|
| |
|
| |
|
|
Revision of the electronic diabetic file at Clinic of Diabetology IKEM
Fialová, Lenka ; Blaha, Milan (referee) ; Provazník, Ivo (advisor)
Electronic diabetic record (EDR) is an electronic medical record used in Diabetology Clinics, Instittute for Clinical and Experimental Medicine (IKEM), Prague. EDR is complex record of patient statistical and clinical data. It´s complexity and large time demands on filling in all items make EDR not user friendly. Thus, benefits of electronic medical record are not fully utilized. The paper describes solution that leads to simpler user interface as component of outpatient message allowing easy entering data. The new interface has been designed and created in the form of program module. There are described other procedures to implement module into IKEM information system Zlatokop. At the end, there are discussed the advantages and shortcomings of this revised vision and current status of project of EDR. The first part of the work is about the basic concepts and problems regarding EDR. It is about e-health, specially about the electronic health record, databases, specially about Caché, which is used in Zlatokop, about diabetes mellitus and about user experience.
|
|
|
Detection and analysis of eye movement
Křístek, Jakub ; Blaha, Milan (referee) ; Provazník, Ivo (advisor)
This thesis deals with the movements of eyes and it describes the matters related to its measurement and data processing. Theoretic part is focused on eye‘s anatomy and electrophysiology, types of eye’s muscles and their movements. Even more methods, not just used electrooculography, are described thoroughly. In the second part of this thesis are designed two experiments and the measurement is realized on a group of ten tested subjects. Acquired signals are saved and processed in Matlab software, statistically analyzed and the results are interpreted.
|
|
|
Analysis of cytology images
Pavlík, Jan ; Blaha, Milan (referee) ; Kolář, Radim (advisor)
This master’s thesis is focused on automating the process of differential leukocyte count in peripherial blood using image processing. It deals with the design of the processing of digital images - from scanning and image preprocessing, segmentation nucleus and cytoplasm, feature selection and classifier, including testing on a set of images that were scanned in the context of this work. This work introduces used segmentation methods and classification procedures which separate nucleus and the cytoplasm of leukocytes. A statistical analysis is performed on the basis of these structures. Following adequate statistical parameters, a set of features has been chosen. This data then go through a classification process realized by three artificial neural networks. Overall were classified 5 types of leukocytes: neutropfiles, lymphocytes, monocytes, eosinophiles and basophiles. The sensitivity and specificity of the classification made for 4 out of 5 leukocyte types (neutropfiles, lymphocytes, monocytes, eosinophiles) is higher than 90 %. Sensitivity of classiffication basophiles was evaluated at 75 % and specificity at 67 %. The total ability of classification has been tested on 111 leukocytes and was approximately 91% successful. All algorithms were created in the MATLAB program.
|
|
| |
|
| |
|
| |
|
| |
|
| |
guest ::
RSS feed.