-
Presentation
Presentation
Medical Imaging II continues Medical Imaging I, shifting the focus from acquisition and reconstruction principles to the processing, post-processing, analysis, and computational exploration of medical images. The curricular unit covers digital imaging foundations, enhancement, restoration, morphology, segmentation, feature recognition, basic classification, three-dimensional visualization, quantification, CAD, radiomics, artificial intelligence, and automation. Its relevance within the study programme derives from the growing use of computational methods in diagnosis, treatment planning, monitoring, biomedical research, and innovation. The unit integrates medical imaging, programming, and Computational Biomedicine, preparing students to develop, select, and evaluate image analysis solutions in clinical and scientific contexts.
-
Class from course
Class from course
-
Degree | Semesters | ECTS
Degree | Semesters | ECTS
Bachelor | Semestral | 7
-
Year | Nature | Language
Year | Nature | Language
2 | Mandatory | Português
-
Code
Code
ULHT7037-18974
-
Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
-
Professional Internship
Professional Internship
Não
-
Syllabus
Syllabus
Introduction to medical image processing and technological evolution. 2. Imaging systems: processing, post-processing, and the current landscape. 3. Digital imaging foundations: representation, basic techniques, hardware, and software. 4. Image enhancement in the spatial and frequency domains. 5. Image restoration: methods, applications, and case studies. 6. Image analysis: morphological processing, segmentation, feature recognition, basic classification, and three-dimensional visualization. 7. Post-processing applied to diagnosis, treatment planning, monitoring, research, and innovation. 8. Quantification, CAD, radiomics, and artificial intelligence in Radiology. 9. Programming and automation: tools, platforms, and best practices for effective, reproducible, and efficient medical image analysis.
-
Objectives
Objectives
By the end of the curricular unit, students should be able to: LO1) understand fundamental medical image processing concepts and techniques, including enhancement, restoration, segmentation, and three-dimensional visualization; LO2) interpret the relevance of processing and post-processing and their impact on image quality in Medical Imaging and Radiotherapy; LO3) apply processing techniques to improve image quality, clarity, and usefulness in clinical and biomedical contexts; LO4) analyse processing methods, recognizing their advantages, limitations, and suitability for a given problem; LO5) integrate image processing, quantification, radiomics, CAD, programming, and automation into biomedical applications; LO6) critically assess literature, innovation, and artificial intelligence applications in Computational Biomedicine.
-
Teaching methodologies
Teaching methodologies
Teaching combines flipped-classroom activities, interactive lectures, scientific article analysis, problem-based learning, and medical image processing projects. Digital materials are provided in advance to promote prior study and active discussion. In theoretical-practical sessions, students work with images and computational tools, implementing or applying enhancement, restoration, segmentation, morphological analysis, quantification, and three-dimensional visualization techniques. Clinical and biomedical cases guide method selection and comparison. Programming and automation are introduced through progressive and reproducible tasks. Individual and collaborative work, formative feedback, presentation of results, and critical analysis of literature promote autonomy, technical reasoning, scientific communication, and evidence-based decision making.
-
References
References
Aktolun, C. (2019). Artificial intelligence and radiomics in nuclear medicine: potentials and challenges. Eur J Nucl Med Mol Imaging. Zeng, G. L. (2017). Image Reconstruction: Applications in Medical Sciences (1st ed.). Walter de Gruyter & Co. Nery, E. et al. (2008). Image Processing in Radiology: Current applications. Springer. Jahne, B. (2005). Digital Image Processing (6th edition). Springer. Dougherty, G. (2007). Digital Image Processing for Medical Applications. Cambridge. Pianykh, O. (2012). Digital Imaging and Communications in Medicine. Springer. Gonzalez, R. (2000). Processamento de Imagens Digitais. Edgar Blucher Lda.
-
Assessment
Assessment
A unidade curricular admite avaliação contínua e avaliação não contínua. A avaliação contínua integra uma frequência escrita (F) e um projeto de processamento de imagem médica (P), com a fórmula: Nota Final = 0,50 × F + 0,50 × P. A frequência avalia conhecimentos, compreensão dos fundamentos, análise de métodos e interpretação de resultados. O projeto avalia definição do problema, seleção e aplicação de técnicas, qualidade do processamento, análise e interpretação das imagens, rigor metodológico, reprodutibilidade, utilização de literatura científica, autonomia, colaboração e comunicação dos resultados. A aprovação exige classificação final mínima de 9,5 valores. Na avaliação não contínua, o estudante realiza exame final com incidência global nos conteúdos e objetivos de aprendizagem, sendo igualmente necessária uma classificação mínima de 9,5 valores.
-
Mobility
Mobility
No
