Università degli Studi di Perugia

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Unit MEDICAL IMAGING ALGORITHMS

Course
Physics
Study-unit Code
GP005502
Curriculum
Fisica medica
Teacher
Laura Angeloni
Teachers
  • Laura Angeloni - Didattica Ufficiale
Hours
  • 42 ore - Didattica Ufficiale - Laura Angeloni
CFU
6
Course Regulation
Coorte 2018
Offered
2018/19
Learning activities
Affine/integrativa
Area
Attività formative affini o integrative
Sector
MAT/05
Type of study-unit
Opzionale (Optional)
Type of learning activities
Attività formativa monodisciplinare
Language of instruction
Italian
Contents
Brief Introduction to classical Signal Theory: continuous and discrete signals, their main properties.
Reconstruction of signals by sampling and applications. Digitale Images and some algorithms of Digital Image Processing. Filtering in the spatial and in the frequency domain. Images compression and redundancy.
Reference texts
Lecture notes and slides by the teacher. Some books will also be advised.
Educational objectives
The aim is to reach competence about the major concepts of signal processing, Fourier analysis and image processing.
Prerequisites
Basic notions of Mathematical Analysis.
Teaching methods
Frontal lessons.
Other information
Some laboratory activity could be held with the aim to deepen some applicative aspect of the course.
Learning verification modality
Oral exam.
The verification of teaching educational goals is through an oral exam. The interview will be held on the dates set out in the examinations calendar of the related Corso di Studi.
The oral exam consists of a discussion of about 40 minutes with the aim to verify the level of knowledge and understanding achieved by the student on the program's arguments. The discussion will also test the communication skills of the student, with a particular focus on properties of language and on the ability of self-organization of the exposition on the theoretical contents of the course.
Extended program
- Introduction to Signal Theory: physical, mathematical, deterministic, random, multi-dimensional signals.
· Theory of continuous signals: definition of continuous signals, examples, symmetry, translation, area and mean value, energy and power, duration and extension of a signal. Periodic signals, periodic repetition and area, mean value, energy, power, duration and extension of a periodic signal. Examples of main signals: constant, sinusoidal, exponential, step, rectangular signals, ideal impulses (Dirac delta), sync-type impulse. Convolution, properties, durability and convolution calculation.
· Series and Fourier transform: definitions and main properties of the series and Fourier transform.
· Band of a signal and examples of transforms of some important signals. Signals Filtering: a study in the time domain and frequency domain.
· Theory of discrete signals and their main properties. Fourier transform for discrete signals.
· Multi-dimensional Fourier transform. DFT and FFT transform: definitions and computational complexity.
· Reconstruction of signals by sampling: history, Shannon sampling theorem and its consequences and applications.
- Digital images, gray-scale and color images. Multidimensional sampling: sampling theorem for images. Aliasing Phenomena. Spatial, spectral, radiometric, temporal resolution. Zoom and shrink of a digital image.
- Digital image operations. Introduction to Image Processing in the spatial domain. Punctual, local, global transformations.
- Histogram of gray tones: definition and main features. Examples of histogram transformations.
- The noise. Image enhancement through arithmetic operations.
- Spatial filtering: smoothing and sharpening filters. Filtering in the frequency domain.
- Image Compression: redundancy, lossless and lossy compression, video compression.
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