Abstract
A spatiotemporal atlas refers to a standard image sequence that represents the general motion pattern of the targeted anatomy across a group of subjects. Recent years have witnessed an increasing interest in using spatiotemporal atlas for scientific research and clinical applications in image processing, data analysis and medical imaging. However, the generation of spatiotemporal atlas is often time-consuming and computationally expensive due to the nonlinear image registration procedures involved. This research targets at accelerating the generation of spatiotemporal atlas by formulating the atlas generation procedure as a multi-level modulation (M-ary) classification problem. In particular, we have implemented a fast template matching method based on singular value decomposition, and applied it to generate high quality spatiotemporal atlas with reasonable time and computational complexity. The performance has been systematically evaluated on public accessible data sets. The results and conclusions hold promise for further developing advanced algorithms for accelerating generation of spatiotemporal atlas.
Highlights
The understanding of muscle structure and muscular movements is the foundation for many scientific researches and clinical applications in image processing, medical imaging and human physiology
This paper focuses on applying the principles of statistical learning and pattern recognition to accelerate the generation of spatiotemporal atlas
Unlike existing methods that focus on the image domain in the generation of spatiotemporal atlas, the template matching algorithm introduced in
Summary
The understanding of muscle structure and muscular movements is the foundation for many scientific researches and clinical applications in image processing, medical imaging and human physiology. A recent imaging-based research has indicated that the size and shape of the human heart vary significantly at multiple cardiac phases of a heartbeat, among different heart beats and across various subjects [1]. Given these potential sources of anatomical differences during muscular motion, it has long been a dream for scientists to establish a standard sequence of images representing the “expected” muscle structure and motion pattern for a targeted group of subjects. If such a standard sequence of images, i.e., a spatiotemporal atlas, were to exist, it serves as the ground truth for quantitatively interpreting the observed muscle movement and accurately characterizing the motion variability of a specific subject as versus to the general motion pattern [2]
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