| Course Unit Code | 450-4086/02 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Optional |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | |
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| Semester when the Course Unit is delivered | Summer Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | English |
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| Prerequisites and Co-Requisites | |
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| Prerequisities | Course Unit Code | Course Unit Title |
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| 450-4073 | Medical Imaging Systems I |
| Name of Lecturer(s) | Personal ID | Name |
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| CER275 | prof. Ing. Martin Černý, Ph.D. |
| KUB631 | Ing. Jan Kubíček, Ph.D. |
| Summary |
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| Subject deals with the mathematical methods for processing, modelling and information extraction from the medical image data. Individual methods will always be put to a context of the medical image data processing, and applications which are recent for the clinical practice needs. Subject is systematically divided into three essential parts. The first part of the subject deals with the basic techniques serving for the medical image data preprocessing. In this part, the geometrical and brightness transformations will be discussed. Consequently, the image filtration on the base of the image convolution in the spatial domain, and the frequency image filtration will be discussed. Lastly, the image binarization analysis, morphological operations, and applications of those methods for modeling of the medical image objects will be discussed. The second part of the subject deals with the image segmentation and classification constituting basic elements for modeling and information extraction from the medical image data. In the last part, the MR and CT reconstruction techniques will be discussed preciselly. |
| Learning Outcomes of the Course Unit |
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| The student will be able to enumerate and define procedures for medical image data analysis methods and procedures. The student will be able to explain these procedures and then apply them to selected image data. He will be able to experiment with them and evaluate their contribution for medical images analysis. |
| Course Contents |
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Syllabus
Lessons and Exercises
1. Basics of the medical image data processing in the SW MATLAB and Simulink, introduction to the imaging process,
parameters of the exposition, photometric and radiometric parameters and definition of elementary unit of the 2D
and 3D image.
2. Basic techniques for adjustment and representation of the digital image: discretization, mathematical process
of the image signal evaluation of the imaging quality, histogram, models of entropy, color modulations and basic
models of the image representation.
3. Brightness transformations: basic types and mathematical models for the brightness transformations, and contrast
transformations. Application of the brightness transformations for optimization of the brightness characteristics
of the medical image objects in a context of the image preprocessing.
4. Geometric transformations: basic types of the transformations, algorithms for rotation and the image translation,
affine transformation, algorithms for the image interpolation, RoI, and VoI definition.
5. Spatial image analysis: mathematical description of the image convolution for the image filtration, definition
of the average and median filter in the spatial area, application of the filtration procedures in the spatial area
on the medical image data.
6. Frequency image analysis: representation of the spatial image frequencies, 2D Fourier transformation, algorithms
for the FFT, filter proposal in the frequency domain, and application of the filtration procedures in the frequency
domain on the medical image data.
7. Analysis of the image noise: the noise mathematical models, noise parameters, selected implementation of the
image noise on the CT and MR data, and analysis of the image noise evaluation.
8. Edge detection: definition of the edge points, image edge boundaries, basic models of the image edge, and basic
operators for the edge detection in the medical image data.
9. Detection of object in image: image segmentation based on the histogram thresholding, fuzzy thresholding, and
algorithms for the regional image segmentation.
10. Iterative segmentation methods: image boundaries detection on the base of the active contours and level sets,
analysis of the basic algorithms and parameters in an application of selected objects in the medical images.
11. Image classification: principles of the data classification, basic models for the medical image data classification,
and image features extraction.
12. Methods of the artificial intelligence: model of the neuron, basic neural networks, classification and segmentation
of the image on the base of the neural network, and deep learning.
13. Cluster analysis: analysis of the K means and FCM, application of methods for the image segmentation and classification.
14. Basic reconstruction techniques for the CT and MR: the sinogram analysis, back projection, filtered back projection,
iterative CT reconstruction, k-space, and MR image signal.
For each topic of lessons will be realized practival exercise using MATLAB. |
| Recommended or Required Reading |
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| Required Reading: |
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DESERNO, Thomas M. Biomedical image processing. Heidelberg: Springer, c2011. Biological and medical physics, biomedical engineering. ISBN 978-3-642-15816-2.
[2] NAJARIAN, Kayvan. Biomedical signal and image processing. 2nd ed. Boca Raton: Taylor & Francis/CRC Press, 2012. ISBN 978-1439870334.
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KUBÍČEK, Jan. Zpracování medicínských obrazových dat. Opava: Slezská univerzita v Opavě, Filozoficko-přírodovědecká fakulta, Ústav fyziky, 2014. ISBN 978-80-7248-941-1.
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| Recommended Reading: |
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| GONZALEZ, Rafael C. a Richard E. WOODS. Digital image processing. 3rd ed. Upper Saddle River, N.J.: Prentice Hall, c2008. ISBN 978-0131687288. |
| SOJKA, Eduard. Digitální zpracování a analýza obrazů. Ostrava: VŠB - Technická univerzita Ostrava, 2000. ISBN 80-7078-746-5. |
| Planned learning activities and teaching methods |
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| Lectures, Individual consultations, Experimental work in labs |
| Assesment methods and criteria |
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| Tasks are not Defined |