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Automated detection framework of the calcified plaque with acoustic shadowing in IVUS images.

Gao Z, Guo W, Liu X, Huang W, Zhang H, Tan N, Hau WK, Zhang YT, Liu H - PLoS ONE (2014)

Bottom Line: However, it is very time-consuming, and with high inter-observer and intra-observer variability between different doctors.The experimental results are quantitatively analyzed separately by three evaluation methods, the test of the sensitivity and specificity, the linear regression and the Bland-Altman analysis.High sensitivity (94.68%) and specificity (95.82%), good correlation and agreement (>96.82% results fall within the 95% confidence interval in the Student t-test) demonstrate the effectiveness of the proposed method in the detection of the calcified plaque with acoustic shadowing in IVUS images.

View Article: PubMed Central - PubMed

Affiliation: Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Key Laboratory of Biomedical information and Health Engineering, Chinese Academy of Sciences, Shenzhen, China.

ABSTRACT
Intravascular Ultrasound (IVUS) is one ultrasonic imaging technology to acquire vascular cross-sectional images for the visualization of the inner vessel structure. This technique has been widely used for the diagnosis and treatment of coronary artery diseases. The detection of the calcified plaque with acoustic shadowing in IVUS images plays a vital role in the quantitative analysis of atheromatous plaques. The conventional method of the calcium detection is manual drawing by the doctors. However, it is very time-consuming, and with high inter-observer and intra-observer variability between different doctors. Therefore, the computer-aided detection of the calcified plaque is highly desired. In this paper, an automated method is proposed to detect the calcified plaque with acoustic shadowing in IVUS images by the Rayleigh mixture model, the Markov random field, the graph searching method and the prior knowledge about the calcified plaque. The performance of our method was evaluated over 996 in-vivo IVUS images acquired from eight patients, and the detected calcified plaques are compared with manually detected calcified plaques by one cardiology doctor. The experimental results are quantitatively analyzed separately by three evaluation methods, the test of the sensitivity and specificity, the linear regression and the Bland-Altman analysis. The first method is used to evaluate the ability to distinguish between IVUS images with and without the calcified plaque, and the latter two methods can respectively measure the correlation and the agreement between our results and manual drawing results for locating the calcified plaque in the IVUS image. High sensitivity (94.68%) and specificity (95.82%), good correlation and agreement (>96.82% results fall within the 95% confidence interval in the Student t-test) demonstrate the effectiveness of the proposed method in the detection of the calcified plaque with acoustic shadowing in IVUS images.

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The flowchart of the proposed method.
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pone-0109997-g001: The flowchart of the proposed method.

Mentions: In this section, we develop a method to detect the calcified plaque with acoustic shadowing in IVUS images. The flowchart of the proposed method is shown in Figure 1. First, a RMM is applied to cluster pixels in IVUS images in order to distinguish between the hyperechoic and hypoechoic regions, and solved by the EM algorithm. In the solution of the RMM, a new prior probability is produced based on the spatial relationship among neighboring pixels. Second, a MRF is employed to coarsely detect the location of the calcified plaque, and solved by the BP algorithm. In the solution of the MRF, the results of the RMM and a curve called maximum intensity curve (MIC) defined in the IVUS image are combined to compute the relationship between the observed variables and hidden variables, which can be considered as the prior information of the MRF. Third, the pseudo calcified plaques are removed by five predefined constraints. At last, the border of the calcified plaque is detected by the graph searching method algorithm [28] with cost function formatted by MIC and the image gradient.


Automated detection framework of the calcified plaque with acoustic shadowing in IVUS images.

Gao Z, Guo W, Liu X, Huang W, Zhang H, Tan N, Hau WK, Zhang YT, Liu H - PLoS ONE (2014)

The flowchart of the proposed method.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4220935&req=5

pone-0109997-g001: The flowchart of the proposed method.
Mentions: In this section, we develop a method to detect the calcified plaque with acoustic shadowing in IVUS images. The flowchart of the proposed method is shown in Figure 1. First, a RMM is applied to cluster pixels in IVUS images in order to distinguish between the hyperechoic and hypoechoic regions, and solved by the EM algorithm. In the solution of the RMM, a new prior probability is produced based on the spatial relationship among neighboring pixels. Second, a MRF is employed to coarsely detect the location of the calcified plaque, and solved by the BP algorithm. In the solution of the MRF, the results of the RMM and a curve called maximum intensity curve (MIC) defined in the IVUS image are combined to compute the relationship between the observed variables and hidden variables, which can be considered as the prior information of the MRF. Third, the pseudo calcified plaques are removed by five predefined constraints. At last, the border of the calcified plaque is detected by the graph searching method algorithm [28] with cost function formatted by MIC and the image gradient.

Bottom Line: However, it is very time-consuming, and with high inter-observer and intra-observer variability between different doctors.The experimental results are quantitatively analyzed separately by three evaluation methods, the test of the sensitivity and specificity, the linear regression and the Bland-Altman analysis.High sensitivity (94.68%) and specificity (95.82%), good correlation and agreement (>96.82% results fall within the 95% confidence interval in the Student t-test) demonstrate the effectiveness of the proposed method in the detection of the calcified plaque with acoustic shadowing in IVUS images.

View Article: PubMed Central - PubMed

Affiliation: Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Key Laboratory of Biomedical information and Health Engineering, Chinese Academy of Sciences, Shenzhen, China.

ABSTRACT
Intravascular Ultrasound (IVUS) is one ultrasonic imaging technology to acquire vascular cross-sectional images for the visualization of the inner vessel structure. This technique has been widely used for the diagnosis and treatment of coronary artery diseases. The detection of the calcified plaque with acoustic shadowing in IVUS images plays a vital role in the quantitative analysis of atheromatous plaques. The conventional method of the calcium detection is manual drawing by the doctors. However, it is very time-consuming, and with high inter-observer and intra-observer variability between different doctors. Therefore, the computer-aided detection of the calcified plaque is highly desired. In this paper, an automated method is proposed to detect the calcified plaque with acoustic shadowing in IVUS images by the Rayleigh mixture model, the Markov random field, the graph searching method and the prior knowledge about the calcified plaque. The performance of our method was evaluated over 996 in-vivo IVUS images acquired from eight patients, and the detected calcified plaques are compared with manually detected calcified plaques by one cardiology doctor. The experimental results are quantitatively analyzed separately by three evaluation methods, the test of the sensitivity and specificity, the linear regression and the Bland-Altman analysis. The first method is used to evaluate the ability to distinguish between IVUS images with and without the calcified plaque, and the latter two methods can respectively measure the correlation and the agreement between our results and manual drawing results for locating the calcified plaque in the IVUS image. High sensitivity (94.68%) and specificity (95.82%), good correlation and agreement (>96.82% results fall within the 95% confidence interval in the Student t-test) demonstrate the effectiveness of the proposed method in the detection of the calcified plaque with acoustic shadowing in IVUS images.

Show MeSH
Related in: MedlinePlus