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Biomedical Image

Code 12886
Year 1
Semester S2
ECTS Credits 6
Workload PL(30H)/T(30H)
Scientific area Bioengenharia
Mode of delivery Face-to-face
Work placements Not applicable
Learning outcomes Digital processing of medical imaging data allows the extraction of quantitative measures and the generation of complex visualization, which can be used for monitoring of disease progression, diagnosis, preoperative planning and intraoperative guidance and monitoring.

The objective of the course is to show how computer science can be used to model, analyze and process medical data in order to improve and / or facilitate diagnostics.
This course covers some of the principles and algorithms used in advanced image processing, and the main techniques for image analysis, both applied to medical imaging.

At the end of the UC student should be able to create a system to aid diagnosis, which use methods that are best suited to a specific type of medical imaging. The student should also be able to propose amendments to existing methods or even propose new methods best suited to the type images he wants to treat.
Syllabus Systems for processing and analysis of medical imaging.
Filtering. Different approaches. Advantages/disadvantages of the different approaches when applied to the different types of images.
Segmentation. Different approaches. Advantages/disadvantages of the different approaches when applied to the different types of images.
Characterization of medical images.
Classification of medical images.
Main Bibliography Handbook of Medical Imaging : Medical Image Proc. and An., ed. M. Sonka J. Fitzpatrick
Handbook of medical imaging: processing and analysis (biom. eng.). Bankman, I. Ac. Press, 2000
L.Hea, et al. A comparative study of deformable contour methods on medical image segmentation . Image. Vis. Comp. 26(2): 141-163, 2008
Volumetric Image Analysis. G. Lohmann, Wiley
P. Lenkiewicz, M. Pereira, et al. "The Whole Mesh Deformation Model for 2D and 3D Image Segmentation". Proceedings of 2009 16th IEEEICIP 2009
T. Heimann *, H-P Meinzer, Statistical shape models for 3D medical image segmentation: A review, Medical Image Analysis 13:543–563, 2009
F. Soares, M.Pereira et al. "Self-similarity Classification of Breast Tumour Lesions on Dynamic Contrast-enhanced Magnetic Resonance Images", Spr Int. Jrn of Comp. Ass. Rad. Surg., 5(1), 2011
Language Portuguese. Tutorial support is available in English.
Last updated on: 2017-01-19

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