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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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(a) The red curve is MIC. contains the acoustic shadowing and part of the calcified plaque. The column coordinates of four vertical dashed lines from left to right are , , , , respectively. (b) The subregion composed of  and  extracted from (a). And the region between the the green dashed rectangle and the red curve (MIC) is . (c) The cost function of  and , and their gap is the MIC in (b). The yellow curves are the optimal paths acquired by the graph searching algorithm in  and , respectively. (d) The yellow curve is the detected calcified plaque in polar coordinate. (e) The yellow curve is the detected calcified plaque in Cartesian coordinate.
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pone-0109997-g003: (a) The red curve is MIC. contains the acoustic shadowing and part of the calcified plaque. The column coordinates of four vertical dashed lines from left to right are , , , , respectively. (b) The subregion composed of and extracted from (a). And the region between the the green dashed rectangle and the red curve (MIC) is . (c) The cost function of and , and their gap is the MIC in (b). The yellow curves are the optimal paths acquired by the graph searching algorithm in and , respectively. (d) The yellow curve is the detected calcified plaque in polar coordinate. (e) The yellow curve is the detected calcified plaque in Cartesian coordinate.

Mentions: After the refinement, we detect the border of the calcified plaque, which can be divided into four part: the left border, the right border, the upper border and the lower border. The left border and the right border represent the angular location of the calcified plaque, and have been computed in the above. Therefore, only the upper border and the lower border of the calcified plaque should be detected in this part. In order to trace the two borders, we employ the graph searching algorithm [28], which is a searching algorithm to find the optimal path in the weighted graph (cost function) connecting two endpoints. Because MIC crosses through the calcified plaque, we consider the upper border is inside a region, denoted by , above MIC and between the th column and the th column, and consider the lower border is inside a region, denoted by , below MIC and between the th column and the th column. and are shown in Figure 3(a). Then the graph searching algorithm is separately applied to and to extract the upper border and the lower border of the calcified plaque. There are two important issues in the graph searching algorithm: the selection of the two endpoints and the formulation of the cost function.


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)

(a) The red curve is MIC. contains the acoustic shadowing and part of the calcified plaque. The column coordinates of four vertical dashed lines from left to right are , , , , respectively. (b) The subregion composed of  and  extracted from (a). And the region between the the green dashed rectangle and the red curve (MIC) is . (c) The cost function of  and , and their gap is the MIC in (b). The yellow curves are the optimal paths acquired by the graph searching algorithm in  and , respectively. (d) The yellow curve is the detected calcified plaque in polar coordinate. (e) The yellow curve is the detected calcified plaque in Cartesian coordinate.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0109997-g003: (a) The red curve is MIC. contains the acoustic shadowing and part of the calcified plaque. The column coordinates of four vertical dashed lines from left to right are , , , , respectively. (b) The subregion composed of and extracted from (a). And the region between the the green dashed rectangle and the red curve (MIC) is . (c) The cost function of and , and their gap is the MIC in (b). The yellow curves are the optimal paths acquired by the graph searching algorithm in and , respectively. (d) The yellow curve is the detected calcified plaque in polar coordinate. (e) The yellow curve is the detected calcified plaque in Cartesian coordinate.
Mentions: After the refinement, we detect the border of the calcified plaque, which can be divided into four part: the left border, the right border, the upper border and the lower border. The left border and the right border represent the angular location of the calcified plaque, and have been computed in the above. Therefore, only the upper border and the lower border of the calcified plaque should be detected in this part. In order to trace the two borders, we employ the graph searching algorithm [28], which is a searching algorithm to find the optimal path in the weighted graph (cost function) connecting two endpoints. Because MIC crosses through the calcified plaque, we consider the upper border is inside a region, denoted by , above MIC and between the th column and the th column, and consider the lower border is inside a region, denoted by , below MIC and between the th column and the th column. and are shown in Figure 3(a). Then the graph searching algorithm is separately applied to and to extract the upper border and the lower border of the calcified plaque. There are two important issues in the graph searching algorithm: the selection of the two endpoints and the formulation of the cost function.

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