English

Not applicable.

The graduate of the course is capable of:
- recommending and critically evaluating suitability of individual methods of medical image analysis to a particular purpose, based on theoretical and practical knowledge gained in the course,
- implementing these methods on a suitable software platform, possibly with commercial software,
- being a valid member of a research / experimental interdisciplinary team in the area of biomedical image data.

The generic knowledge on the Bachelor´s degree level is requested, namely in the area of mathematics and signal processing.

Not applicable.

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations. Techning methods include lectures and computer laboratories. Course is taking advantage of e-learning (Moodle) system. Students have to write a project/assignment during the course.

Requirements for completion of a course are elaborated by the lecturer responsible for the course every year;
basically:
- obtaining at least 12 points (out of 24 as course-unit credit based on active presence in demonstration exercises),
- successful passing of final written exam (up to 76 points)

1. Two-dimensional signal as image representation, 2D Fourier transform and 2D spectra, spatial 3D images, with temporal development (4D), profiles and slices.
2. Digital image representation, basic image properties, 2D DFT and further 2D transforms, discrete spectra, temporal sequences of 2D and 3D images – 4D data.
3. Pre-processing of medical image data 1: contrast and colour transforms.
4. Pre-processing of medical image data 2: Mask operators, sharpening, noise suppression, field homogenisation, spectral domain processing.
5. Local features, statistical and spectral parameters, parametric images, edge, line and corner detection, raw and pure edge representation.
6. Texture analysis: texture descriptors in original and spectral domains, feature based and syntactic texture analysis, texture parametric images, textural gradient.
7. Image segmentation 1: edge oriented segmentation and Hough transform, segmentation based on parametric and texture-parametric images, region oriented segmentation (region growing, splitting and merging), watershed method.
8. Image segmentation 2: flexible contours, level-set based contours, active contours, pattern recognition based segmentation.
9. Registration and fusion of medical images: similarity criteria, optimization registration, mono- and multimodal registration, fusion based acquisition of image information.
10. Properties of image data in planar X-ray imaging and in X-ray computer tomography (CT).
11. Reconstruction of images from tomographic data: reconstruction from CT projections – algebraic methods, reconstruction via spectral domain, filtered back-projection; modifications needed in nuclear imaging.
12. Image data properties in magnetic resonance imaging (MRI) and principles of image reconstruction in MRI. Properties of data in nuclear imaging, in ultrasonography, electron microscopy, infra-imaging and electric impedance tomography.
13. Medical image processing environment – hardware and software requirements, data formats of medical images, compatibility of image data. Trends in analysis of medical multidimensional and multimodal images.

Not applicable.

The goal of the course is to enable the students gaining an overview of, and insight into, the methods of medical image analysis; acquiring practical experience in software realisation of the methods.

Delimitation of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year (see Rozvrhové jednotky).
Basically:
- obligatory computer-lab tutorial
- voluntary lecture

Not applicable.

Not applicable.

J. Jan: Medical Image Processing,Reconstruction and Restoration, CRC Taylor and Francis 2006 (EN)
J. Jan: Medical ImageProcessing, Reconstruction and Analysis 2nd edition CRC Press, Taylor & Francis 2020 (EN)
M. Nixon: Feature Extraction and Image Processing for Computer Vision, Academic Press (EN)

M. Sonka, V. Hlavac: Image Processing, Analysis And Machine Vision, Cengage Learning, 2017 (EN)