompassing relationship between several signals related to autonomic nervous system: ECG, arterial blood pressure and respiration; open- and closed-loop models. Pathophysiological interpretations.

Neurosensorial System. EEG (electroencephalographic) signal analysis, evoked potentials (EPs) and event-related potentials (ERPs). Review on traditional processing methods with main applications in clinical and research environments: cerebral activity detected on the scalp, at cortical level and in deep brain stimulation (DBS).

Signal processing and parametric identification. Time series analysis extracted from biological signals. Examples in ECG, EEG, EP in pre-processing, filtering, prediction, extraction and estimation of parameters.

Diagnostic Classification and Modelling. Deterministic and stochastic identification. Non parametric and parametric models. FFT and DFT power spectrum analysis. Review on stochastic identification approaches. Bayes classifiers. Model families: AR/MA/ARMA (autoregressive and moving average). Models with exogenous input X. Parametric spectral analysis: backward and forward, maximum entropy methods, Pisarenko, Prony. Comparison with traditional non-parametric techniques: examples on HRV signals, EEGs and EPs. 

Classification methods: Principal Component Analysis (PCA): general introduction and applications. Independent Component Analysis (ICA).

Entropy Analysis: methods and applications. Approximate and Sample Entropy.

Laboratory activities: Exercises with Matlab implementing signal analysis tools. Application to biomedical signals. Homework exercises with evaluation. Students will develop knowledge about the most important methods and applications for biomedical signal processing and analysis working on biomedical signal samples.

Part [1] Biomedical Signal processing (short summary)

The course is intended to recall general signal processing aspects, to introduce new methods beyond those considered in the bachelor degree and cover various clinical and research application fields. Methods. From deterministic filtering to stochastic parametric analysis: mono and multi-variate AR/MA/ARMA models and non parametric as well as parametric spectral analysis. Principal component analysis. Entropy in signal processing. Applications. Automatic analysis and classification of the electrocardiographic signal (ECG). The autonomic nervous system, cardiovascular variability signals, and cardiorespiratory interactions. Fetal ECG signal. High resolution ECG and late ventricular potentials. The central nervous system: processing of the electroencephalographic signal (EEG) and of evoked (EP) and event-related potentials (ERP).

Part [2] Medical Images

Introduction to Medical Imaging modalities

Introduction to image classification. Image generation: Point Spread Function, Modulation Transfer Function, Spatial and amplitude resolution, Contrast, Signal to Noise ratio. Fourier Transform in the 2D spatial domain. Fotonic images. Human perception and Biomedical Images. Psychophysics Weber's Law. Perception thresholds for human vision.

Basic Principles for image processing and reconstruction. Sampling and quantization in the spatial domain. Discrete Fourier Transform in the 2D space domain. Convolution 2D. Image quality enhancement: spatial filters, equalization. Geometric operators. Artifacts and their removal. Tomographic reconstruction. Numeric approach to tomographic reconstruction: Radon transform, Sinogram, back-projection and blurring. Filtered back-projection. Ramp high pass filter, Central Slice Theorem.

X ray Imaging: Basic radiography, X-ray generation, radiation interaction with matter, dose. X-ray instrumentation. Digital Radiology. Mammography, Angiographic X-Ray imaging. Computed Tomography (CT): applications, history and evolution of instrumentation, spiral CT. Tomographic reconstruction application to CT images.

Nuclear medicine Imaging Radioactivity. Nuclear Emission images. General principles Single Photon Emission Tomography (SPECT). Detectors and data acquisition system. Positron emission Tomography (PET). Spatial resolution. Detectors. Instrumentation. Dual mode PET-CT scanners.

Magnetic Resonance Imaging (MRI): Principles, Instrumentation, Pulse Sequences, T1 and T2 contrast, image generation. FMRI.

Ultrasound Imaging: Principles, echography and doppler, other contrast agents.

Laboratory activities:  Exercises with Matlab implementing image analysis tools. Application to different medical images. Homework exercises with evaluation. Objective of the Lab is teaching how to work on examples of medical image analysis by the most used tools in space and in spatial frequency domain. Students will learn how to preprocess biomedical images, how to extract information which could be of pathophysiological importance. Furthermore, practical activity will introduce students to general criteria to work with medical image analysis even different from those seen in the Lab.

 Part [2] Medical Images (short summary)

Introduction to the main imaging diagnostic systems: analysis of the relevant features: spatial resolution, contrast, signal to noise ratio, artifacts. Basic principles for image processing and reconstruction: 2D Fourier transform, sampling and quantization, enhancement techniques (spatial filtering, equalization), geometric operations tomographic reconstruction. Imaging modalities and their characteristics. X ray images: planar radiographs, digital radiology, angiography, transmission tomography (CT). Emission images with radiotracers: scintigraphy and gamma-camera, emission tomography (SPECT and PET). Magnetic resonance imaging (MRI): T1 and T2 contrast, construction of MRI (basic acquisition sequences, frequency and phase encoding), functional MRI (brief remark). Ultrasound (US) imaging: echography, Doppler.

Fundamentals of digital signal processing.

Assessment

The assessment will be based on a written exam with exercises and open questions covering the two parts of the Course and on a project developed in the Laboratory part of the Course. The project will be presented to the instructor at the end of the course. Practical assessments given as lab activity (not mandatory) and made by students will complete the exam.

The score of the written exam, the project part and the practical assessments will be summed to compute the total score. 30 cum laude will be assigned when the total score is higher than 31.

The written exam will cover all course topics both in signal and image processing. It will assign up to 24 points and will be considered sufficient when the score will be equal or higher than 14. The project part will assign up to 6 points. The four Lab assignments (2 signals and 2 images) will assign up to 4 points. These Lab assignments are not mandatory. Students can take the written part at any exam session during the year. No mid-term assessments will be scheduled.