Unit MEDICAL IMAGING ALGORITHMS
- Course
- Physics
- Study-unit Code
- GP005502
- Curriculum
- Fisica medica
- Teacher
- Laura Angeloni
- Teachers
-
- Laura Angeloni
- Hours
- 42 ore - Laura Angeloni
- CFU
- 6
- Course Regulation
- Coorte 2017
- Offered
- 2017/18
- Learning activities
- Affine/integrativa
- Area
- Attività formative affini o integrative
- Academic discipline
- MAT/05
- Type of study-unit
- Opzionale (Optional)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- Italian
- Contents
- Brief Introduction to Signal Theory. Theory of continuous signals. Series and Fourier transform. Band of a signal and examples of transforms of some important signals.
Theory about discrete signals and their main properties. Fourier transform for discrete signals.
Multi-dimensional Fourier transform.
Reconstruction of signals by sampling and applications. Image processing. Filtering. Images compression and redundancy. - Reference texts
- Lecture notes by the teacher, slides. 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 lessons could be held at the hospital Silvestrini in Perugia to ideepen 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.