450-4086/02 – Medical Imaging Systems II (LZS II)

Gurantor departmentDepartment of Cybernetics and Biomedical EngineeringCredits4
Subject guarantorIng. Jan Kubíček, Ph.D.Subject version guarantorIng. Jan Kubíček, Ph.D.
Study levelundergraduate or graduate
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
CER275 doc. Ing. Martin Černý, Ph.D.
KUB631 Ing. Jan Kubíček, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2
Part-time Credit and Examination 0+16

Subject aims expressed by acquired skills and competences

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.

Teaching methods

Lectures
Individual consultations
Experimental work in labs

Summary

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.

Compulsory literature:

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.

Recommended literature:

GONZALEZ, Rafael C. a Richard E. WOODS. Digital image processing. 3rd ed. Upper Saddle River, N.J.: Prentice Hall, c2008. ISBN 978-0131687288.

Way of continuous check of knowledge in the course of semester

Protocols from laboratory exercises. Compulsory attendance at lessons of at least 80% of the lessons taught.

E-learning

Other requirements

The prerequisite is the subject Medical Imaging Systems I. The student must have knowledge of programming in Matlab.

Prerequisities

Subject codeAbbreviationTitleRequirement
450-4073 LZS I Medical Imaging Systems I Compulsory

Co-requisities

Subject has no co-requisities.

Subject syllabus:

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.

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 40  20
        Examination Examination 60 (60) 30
                Written exam Written examination 40  20
                Oral exam Oral examination 20  5
Mandatory attendence parzicipation: Protocols from laboratory exercises. Oral and written exam.

Show history
Part-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 40  20
        Examination Examination 60 (60) 30
                Written exam Written examination 40  20
                Oral exam Oral examination 20  5
Mandatory attendence parzicipation: Semestral project. Oral and written exam.

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2022/2023 (N0988A060002) Biomedical Engineering MZD K English Ostrava 1 Compulsory study plan
2022/2023 (N0988A060002) Biomedical Engineering MZD P English Ostrava 1 Compulsory study plan
2021/2022 (N0988A060002) Biomedical Engineering MZD P English Ostrava 1 Compulsory study plan
2021/2022 (N0988A060002) Biomedical Engineering MZD K English Ostrava 1 Compulsory study plan
2020/2021 (N0988A060002) Biomedical Engineering MZD P English Ostrava 1 Compulsory study plan
2020/2021 (N0988A060002) Biomedical Engineering MZD K English Ostrava 1 Compulsory study plan
2019/2020 (N0988A060002) Biomedical Engineering MZD P English Ostrava 1 Compulsory study plan
2019/2020 (N0988A060002) Biomedical Engineering MZD K English Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner
V - ECTS - mgr. 2022/2023 Full-time English Optional 401 - Study Office stu. block
V - ECTS - mgr. 2021/2022 Full-time English Optional 401 - Study Office stu. block