基于稀疏随机投影的SIFT医学图像配准算法

J4 ›› 2015, Vol. 32 ›› Issue (3): 283-289.

基于稀疏随机投影的SIFT医学图像配准算法

杨飒，郑志硕

1.广东第二师范学院物理系，广州 510310; 2.华南理工大学计算机科学与工程学院，广州 510640

收稿日期:2015-01-14 修回日期:2015-03-03 出版日期:2015-05-28 发布日期:2015-05-28
通讯作者: 杨飒（1970-），女，湖南人，硕士，高级实验师，从事图像信息处理的研究 E-mail:yangsa@gdei.edu.cn
基金资助:
国家自然科学基金资助项目（21302023）

Medical Image Registration Algorithm Based on Sparse Random Projection and SIFT Transform

YANG Sa, ZHENG Zhi-shuo

1. Department of Physics, Guangdong University of Education, Guangzhou 510640 China; 2. School of Computer Science and Engineering，South China University of Technology，Guangzhou 510640 China

Received:2015-01-14 Revised:2015-03-03 Published:2015-05-28 Online:2015-05-28

摘要/Abstract

摘要：

针对尺度不变特征变换(Scale-Invariant Feature Transform，SIFT)算法在关键点特征描述向量阶段计算复杂并且维数较高的现象，提出了一种基于压缩感知理论的SIFT算法。通过压缩感知理论的稀疏特征表示方法，对SIFT关键点特征向量进行提取，将高维梯度导数向量降到低维的稀疏特征向量，降低了关键点描述向量维度。采用欧式距离作为关键点的相似性度量， Best-Bin-First(BBF)数据结构避免穷举，使数据的运算量大为减少。实验结果表明，新算法对存在仿射变换的医学图像配准性能优于传统SIFT算法，与当前改进型的SIFT算法相比，本文算法的实时性明显增强。

关键词: 图像处理, 图像配准, 尺度不变特征变换, 特征提取, 稀疏随机投影

Abstract:

SIFT (Scale-Invariant Feature Transform) has defects in computational complexity of its key point descriptor computing stage and in the high dimensionality of the key point feature vectors. To speed up the SIFT computation, a SIFT based on compressive sensing algorithm was proposed. By the sparse feature representation methods of compressive sensing theory, the feature vector of SIFT is extracted and the high-dimensional gradient derivative was decreased to low-dimensional sparse feature vector. Accordingly, Euclidean distance was introduced to compute the similarity and dissimilarity between feature vectors used for image registration and BBF（Best-Bin-First） data structure was used to avoid exhaustion. The experimental results show that the proposed algorithm has better performance than the standard SIFT algorithm while registering the affine transformation medical images. Comparing with the current modified SIFT algorithms, the real-time performance of the proposed algorithm is improved obviously.

Key words: image processing, image registration, scale-invariant feature transform, feature extraction, sparse random projection

中图分类号:

TP391.4

杨飒，郑志硕. 基于稀疏随机投影的SIFT医学图像配准算法[J]. J4, 2015, 32(3): 283-289.

YANG Sa, ZHENG Zhi-shuo. Medical Image Registration Algorithm Based on Sparse Random Projection and SIFT Transform[J]. J4, 2015, 32(3): 283-289.

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