This unique text/reference highlights a selection of practical applications of advanced image analysis methods for medical images. The book covers the complete methodology for processing, analysing and interpreting diagnostic results of sample CT images. The text also presents significant problems related to new approaches and paradigms in image understanding and semantic image analysis. To further engage the reader, example source code is provided for the implemented algorithms in the described solutions. Features: describes the most important methods and algorithms used for image analysis; examines the fundamentals of cognitive computer image analysis for computer-aided diagnosis and semantic image description; presents original approaches for the semantic analysis of CT perfusion and CT angiography images of the brain and carotid artery; discusses techniques for creating 3D visualisations of large datasets; reviews natural user interfaces in medical imaging systems, including GDL technology.The proposed vessel lumen segmentation framework proved to be an efficient algorithm, and the quality of the final solution measured with Dicea#39;s coefficient is respectively high for this type of ... The first is that the deformable contour framework may produce inconsistently segmented regions, as shown in Fig. 3.13. ... It is difficult to predict how the region growing procedure will evolve in each step. ... Our implementation is an adaptation to C# of a Matlab code written by Shawn Lanktonanbsp;...
|Title||:||Natural User Interfaces in Medical Image Analysis|
|Author||:||Marek R. Ogiela, Tomasz Hachaj|
|Publisher||:||Springer - 2014-06-07|