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Proceedings Paper

Individual 3D region-of-interest atlas of the human brain: knowledge-based class image analysis for extraction of anatomical objects
Author(s): Gudrun Wagenknecht; Hans-Juergen Kaiser; Osama Sabri; Udalrich Buell
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Paper Abstract

After neural network-based classification of tissue types, the second step of atlas extraction is knowledge-based class image analysis to get anatomically meaningful objects. Basic algorithms are region growing, mathematical morphology operations, and template matching. A special algorithm was designed for each object. The class label of each voxel and the knowledge about the relative position of anatomical objects to each other and to the sagittal midplane of the brain can be utilized for object extraction. User interaction is only necessary to define starting, mid- and end planes for most object extractions and to determine the number of iterations for erosion and dilation operations. Extraction can be done for the following anatomical brain regions: cerebrum; cerebral hemispheres; cerebellum; brain stem; white matter (e.g., centrum semiovale); gray matter [cortex, frontal, parietal, occipital, temporal lobes, cingulum, insula, basal ganglia (nuclei caudati, putamen, thalami)]. For atlas- based quantification of functional data, anatomical objects can be convoluted with the point spread function of functional data to take into account the different resolutions of morphological and functional modalities. This method allows individual atlas extraction from MRI image data of a patient without the need of warping individual data to an anatomical or statistical MRI brain atlas.

Paper Details

Date Published: 6 June 2000
PDF: 12 pages
Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); doi: 10.1117/12.387694
Show Author Affiliations
Gudrun Wagenknecht, Univ. Hospital/Aachen Univ. of Technology (Germany)
Hans-Juergen Kaiser, Univ. Hospital/Aachen Univ. of Technology (Germany)
Osama Sabri, Univ. Hospital/Aachen Univ. of Technology (Germany)
Udalrich Buell, Univ. Hospital/Aachen Univ. of Technology (Germany)


Published in SPIE Proceedings Vol. 3979:
Medical Imaging 2000: Image Processing
Kenneth M. Hanson, Editor(s)

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