Active shape models—their training and application
Computer Vision and Image Understanding
Digital Image Processing
Grey-Level Morphology Based Segmentation of MRI of the Human Cortex
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
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This paper presents an algorithm for improving the segmentation from a semi-automatic artificial neural network (ANN) hippocampus segmentation of co-registered T1-weigthted and T2-weighted MRI data, in which the semi-automatic part is the selection of a bounding-box. Due to the morphological complexity of the hippocampus and the difficulty of separating from adjacent structures, reproducible segmentation using MR imaging is complicated. The grey-level thresholding uses a histogram-based method to find robust thresholds. The T1-weighted data is grey-level eroded and dilated to reduce leaking from hippocampal tissue to the surrounding tissues and selecting possible foreground tissue. The method is a 3D approach, it uses 3 × 3 × 3 structure element for the grey-level morphology operations and the algorithms are applied in the three directions, sagittal, axial, and coronal, and the results are then combined together.