Paper Info
Title
A Hybrid Active Contour Segmentation Method for Myocardial D-SPECT Images.
Abstract
The 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 the myocardial function in patients suffering from various heart diseases. Accurate automatic localization and segmentation of myocardial regions is helpful in creating a 3-D 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 artifacts 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 the 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 neighborhood center. The proposed approach guarantees precision and computational efficiency by combining the region-scalable fitting energy model and local image fitting energy model, and it can solve the issue of high sensitivity to initialization for myocardial D-SPECT segmentation.
Year
Venue
Field
2018
IEEE Access
Active contour model,Computer vision,Emission computed tomography,Computer science,Segmentation,Level set method,Image segmentation,Artificial intelligence,Initialization,Distributed computing
DocType
Volume
Citations 
Journal
6
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Chenxi Huang133.14
Xiaoying Shan200.34
Yisha Lan352.74
Lu Liu41501170.70
Haidong Cai500.34
Wenliang Che642.38
Yongtao Hao700.34
Yongqiang Cheng845.44
Yonghong Peng940033.39