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Proceedings Paper

Three dimensional multi-scale visual words for texture-based cerebellum segmentation
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Paper Abstract

Segmentation of the various parts of the brain is a challenging area in medical imaging and it is a prerequisite for many image analysis tasks useful for clinical research. Advances have been made in generating brain image templates that can be registered to automatically segment regions of interest in the human brain. However, these methods may fail with some subjects if there is a significant shape distortion or difference from the proposed models. This is also the case of newborns, where the developing brain strongly differs from adult magnetic resonance imaging (MRI) templates. In this article, a texture-based cerebellum segmentation method is described. The algorithm presented does not use any prior spatial knowledge to segment the MRI images. Instead, the system learns the texture features by means of a multi-scale filtering and visual words feature aggregation. Visual words are a commonly used technique in image retrieval. Instead of using visual features directly, the features of specific regions are modeled (clustered) into groups of discriminative features. This means that the final feature space can be reduced in size and also that the visual words in local regions are really discriminative for the given data set. The system is currently trained and tested with a dataset of 18 adult brain MRIs. An extension to the use with newborn brain images is being foreseen as this could highlight the advantages of the proposed technique. Results show that the use of texture features can be valuable for the task described and can lead to good results. The use of visual words can potentially improve robustness of existing shape-based techniques for cases with significant shape distortion or other differences from the models. As the visual words based techniques are not assuming any prior knowledge such techniques could be used for other types of segmentations as well using a large variety of basic visual features.

Paper Details

Date Published: 14 February 2012
PDF: 12 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83142Z (14 February 2012); doi: 10.1117/12.911523
Show Author Affiliations
Antonio Foncubierta-Rodríguez, Univ. of Applied Sciences Western Switzerland (Switzerland)
Adrien Depeursinge, Univ. of Applied Sciences Western Switzerland (Switzerland)
Univ. Hospital of Geneva (Switzerland)
Laura Gui, Univ. Hospital of Geneva (Switzerland)
Henning Müller, Univ. of Applied Sciences Western Switzerland (Switzerland)
Univ. Hospital of Geneva (Switzerland)

Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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