A manifold learning method to detect respiratory signal from liver ultrasound images. (March 2015)

• Record Type: Journal Article
• Authors: Wu, Jiaze; Gogna, Apoorva; Tan, Bien Soo; Ooi, London Lucien; Tian, Qi; Liu, Feng; Liu, Jimin
• Is Part Of: Computerized medical imaging and graphics. Volume 40(2015)
• Page Start: 194
• Page End: 204
• Publication Date: 2015-03
• Subjects: Liver ultrasound images -- Respiratory gating -- Respiratory signal -- Manifold learning -- Local tangent space alignment; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2014.11.013
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: We proposed a manifold learning based method to detect the respiratory signal from 2D ultrasound images. We apply the proposed method to create breathing-corrected 3D ultrasound images. The experiments demonstrate robustness and accuracy of the proposed, and potential application in 3D ultrasound imaging. Abstract: Respiratory gating has been widely applied for respiratory correction or compensation in image acquisition and image-guided interventions. A novel image-based method is proposed to extract respiratory signal directly from 2D ultrasound liver images. The proposed method utilizes a typical manifold learning method, based on local tangent space alignment based technique, to detect principal respiratory motion from a sequence of ultrasound images. This technique assumes all the images lying on a low-dimensional manifold embedding into the high-dimensional image space, constructs an approximate tangent space of each point to represent its local geometry on the manifold, and then aligns the local tangent spaces to form the global coordinate system, where the respiratory signal is extracted. The experimental results show that the proposed method can detect relatively accurate respiratory signal with high correlation coefficient (0.9775) with respect to the ground-truth signal by tracking external markers, and achieve satisfactory computing performance (2.3 s for an image sequence of 256 frames). The proposed method is also used to create breathing-corrected 3D ultrasound images to demonstrate its potential application values.