Study-Unit Description

Study-Unit Description


CODE CBC5112

 
TITLE Advanced Signal and Image Processing for Physiological Measurement and Medical Imaging

 
UM LEVEL 05 - Postgraduate Modular Diploma or Degree Course

 
MQF LEVEL 7

 
ECTS CREDITS 10

 
DEPARTMENT Centre for Biomedical Cybernetics

 
DESCRIPTION In this study-unit the details of physiological signal and medical image acquisition modalities will be presented to provide the students with a solid understanding of the underlying nature and characteristics of the acquired signals and images.

The theory behind a range of advanced signal and image processing methods applied to these signals and images will be provided, together with insight on the use of these methods in practical clinical applications.

The students will also acquire the necessary programming skills to implement and apply advanced signal and image processing techniques on physiological data.

Study-unit Aims:

1. To ensure that students acquire the knowledge and skills necessary to act as experts in signal and image processing applied to physiological signals and medical images.

2. To ensure that students have a good understanding of the following aspects related to physiological signals and medical mages considered in various clinical applications:
(i) the underlying physiological activity that leads to the generation and characteristics of specific signals;
(ii) the associated data acquisition methods; and
(iii) the associated signal and image analysis/processing methods.

Specifically, by the end of the study-unit, the student is expected to have an in-depth understanding of the above for the following physiological signals:
a) ECG signals;
b) EEG signals;
c) EMG signals;
d) blood oxygenation levels;
e) respiratory rate;
and for the following imaging modalities:
f) ultrasound imaging;
g) X-ray imaging;
h) computed tomography (CT);
i) magnetic resonance imaging (MRI) and functional MRI (fMRI);
j) positron emission tomography (PET).

3. To ensure the students have a good understanding of the analysis and processing methods applied to physiological signals in the context of a range of clinical applications including:
a) sleep analysis (ECG, EEG, respiratory rate, pulse oximetry);
b) heart activity monitoring (ECG, pulse oximetry);
c) fetal growth monitoring (ultrasound);
d) bone fracture monitoring (X-ray imaging, CT scans);
e) mammography for tumour detection (X-ray imaging);
f) tumour detection, monitoring and treatment (CT scans, PET);
g) aneurysms of cerebral vessels (MRI).

4. To ensure that students gain the required programming skills to implement and apply advanced signal and image processing methods for clinical applications, namely, using Python.

Learning Outcomes:

1. Knowledge & Understanding
By the end of the study-unit the student will be able to:

1. Explain the origin and characteristics of a range of common physiological signals, and the acquisition process for the associated signal acquisition modality. Specifically, the students will gain an understanding of the fundamentals of electrophysiological (e.g., ECG, EEG), respiratory (e.g., blood oxygenation) and sensory (e.g., auditory) biosignals.

2. Understand the medical image acquisition process from a signal and image processing perspective. Specifically, the following imaging modalities will be considered:
a) ultrasound imaging;
b) X-ray imaging:
c) computed tomography (CT) imaging;
d) magnetic resonance imaging (MRI) and functional MRI (fMRI);
e) positron emission tomography (PET).

3. Understand and apply a range of advanced signal and image processing methods for physiological signal and image acquisition, analysis, processing and reconstruction including:
a) time-frequency analysis methods (short-time Fourier transform, continuous wavelet transform);
b) filtering for artifact removal;
c) template matching for waveform detection;
d) signal decomposition methods (PCA, ICA, EMD);
e) pattern classification (supervised, unsupervised, neural networks);
f) image reconstruction from projections (the Radon transform);
g) image segmentation methods.

4. Understand the relevance of these physiological signal acquisition and processing methods for the following clinical applications:
a) sleep analysis (using ECG, EEG, respiratory rate, pulse oximetry);
b) diagnosis of abnormal heart conditions (using ECG, pulse oximetry);
c) fetal growth monitoring (using ultrasound);
d) mammography (using X-ray imaging);
e) tumour detection, monitoring and treatment (using CT scans, PET scans, MRIs).

2. Skills
By the end of the study-unit the student will be able to:

1. Identify suitable signal and image analysis and processing techniques for tackling problems presented when working with physiological signal acquisition and medical imaging modalities.

2. Make use of advanced mathematical and analytical skills for the analysis and processing of biomedical signals and images.

3. Analyse, process and visualise biomedical signals and medical images using programming skills (in Python).

Main Text/s and any supplementary readings:

Rangayyan, R. M. (2011). Biomedical Signal Analysis (2nd ed.). Wiley.
Rangayyan, R. M. (2005). Biomedical Image Analysis. CRC Press.
Proakis, J. G., & Manolakis, D. G. (2006). Digital Signal Processing: Principles, Algorithms and Applications. Pearson Prentice Hall.
Gonzalez, R. C., & Woods, R. E. (2008). Digital image processing. Pearson.

 
STUDY-UNIT TYPE Lecture, Independent Study & Tutorial

 
METHOD OF ASSESSMENT
Assessment Component/s Assessment Due Sept. Asst Session Weighting
Assignment SEM2 Yes 40%
Examination (2 Hours) SEM2 Yes 60%

 
LECTURER/S Natasha Padfield
Nathaniel Barbara
Liam Butler

 

 
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The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years.

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