| Code: | EBE0056 | Acronym: | AIBI |
| Keywords | |
|---|---|
| Classification | Keyword |
| OFICIAL | Biomedical Engineering |
| Active? | Yes |
| Web Page: | http://moodle.fe.up.pt |
| Responsible unit: | Department of Electrical and Computer Engineering |
| Course/CS Responsible: | Master in Bioengineering |
| Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
|---|---|---|---|---|---|---|---|
| MIB | 30 | Syllabus | 3 | - | 6 | 56 | 162 |
| MIEEC | 18 | Syllabus (Transition) since 2010/2011 | 4 | - | 6 | 56 | 162 |
| 5 | |||||||
| Syllabus | 4 | - | 6 | 56 | 162 | ||
| 5 |
Main objectives are to develop knowledge and skills in: . concepts and methodologies for digital image processing; . principles, concepts and methods of physics and imaging technologies used in Biology and Medicine; . students' exposure to various forms of Image Analysis and Processing in Biology and Medicine (IAP-BM). Learning outcomes are: . knowledge acquisition in IPA-BM; . analysis of problems in IPA-BM; . design of IPA-BM; . oral and written presentation. |
Learning outcomes are: . knowledge acquisition in IPA-BM; . analysis of problems in IPA-BM; . design of IPA-BM; . oral and written presentation.
1. INTRODUCTION 1.1. The image processing cycle 1.2. The machine and computer vision cycle 1.3. The Biomedical image analysis cycle 1.4. Applications .
2. DIGITAL IMAGES: ACQUISITION, SAMPLING, QUANTIZATION AND REPRESENTATION 2.1. Introduction 2.2. Digital Images 2.3. Biomedical Images.
3. IMAGE ENHANCEMENT 3.1. Basic intensity operations 3.2. Image enhancement using local operators 3.3. Image quality evaluation. 3.4 Mathematical morphology in image enhancement 3.5 Methods in the frequency domain.
4. FEATURE DETECTION 4.1. Introduction 4.2. Edge detection 4.3. Corner detectors 4.4. Blob detectors 4.5 Line and curve fitting .
5. IMAGE SEGMENTATION 5.1. Introduction 5.2. Feature domain 5.3. Image domain
6. QUANTITATIVE IMAGE ANALYSIS 7.1. Introduction 7.2. Connected components labeling 7.3. Feature measurement 7.4. Object representation
8. APPLICATIONS in MEDICINE AND BIOLOGY
Lectures (classes TP) exposing the main topics of the syllabus, always with illustrative examples. Lab work (classes PL) with the development by the students of application problems of the concepts and methods taught in the lectures.
| Designation | Weight (%) |
|---|---|
| Exame | 60,00 |
| Participação presencial | 0,00 |
| Trabalho laboratorial | 40,00 |
| Total: | 100,00 |
| Designation | Time (hours) |
|---|---|
| Estudo autónomo | 74,00 |
| Frequência das aulas | 68,00 |
| Trabalho laboratorial | 20,00 |
| Total: | 162,00 |
Type of evaluation: Distributed evaluation with final exam To be admitted to the final exam, the student: 1. Should not miss more than 25% of classes ( both TP and lab) (necessary condition); 2. Do a group assignment (study-EST) on a theme to be defined. The frequency grade (CF) is the grade of the group assignment. The final grade (NF) is calculated according to the following expression:NF = 0.6* PE +0.4 * F, where PE is the grade of the written final exam and F = min (CF, PE +4).
The frequency grade (CF) is the grade of the group assignment. The final grade (NF) is calculated according to the following expression:NF = 0.6* PE +0.4 * F, where PE is the grade of the written final exam and F = min (CF, PE +4).
1. Group assignment (two students) (40%)
2. Examination covering all course subjects.
Students with a special status will be evaluated as regular students.
Students have to attend recurso (resit) exam to improve their grades. The exam will cover the entire program.