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Proceedings Paper

Random local binary pattern based label learning for multi-atlas segmentation
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Paper Abstract

Multi-atlas segmentation method has attracted increasing attention in the field of medical image segmentation. It segments the target image by combining warped atlas labels according to a label fusion strategy, usually based on the intensity information of the target and atlas images. However, it has been demonstrated that image intensity information itself is not discriminative enough for distinguishing different subcortical structures in brain magnetic resonance (MR) images. Recent advance in multi-atlas based segmentation has witnessed success of label fusion methods built on informative image features. The key component in these methods is the image feature extraction. Conventional image feature extraction methods, such as textural feature extraction, are built on manually designed image filters and their performance varies when applied to different segmentation problems. In this paper, we propose a random local binary pattern (RLBP) method to generate image features in a random fashion. Based on RLBP features, we use a local learning strategy to fuse labels in multi-atlas based segmentation. Our method has been validated for segmenting hippocampus from MR images. The experiment results have demonstrated that our method can achieve competitive segmentation performance as the state-of-the-art methods.

Paper Details

Date Published: 20 March 2015
PDF: 8 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94131B (20 March 2015); doi: 10.1117/12.2082381
Show Author Affiliations
Hancan Zhu, Institute of Automation (China)
Shaoxing Univ. (China)
Hewei Cheng, Institute of Automation (China)
Yong Fan, Institute of Automation (China)

Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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