A hybrid active contour segmentation method for myocardial D-SPECT images

Huang, Chenxi; Shan, Xiaoying; Lan, Yisha; Liu, Lu; Cai, Haidong; Che, Wenliang; Hao, Yongtao; Cheng, Yongqiang; Peng, Yonghong

Abstract

Ischaemic heart disease has become one of the leading causes of mortality worldwide. Dynamic single-photon emission computed tomography (D-SPECT) is an advanced routine diagnostic tool commonly used to validate myocardial function in patients suffering from various heart diseases. Accurate automatic localization and segmentation of myocardial regions is helpful in creating a three-dimensional myocardial model and assisting clinicians to perform assessments of myocardial function. Thus, image segmentation is a key technology in preclinical cardiac studies. Intensity inhomogeneity is one of the common challenges in image segmentation and is caused by image artefacts and instrument inaccuracy. In this paper, a novel region-based active contour model that can segment the myocardial D-SPECT image accurately is presented. First, a local region-based fitting image is defined based on information related to the intensity. Second, a likelihood fitting image energy function is built in a local region around each point in a given vector-valued image. Next, the level set method is used to present a global energy function with respect to the neighbourhood centre. The proposed approach guarantees precision and computational efficiency by combining the region-scalable fitting energy (RSF) model and local image fitting energy (LIF) model, and it can solve the issue of high sensitivity to initialization for myocardial D-SPECT segmentation.

Citation

Huang, C., Shan, X., Lan, Y., Liu, L., Cai, H., Che, W., …Peng, Y. (2018). A hybrid active contour segmentation method for myocardial D-SPECT images. IEEE Access, 6, 39334-39343. https://doi.org/10.1109/ACCESS.2018.2855060

• Journal Article Type: Article
• Acceptance Date: Jul 8, 2018
• Online Publication Date: Jul 11, 2018
• Publication Date: Jul 10, 2018
• Deposit Date: Jul 20, 2018
• Publicly Available Date: Jul 20, 2018
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Pages: 39334-39343
• DOI: https://doi.org/10.1109/ACCESS.2018.2855060
• Keywords: General Engineering; General Materials Science; General Computer Science
• Public URL: https://hull-repository.worktribe.com/output/937873
• Publisher URL: https://ieeexplore.ieee.org/document/8409947/