摘要
针对目前基于数字人脑切片图像的分割算法较少,分割精度和有效性较低等不足,提出一种基于稀疏自编码器(SAE)深度特征学习的分割算法.在特征提取阶段,采用从粗到精两级方式对SAE进行训练,以增强模型学习到的深度特征的鉴别能力;在分类阶段,使用softmax分类器进行目标分割.对中国可视化人体(CVH)数据集的脑白质分割及三维重建的实验结果表明,相对于其他传统的手工特征(如图像强度特征、方向梯度直方图特征和主成分分析特征),SAE提取的图像深度特征具有更强的鉴别能力,显著地提高了分割精度.
There are few algorithms for segmenting cryosection brain images, and most existing segmentation techniques presented limited precision and low efficiency. To address these problems, this paper proposed a novel deep feature learning-based segmentation algorithm using sparse autoencoder（SAE）. At the stage of feature extraction, SAE is trained twice to enhance the discriminability of the deep-learned feature representations. At the stage of classification, a softmax classifier is used for segmenting different objects. Experimental results of white matter segmentation on the Chinese Visible Human（CVH） dataset and its 3-D reconstruction show that, the learned deep feature performs much better in discriminability compared with other representative hand-crafted features（such as intensity, histogram of oriented gradient and principal components analysis） and achieves higher recognition accuracy.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2016年第8期1297-1305,共9页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(60903142
61190122)
中国博士后基金特别资助(2013T60841)
中央高校基本业务费项目(106112015CDJXY120003)
