Robust Brain Segmentation Using Histogram Scale-Space Analysis and Mathematical Morphology
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Fully automated biomedical image segmentation by self-organized model adaptation
Neural Networks - 2004 Special issue: New developments in self-organizing systems
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The segmentation technique presented in this paper is part of ongoing research work to develop a new imaging and segmentation technique to generate a three-dimensional structure of the brain in order to enhance the visualization of brain fiber tracts in diffusion tensor images using developmental software by Phillips. The visualization enhancement will be achieved by displaying the 3D brain structure as a semi-transparent image, such that key anatomical landmarks, as well as the mathematically constructed fiber tracts, may be observed within the brain. This paper describes the development and implementation of task-oriented techniques for automatic brain segmentation to locate and display the lateral ventricles within the brain structure in order to support the 3D rendering as landmarks in the complete 3D structure. With an automatic segmentation technique, the software will calculate and discern the different regions in the brain without ad-hoc interventions. This is a difficult task because the brain structure is different from one patient to the next, in addition to the fact that the data to be analyzed is from patients who may already have brain abnormalities. Ten T1-weighted 3D Gradient Echo MR images obtained from patients at Miami Children's Hospital (MCH) were used for the development of this technique.