Close menu


Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

A New Framework for the Integrative Analytics of Intravascular Ultrasound and Optical Coherence Tomography Images

Huang, Chenxi, Xie, Yuan, Lan, Yisha, Hao, Yongtao, Chen, Fei, Cheng, Yongqiang and Peng, Yonghong (2018) A New Framework for the Integrative Analytics of Intravascular Ultrasound and Optical Coherence Tomography Images. IEEE Access, 6. pp. 36408-36419. ISSN 2169-3536

Item Type: Article


The integrative analysis of multimodal medical images plays an important role in the diagnosis of coronary artery disease by providing additional comprehensive information that cannot be found in an individual source image. Intravascular Ultrasound (IVUS) and Optical Coherence Tomography (IV-OCT) are two imaging modalities that have been widely used in the medical practice for the assessment of arterial health and the detection of vascular lumen lesions. IV-OCT has a high resolution and poor penetration, while IVUS has a low resolution and high detection depth. This paper proposes a new approach for the fusion of intravascular ultrasound and optical coherence tomography pullbacks to significantly improve the use of those two types of medical images. It also presents a new two-phase multimodal fusion framework using a coarse-to-fine registration and a wavelet fusion method. In the coarse-registration process, we define a set of new feature points to match the IVUS image and IV-OCT image. Then, the improved quality image is obtained based on the integration of the mutual information of two types of images. Finally, the matched registered images are fused with an approach based on the new proposed wavelet algorithm. The experimental results demonstrate the performance of the proposed new approach for significantly enhancing both the precision and computational stability. The proposed approach is shown to be promising for providing additional information to enhance the diagnosis and enable a deeper understanding of atherosclerosis.

08362608.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (11MB) | Preview

More Information

Depositing User: Yonghong Peng


Item ID: 10179
Identification Number:
ISSN: 2169-3536
Official URL:

Users with ORCIDS

Catalogue record

Date Deposited: 20 Nov 2018 14:45
Last Modified: 18 Dec 2019 16:07


Author: Chenxi Huang
Author: Yuan Xie
Author: Yisha Lan
Author: Yongtao Hao
Author: Fei Chen
Author: Yongqiang Cheng
Author: Yonghong Peng

University Divisions

Faculty of Technology
Faculty of Technology > FOT Executive


Computing > Data Science
Computing > Artificial Intelligence

Actions (login required)

View Item View Item