|
Lecturer(s)
|
-
Nedoma Jiří, RNDr. CSc.
-
Tkadlec Emil, prof. MVDr. CSc.
|
|
Course content
|
Aims of this course are (1) to provide students with a general overview of potential and possibilities of contemporary image analysis and its applications in life sciences, (2) to train students in using basic tools and procedures of image analysis in quantitative evaluation of image information (at the intuitive level - without knowledge of mathematical background), and (3) train students in effective planning and managing the steps required for working on their independent projects: image acquisition and processing, measurements, task automation, spreadsheet-based data processing and presentation. Content of lectures: (1) Introduction to image analysis, terminology, history, trends, hardware and software. (2) Input devices (camera, scanner etc.). Image formats, compression, archiving, calibration, optimum resolution. (3) Image processing (for image presentation and visual interpretation, for image segmentation and quantification). (4) Transformation of colour and monochromatic images, logical operations with images, manual measurements on live and achieved images. (5) Work with spatial information, z-stacks, deconvolution, digital focussing, 3D reconstruction. (6) Image segmentation (manual, automated, multicriterial). (7) Binary images, binary operations. (8) Automated object measurements, measured parameters: interpretation, limitations. (9) Procedure automation, creation of macros. (10) Effective processing of large data sets (data storage, sorting, categorization, presentation) in a spreadsheet processor.
|
|
Learning activities and teaching methods
|
|
unspecified
|
|
Learning outcomes
|
Objectives: Aims of this course are (1) to provide students with a general overview of potential and possibilities of contemporary image analysis and its applications in life sciences, (2) to train students in using basic tools and procedures of image analysis in quantitative evaluation of image information (at the intuitive level - without knowledge of mathematical background), and (3) train students in effective planning and managing the steps required for working on their independent projects: image acquisition and processing, measurements, task automation, spreadsheet-based data processing and presentation. Content of lectures: (1) Introduction to image analysis, terminology, history, trends, hardware and software. (2) Input devices (camera, scanner etc.). Image formats, compression, archiving, calibration, optimum resolution. (3) Image processing (for image presentation and visual interpretation, for image segmentation and quantification). (4) Transformation of colour and monochromatic images, logical operations with images, manual measurements on live and achieved images. (5) Work with spatial information, z-stacks, deconvolution, digital focussing, 3D reconstruction. (6) Image segmentation (manual, automated, multicriterial). (7) Binary images, binary operations. (8) Automated object measurements, measured parameters: interpretation, limitations. (9) Procedure automation, creation of macros. (10) Effective processing of large data sets (data storage, sorting, categorization, presentation) in a spreadsheet processor.
|
|
Prerequisites
|
unspecified
|
|
Assessment methods and criteria
|
unspecified
Exam - good knowledge and orientation in modern theory of life histories on selected subjects
|
|
Recommended literature
|
-
Image analysis: principles and practice. A technical handbook. Joyce-Loebl, UK, 1985.
-
Wilkinson, MHF, Schut, F (eds). (1998). Digital image analysis of microbes .. Wiley, Chichester.
|