The doctoral study provides the graduates of MSc study in the area of biomedical electronics and biocybernetics with a higher degree of education, deepening their theoretical background.
The study is aimed at deepening of theoretical knowledge of students in advanced mathematics, physics and in disciplins forming the theoretical basis of the chosen field. Also, necessary experience in experimental work and in processing of the obtained results should be provided, including exploitation of advanced methods of applied informatics.
The final goal is that the students will master methods of scientific research.

A graduate of the doctoral study is expected to be a distinct personality with a recognised research result, wide horizon of knowledge and ability to solve complex scientific and technical-research tasks in the field of biomedical electronics and biocybernetics and in neighbouring fields.
Maximum flexibility and professional adaptivity is the undisputed property of a graduate of doctoral study.
The graduates of the doctoral study in biomedical electronics and biocybernetics will be capable of working as scientists and researchers involved in basic or applied research namely in medical or biological area, as leading specialists in development and construction departments of research and development institutions, and in manufacturing enterprises or institutions exploiting advanced technology, namely in biomedical field.

Graduate of doctoral programme should be a strong personality with substantial scientific results, large horizon and ability to solve complex scientific and research technical tasks in area of biomedical electronics and biocybernetics. He/she will have maximum flexibility and professional adaptability in wide area of biomedical engineering. Graduates will be able to work as scientific and research staff in basic and applied research, as specialists in development, construction and production, in research institutes and at industrial companies and users of medical devices and applied information technologies in medicine and biology.

prof. Ing. Jiří Jan, CSc.

1. Acquisition and analysis of retinal video sequences from experimental ophthalmoscope

   This topic is focused on quality improvement of images from experimental video ophthalmoscope. It will cover work on experimental camera, design of specific eye models, implementation of acquisition software etc. The image processing part will cover image registration of frames, analysis of aligned retinal sequences - blood-vessel pulsation estimation, optic disc variability assessment or eye movement extraction. Advanced methods from image and signal processing area will be used. The project should improve the applications of video-ophthalmoscope in eye and neurological diseases. This project is solved in cooperation with Erlangen University, Germany.

   Tutor: Kolář Radim, doc. Ing., Ph.D.

2. Acquisition and analysis of retinal video sequences from experimental ophthalmoscope

   This topic is focused on quality improvement of images from experimental video ophthalmoscope. It will cover work on experimental camera, design of specific eye models, implementation of acquisition software etc. The image processing part will cover image registration of frames, analysis of aligned retinal sequences - blood-vessel pulsation estimation, optic disc variability assessment or eye movement extraction. Advanced methods from image and signal processing area will be used. The project should improve the applications of video-ophthalmoscope in eye and neurological diseases. This project is solved in cooperation with Erlangen University, Germany.

   Tutor: Kolář Radim, doc. Ing., Ph.D.

3. Advanced detection of fingerprint liveness in Biometrics

   The theme of this dissertation is aimed on software recognition of fingerprint liveness from image fingerprint data and it can be divided into two parts. The goal of the first part is to find features, which are suitable for detection of various types of false fingerprints. The goal of the second part is to design an advanced algorithm for recognition of false fingerprints from real ones regardless on type of false fingerprint, sensing device, or image data resolution. Applicants are expected to be familiar with Matlab programming and have an overview in the area of processing and analysis of image data.

   Tutor: Vítek Martin, Ing., Ph.D.

4. Advanced P wave detection in ECG signals

   The theme of this dissertation is aimed on detection of P wave in experimental and clinical ECG records and it can be divided into two parts. The goal of the first part is to map the current principles of P wave detection and describe P waves, which are difficult to detect reliably. The goal of the second part is to design an advanced P wave detection algorithm, which will increase the current reliability of its detection especially in cases, where the size of P wave is comparable with the size of interference or when P wave is unbound. Applicants are expected to be familiar with Matlab programming and have an overview in the area of processing and analysis of 1D signals.

   Tutor: Vítek Martin, Ing., Ph.D.

5. Analysis of signals from sleep laboratory

   The work will be focused on the processing of polysomnographic (PSG) signals that are taken in the Faculty hospital in St. Ann in Brno for the International Clinical Research Centre (ICRC) for the study of sleep apnea in patients with cardiological diseases. We suppose the development of new methods for automated sleep apnea detection. Furthermore, we expect the development of new algorithms, which may contribute to the automatic classification of sleep stages. We expect cooperation with the International Clinical Research Centre (ICRC) in Brno.

   Tutor: Kozumplík Jiří, doc. Ing., CSc.

6. Analysis of 3D CT image data aimed at deformable model based segmentation of different types of skeletal elements

   Design and development of new methods of CT image data analysis, namely for reliable segmentation of different types of skeletal elements based on, aimed at different clinical applications (cooperation with international and regional medical institutions). The theme is a part of a long-term project supported by the firm PHILIPS NEDERLAND in frame of a contract, enabling to offer – to successful students – an interesting regular increase added to the stipend. The applicant is expected, besides of being interested in research work in a renowned team, to be capable of formulating structured algorithms, programming in MATLAB environment and mastering basic methodology of image processing and analysis.

   Tutor: Jan Jiří, prof. Ing., CSc.

7. Analysis of 3D CT image data aimed at following of temporal development of local bone mineral density

   Design and development of new methods of CT image data analysis, namely for determination of local bone mineral density and following its temporal development, aimed at different clinical applications (cooperation with international and regional medical institutions). The theme is a part of a long-term project supported by the firm PHILIPS NEDERLAND in frame of a contract, enabling to offer – to successful students – an interesting regular increase added to the stipend. The applicant is expected, besides of being interested in research work in a renowned team, to be capable of formulating structured algorithms, programming in MATLAB environment and mastering basic methodology of image processing and analysis.

   Tutor: Jan Jiří, prof. Ing., CSc.

8. Speckle tracking in ultrasound contrast imaging

   The theme of this thesis is aimed on speckle tracking methods used in contrast enhanced ultrasound imaging. The main goal is the development method, which will be used for estimation of structural movement and also for estimation of important perfusion parameters. The applicant is expected, besides of being interested in research work, to be familiar with programming and have an overview of the area of image processing.

   Tutor: Harabiš Vratislav, Ing., Ph.D.

9. The application of tensor calculus to classification of biomedical signals

   Recent approaches combine different methods from different areas to create new sufficiently powerful methods for hybrid signals processing. One of the possible way is the application of advanced tensor calculus to perform an effective discriminant analysis or clustering. Tensor discriminant analysis is a powerful tool for multi-way data discriminant analysis. For multi-way data, the traditional way is to vectorize the samples and then use ordinary 2D discriminant analysis methods, such as linear discriminant analysis (LDA), k-nearest neighbors (KNN), support-vector machine (SVM), etc. However, this way often causes overfitting problem when the dimension of features is higher than the number of samples. Tensor discriminant analysis is a promising way to overcome this problem.

   Tutor: Tkacz Ewaryst, prof., Ph.D.,D.Sc.