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Open-source systems for image processing
Slinták, Vlastimil ; Zukal, Martin (referee) ; Mikulka, Jan (advisor)
The aim of this document is to describe open-source libraries for image processing — ITK, VTK and OpenCV. Their architectures are described and it is shown how to compile and use them in real applications. It also provides basic description of algorithms for image processing. In the second part of this document, we try to use ITK library with data-mining environment RapidMiner.
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Image Segmentation Using Height Maps
Moučka, Milan ; Kršek, Přemysl (referee) ; Španěl, Michal (advisor)
This thesis deals with image segmentation of volumetric medical data. It describes a well-known watershed technique that has received much attention in the field of medical image processing. An application for a direct segmentation of 3D data is proposed and further implemented by using ITK and VTK toolkits. Several kinds of pre-processing steps used before the watershed method are presented and evaluated. The obtained results are further compared against manually annotated datasets by means of the F-Measure and discussed.
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Open-source systems for image processing
Slinták, Vlastimil ; Zukal, Martin (referee) ; Mikulka, Jan (advisor)
The aim of this document is to describe open-source libraries for image processing — ITK, VTK and OpenCV. Their architectures are described and it is shown how to compile and use them in real applications. It also provides basic description of algorithms for image processing. In the second part of this document, we try to use ITK library with data-mining environment RapidMiner.
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|
|
Image Segmentation Using Height Maps
Moučka, Milan ; Kršek, Přemysl (referee) ; Španěl, Michal (advisor)
This thesis deals with image segmentation of volumetric medical data. It describes a well-known watershed technique that has received much attention in the field of medical image processing. An application for a direct segmentation of 3D data is proposed and further implemented by using ITK and VTK toolkits. Several kinds of pre-processing steps used before the watershed method are presented and evaluated. The obtained results are further compared against manually annotated datasets by means of the F-Measure and discussed.
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