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Image Analysis and Processing, International Conference on (2003)
Mantova, Italy
Sept. 17, 2003 to Sept. 19, 2003
ISBN: 0-7695-1948-2
pp: 277
Roger Hult , Uppsala University and Karolinska Institutet
<p>This paper presents an algorithm that continues segmentation from a semi automatic artificial neural network (ANN) segmentation of the hippocampus of registered T1-weighted and T2-weighted MRI data. Due to the morphological complexity of the hippocampus and difficulty of separating from adjacent structures, reproducible segmentation using MR imaging is complicated.</p> <p>The human intervention in the ANN approach, consists of selecting a bounding-box. Grey-level dilated and grey-level eroded versions of the T1-weighted and T2-weighted data are used to minimise leaking from hippocampus to surrounding tissue combined with possible foreground tissue. The segmentation algorithm uses a histogram-based method to find accurate threshold values. Grey-level morphology is a powerful tool to break stronger connections between the hippocampus and surrounding regions than is otherwise possible. The method is 3D in the sense that all grey-level morphology operations use a 3 ? 3 ? 3 structure element and the herein described algorithms are applied in the three directions, sagittal, axial, and coronal, and the result are then combined together.</p>
Roger Hult, "Grey-Level Morphology Combined with an Artificial Neural Networks Aproach for Multimodal Segmentation of the Hippocampus", Image Analysis and Processing, International Conference on, vol. 00, no. , pp. 277, 2003, doi:10.1109/ICIAP.2003.1234063
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