| Study programme competencies |
| Code | Study programme competences |
| A1 | CE1 - Ability to know the scope of Bioinformatics and its most important aspects |
| A2 | CE2 – To define, evaluate and select the architecture and the most suitable software for solving a problem in the field of bioinformatics |
| A4 | CE4 - Ability to acquire, obtain, formalize and represent human knowledge in a computable form for the resolution of problems through a computer system in any field of application, particularly those related to aspects of computing, perception and action in bioinformatics applications |
| A6 | CE6 - Ability to identify software tools and most relevant bioinformatics data sources, and acquire skill in their use |
| B1 | CB6 - Own and understand knowledge that can provide a base or opportunity to be original in the development and/or application of ideas, often in a context of research |
| B2 | CB7 - Students should know how to apply the acquired knowledge and ability to problem solving in new environments or little known within broad (or multidisciplinary) contexts related to their field of study |
| B5 | CB10 - Students should possess learning skills that allow them to continue studying in a way that will largely be self-directed or autonomous. |
| B6 | CG1 -Search for and select the useful information needed to solve complex problems, driving fluently bibliographical sources for the field |
| B7 | CG2 - Maintain and extend well-founded theoretical approaches to enable the introduction and exploitation of new and advanced technologies |
| C3 | CT3 - Use the basic tools of the information technology and communications (ICT) necessary for the exercise of their profession and lifelong learning |
| C6 | CT6 - To assess critically the knowledge, technology and information available to solve the problems they face to. |
| Learning aims | |||
| Learning outcomes | Study programme competences | ||
| Understand the medical imaging modalities and their significance | AJ1 |
BJ1 |
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| Understand the basic concepts of image processing | AJ4 AJ6 |
BJ5 BJ6 |
CJ3 |
| Design and evaluate medical analysis techniques | AJ2 |
BJ2 BJ7 |
CJ6 |
| Contents |
| Topic | Sub-topic |
| Introduction to digital imaging. | Adquisition models. Quality metrics. Color spaces. Histograms. |
| Image processing. | Enhancement. Edge detection. Segmentation. Morphological operators. |
| Image registration and fusion. | Intensity vs features. Similarity measures. Multimodal methods. |
| Validation of medical image analysis methodologies | Measures for quality assessment Training and testing methods Statistical tests |
| Planning | ||||
| Methodologies / tests | Competencies | Ordinary class hours | Student’s personal work hours | Total hours |
| Guest lecture / keynote speech | A1 A4 B1 | 16 | 16 | 32 |
| Laboratory practice | A2 A6 B2 B7 C3 | 16 | 32 | 48 |
| Research (Research project) | A2 B2 B5 B6 | 16 | 32 | 48 |
| Practical test: | A2 A6 | 0 | 16 | 16 |
| Objective test | A1 A2 B1 B2 C6 | 3 | 0 | 3 |
| Personalized attention | 3 | 0 | 3 | |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. | ||||
| Methodologies |
| Methodologies | Description |
| Guest lecture / keynote speech | Lectures with the use of audiovisual aids. Questions will be raised in order to transmit the knowledge and enforce the learning. |
| Laboratory practice | The aim is to solve common problems in medical imaging using the methods explained in the lectures. |
| Research (Research project) | Proposal of a biomedical imaging problem in which learner is tasked with identifying problem, articulating specific nature of problem, analysing it, interpreting results, and reaching appropriate conclusion. |
| Practical test: | Practical application of specific techniques or procedures already studied in the keynote lectures during the semester. |
| Objective test | Test with questions about the theoretical contents of the subject as well as practical problems. |
| Personalized attention |
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| Assessment | |||
| Methodologies | Competencies | Description | Qualification |
| Research (Research project) | A2 B2 B5 B6 | Suitability of the proposed solutions to the problems. Quality of the obtained results. Comprehension of the employed techniques. | 30 |
| Practical test: | A2 A6 | Suitability of the solutions to the practical excercises proposed during the semester. | 10 |
| Laboratory practice | A2 A6 B2 B7 C3 | Suitability of the proposed solutions to the problems. Quality of the obtained results. Comprehension of the employed techniques. | 20 |
| Objective test | A1 A2 B1 B2 C6 | Written test with theoretical questions and practical problems to be solved. | 40 |
| Assessment comments | |||
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In order to pass this subject, students have to get, at least, 50% of the mark in laboratory practice, supervised projects and objective test. ACADEMIC EXEMPTION For all those students with half time dedication and academic exemption specific considerations will be taken. |
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| Sources of information |
| Basic |
Rafael C. González, Richard E. Woods (2010). Digital image processing. Upper Saddle River (New Jersey) : Pearson-Prentice Hall, [2010]
Milan Sonka, Vaclav Hlavac, Roger Boyle (2014). Image processing, analysis and machine vision. Pacific Grove, California : Brooks/Cole Publishing Company, |
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| Complementary |
David A. Forsyth, Jean Ponce (2012). Computer vision : a modern approach. Boston : Pearson
Richard Szeliski (2010). Computer Vision: Algorithms and Applications. Springer (draft online) |
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| Recommendations | ||
| Subjects that it is recommended to have taken before | |
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| Subjects that are recommended to be taken simultaneously | ||
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| Subjects that continue the syllabus | |
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| Other comments | |