Overall objectives:

1 - Convey the concepts associated with the digital image formation process.
2 - Contextualize digital image processing (DIP) in the field of acquisition and analysis of medical images.
3 - Convey the theoretical foundations of DIP.
4 - Provide students with knowledge of the main techniques for the acquisition and analysis of medical images.
5 - Equip students with the knowledge and ability to apply techniques for enhancing medical image quality.

Syllabus:

1 - Introduction
2 - Fundamentals of Digital Image - Format, acquisition, digitization. Binary representation, storage, visualization.
3 - Spatial Processing - Histograms, equalization, and filtering.
4 - Spectral Processing - Fourier transforms, spectral filters, FFT, convolution, and correlation.
5 - Image Restoration - Degradation/restoration model, Noise models, and deconvolution.
6 - Color and Shape Processing - Color models, Shape processing, feature detection.
7 - Image Reconstruction - Data organization, Radon transform, analytical and iterative methods.
8 - Nature of Biomedical Images - Radiography, Tomography, Magnetic Resonance Imaging, Nuclear Imaging, Ultrasound.
9 - Artifact Removal and Morphology - Artifact characterization and filtering. Morphological operations.
10 - Image Enhancement - Histogram manipulation. Convolution and enhancement.
11 - Line, Edge, and Region Detection - Detection techniques. Thresholding, binarization, and segmentation.

Literature/Sources:

Semmlow, J.L. , 2004 , Biosignal and Medical Image Processing (2nd ed.) , CRC Press.
Russ, John C , 2006 , The image processing handbook , CRC Press.
Dhawan, A. P. , 2011 , Medical image analysis , John Wiley & Sons

Assesssment methods and criteria:

Classification Type: Quantitativa (0-20)

Evaluation Methodology:
Esta unidade curricular está organizada em sessões teóricas e práticas. Durante as sessões teóricas, são abordados os conteúdos programáticos planeados. As sessões práticas decorrem em sala de informática, onde os alunos têm a oportunidade de desenvolver aplicações para o processamento de imagem médica, utilizando bibliotecas de processamento de imagem e a linguagem de programação Python. O método de avaliação consiste na realização de uma prova escrita e no design e implementação de uma aplicação de análise e/ou processamento de imagem, sendo que ambas as componentes de avaliação têm um peso de 50% da classificação final.