文献类型：会议论文

英文题名：Random local binary pattern based label learning for multi-atlas segmentation

作者：Zhu, Hancan[1,2,3];Cheng, Hewei[1,2];Fan, Yong[1,2]

机构：[1]Chinese Acad Sci, Ctr Brainnetome, Beijing 100190, Peoples R China;[2]Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;[3]Shaoxing Univ, Coll Math Phys & Informat, Shaoxing 312000, Peoples R China

会议论文集：Conference on Medical Imaging - Image Processing

会议日期：FEB 24-26, 2015

会议地点：Orlando, FL

语种：英文

外文关键词：multi-atlas segmentation; hippocampus segmentation; random local binary pattern; label fusion

外文摘要：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.