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

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Abstract

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.

Item Type: Article
Subjects: Computing > Data Science
Computing > Artificial Intelligence
Divisions: Faculty of Technology
Depositing User: Yonghong Peng
Date Deposited: 20 Nov 2018 14:45
Last Modified: 28 Nov 2018 13:40
URI: http://sure.sunderland.ac.uk/id/eprint/10179

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