Signal and image processing for bioinformatics (2019/2020)



Course code
4S003710
Credits
12
Coordinator
Gloria Menegaz
Academic sector
INF/01 - INFORMATICS
Language of instruction
Italian
Teaching is organised as follows:
Activity Credits Period Academic staff Timetable
Segnali teoria 4 I semestre Gloria Menegaz

Go to lesson schedule

Segnali laboratorio 2 I semestre Gloria Menegaz

Go to lesson schedule

Immagini teoria 4 I semestre Alessandro Daducci

Go to lesson schedule

Immagini laboratorio 2 I semestre Alessandro Daducci

Go to lesson schedule

Learning outcomes

The purpose of the course is to introduce the main theoretical and practical tools that are necessary for signal and image processing, including both natural and medical images.
At the end of the course, the students will be able to apply the methodologies studied and employ image processing software tools for tackling some typical tasks in the analysis of medical and biomedical images.

Syllabus

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Part 1: Signal processing - Theory
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- Introduction to signal and image processing - Fourier transform in one dimension - A/D conversion (sampling, quantization) - Digital filtering (low-pass, high-pass, linear, non-linear) - Fourier transform in two dimensions - Image enhancement - Bases of image segmentation (edge-based, region-based) - Applications
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Part 1: Signal processing - Lab
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The Lab activity will consist in solving exercises in Matlab concerning the topics covered during the lessons.

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Part 2: Image processing - Theory
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- Recall of basic notions on segnals (time and frequency domain, Fourier transform...) - Digital image fundamentals - Histogram and point operations - Spatial filtering (spatial and frequency domain) - Restoration - Feature extraction (points, lines, edges) - Morphological operations - Geometric transformations and registration - Segmentation - Compression
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Part 2: Image processing - Lab
The hands-on part starts with a guided-session to practice with the topics covered during the course by using functions/procedures already implemented inn MATLAB. During the remaining of the session students will have time to implement themselves some algorithms seen during the course.

Assessment methods and criteria

The grade for Part 1 (Signal processing) will be the mean of the grades of the theory and lab exams rounded to the nearest integer.
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Part 1: Signal processing - Theory
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Written exam on the topics covered during the course.

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Part 1: Signal processing - Lab
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The Lab. activity will be verified by a final exam consisting in the solution of exercises in Matlab similar to those solved during the course Lab sessions.

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Part 2: Image processing - Theory
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The aim of the written exam consists in verifying the comprehension of course contents and the capability to apply these contents for generalizing case studies presented during the course and for facing new issues. The written exams includes both open questions about the theory as well as exercises.
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Part 2: Image processing - Lab
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The aim of the laboratory exam is to verify the acquisition of the tools and methods and to assess the ability to apply such knowledge to the solution of new problems. The exam consists of an implementation part, where students have to implement themselves some of the algorithms seen during the course, as well as of solving some exercises using built-in MATLAB functions.

The final grade will be the mean of the grades of the two modules, rounded to the nearest integer.

Reference books
Activity Author Title Publisher Year ISBN Note
Segnali teoria Rafael C. Gonzalez and Richard E. Woods Digital Image Processing (Edizione 4) Prentice Hall College Div 2017 0133356728
Segnali teoria B.P. Lathi Signal Processing and Linear Systems Berkeley-Cambridge 1998 0-941413-35-7
Immagini teoria Rafael C. Gonzalez and Richard E. Woods Digital Image Processing (Edizione 4) Prentice Hall College Div 2017 0133356728
Immagini laboratorio Stormy Attaway Matlab: A Practical Introduction to Programming and Problem Solving (Edizione 3) Elsevier 2013 978-0-12-405876-7