Initialisation of 3D level set for hippocampus segmentation from volumetric brain MR images

Maryam Hajiesmaeili; Jamshid Dehmeshki; Bashir Bagheri Nakhjavanlo; Tim Ellis

doi:10.1117/12.2064402

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16 April 2014 Initialisation of 3D level set for hippocampus segmentation from volumetric brain MR images

Maryam Hajiesmaeili, Jamshid Dehmeshki, Bashir Bagheri Nakhjavanlo, Tim Ellis

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Proceedings Volume 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014); 91591D (2014) https://doi.org/10.1117/12.2064402
Event: Sixth International Conference on Digital Image Processing, 2014, Athens, Greece

Abstract

Shrinkage of the hippocampus is a primary biomarker for Alzheimer’s disease and can be measured through accurate segmentation of brain MR images. The paper will describe the problem of initialisation of a 3D level set algorithm for hippocampus segmentation that must cope with the some challenging characteristics, such as small size, wide range of intensities, narrow width, and shape variation. In addition, MR images require bias correction, to account for additional inhomogeneity associated with the scanner technology. Due to these inhomogeneities, using a single initialisation seed region inside the hippocampus is prone to failure. Alternative initialisation strategies are explored, such as using multiple initialisations in different sections (such as the head, body and tail) of the hippocampus. The Dice metric is used to validate our segmentation results with respect to ground truth for a dataset of 25 MR images. Experimental results indicate significant improvement in segmentation performance using the multiple initialisations techniques, yielding more accurate segmentation results for the hippocampus.

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Maryam Hajiesmaeili, Jamshid Dehmeshki, Bashir Bagheri Nakhjavanlo, and Tim Ellis "Initialisation of 3D level set for hippocampus segmentation from volumetric brain MR images", Proc. SPIE 9159, Sixth International Conference on Digital Image Processing (ICDIP 2014), 91591D (16 April 2014); https://doi.org/10.1117/12.2064402

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