An Improved Approach for Accurate and Efficient Measurement of Common Carotid Artery Intima-Media Thickness in Ultrasound Images

Li, Qiang; Zhang, Wei; Guan, Xin Yuan; Bai, Yu; Jia, Jing

doi:10.1155/2014/740328

Cited by 12 publications

(9 citation statements)

References 27 publications

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“…Once, the coordinates of the arterial wall boundary are recorded, its distance is measured using mean absolute difference metrics [20]. It is one of the most used methods for IMT and LD measurement if the same numbers of coordinate in both the contours are available.…”

Section: Imt and Ld Measurementmentioning

confidence: 99%

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Arterial Parameters and Elasticity Estimation in Common Carotid Artery Using Deep Learning Approach

Patel¹,

Jain²

2019

IJIGSP

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The risk of cardiovascular diseases is growing worldwide, and its early detection is necessary to reduce the level of risk. Structural parameters of the carotid artery as intima-media thickness and functional parameters such as arterial elasticity are directly associated with cardiovascular diseases. Segmentation of the carotid artery is required to measure the structural parameters and its temporal value that is used to estimate the arterial elasticity. This paper has two primary objectives: (i) Segmentation of the sequence of carotid artery ultrasound to measure temporal value of intimamedia thickness and lumen-diameter, and (ii) Young's modulus of elasticity estimation. The proposed segmentation method uses the contextual feature of the image pattern and is based on multi-layer extreme learning machine auto-encoder network. This segmentation method has two parts: (a) region of interest localization and (b) lumen-intima interface and mediaadventitia interface detection at the far wall. ROI localization algorithm divides the ultrasound frame into columns and also divides each column into overlapping blocks, ensuring that every column has a region of interest block. A multi-layer extreme learning machine with auto-encoder is trained with labelled data and in testing; system classifies the blocks into 'region of interest' and 'non-region of interest'. Pixels belonging to the region of interest are classified in the first part and a similar network-based method is proposed for lumenintima and media-adventitia interface detection at the near wall of the carotid artery. Structural parameter of the artery, intima-media thickness and lumen diameter are measured in a sequence of images of the cardiac cycle. The temporal values of structural parameters are used to estimate the young's modulus of elasticity. .

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Section: Imt and Ld Measurementmentioning

confidence: 99%

“…However, IMT estimation underperforms with some existing method [18,21,22]. We have measured LD of the CCA with MAD metrics [20]. The coordinates of the LI interface in far and nearwall has already extracted in 'LI and MA detection in far wall' sub-section and 'LII detection in near-wall' subsection respectively.…”

Section: Imt and Ld Measurementmentioning

confidence: 99%

Arterial Parameters and Elasticity Estimation in Common Carotid Artery Using Deep Learning Approach

Patel¹,

Jain²

2019

IJIGSP

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“…A variety of existing algorithms can be incorporated into the device to obtain these measurements [15]. However, we propose a novel lumen-diameterbased anomaly detection algorithm (Fig.…”

Section: System Operationmentioning

confidence: 99%

A wearable carotid ultrasound assembly for early detection of cardiovascular diseases

Shomaji

Basak

Mandai

et al. 2016

2016 IEEE Healthcare Innovation Point-of-Care Technologies Conference (HI-POCT)

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Abstract-Cardiovascular disease is currently responsible for a major portion of all global deaths. Fortunately, early detection of its symptoms can greatly contribute to effective prevention. Hence, clinicians recommend routine cardiovascular check-ups. Even though various methods for cardiac diagnosis exist, most of them are clinic-based, and therefore time-consuming and costly. Therefore, it is not always possible for patients to go through these procedures on a regular basis. In this paper, we propose a novel wearable ultrasonic imaging assembly for routine, easy-to-use, and economical monitoring of the carotid arteries. The device monitors intima-media thickness (IMT), which is a proven clinically useful marker for diagnosis of cardiovascular disease and prediction of imminent cardiovascular events. It uses standard B-mode ultrasound, which is suitable for implementation within area-and power-constrained wearable form factors. We present design parameters and power requirements for all the essential hardware components of the proposed wearable imaging system. We also present an efficient algorithm for predicting IMT anomalies from ultrasound images.

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“…The earliest method for IMT measurement was reported in 1986 [6]. Since then, many approaches and techniques have been reported in numerous studies [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Additionally, almost all of these methods consist of two steps: (i) finding the region of interest (ROI) and estimating the initial boundaries (LII, MAI) and (ii) tracing the real boundaries.…”

Section: Introductionmentioning

confidence: 99%

“…Molinari et al [17] developed a fully automated method, called completely automated multi-resolution edge snapper (CAMES), that can find the CCA's value based on a combination of scalespace and statistical classifications in a multi-resolutional framework. Other methods, such as the watershed transform in the work of Menchon-Lara et al [20] and the method of template-based matching in the research of Li et al [21], were applied to extract the ROI. For the second step, many dissimilar approaches, such as those based on dynamic programming [7,14], active contours [9, 11-13, 18, 19], neural networks [20], and random field models [22], have been developed.…”

Section: Introductionmentioning

confidence: 99%

Automated Measurement Method of Common Carotid Artery Intima-Media Thickness in Ultrasound Image Based on Markov Random Field Models

Xiao

Bai

et al. 2015

J. Med. Biol. Eng.

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Common carotid artery intima-media thickness (IMT) is regarded as one of the most significant indicators for the assessment of cardiovascular diseases. This paper proposes an automated method based on Markov random field models for IMT measurement. The label field is initialized from line segments generated using the Hough transform and the iterated conditional modes method is applied to detect the final boundaries of the lumen-intima interface as well as the media-adventitia interface. A total of 80 ultrasound images from 80 corresponding patients were tested with the proposed method. The ground truth (GT) of the IMT was manually measured a total of four times and then averaged, and the automatically segmented (AS) IMT was computed using the proposed method. The mean of the absolute error ± standard deviation between AS and GT IMT values was 0.0244 ± 0.0227 mm, and the correlation coefficient between the two was 0.9810. Furthermore, the computational time was 1.7 s per image for serial computation with MATLAB on a computer with an Intel Core i5-4200M CPU running 32-bit Windows 7. Experimental results show that the proposed method is both efficient and accurate. The proposed method can be applied for real-time IMT measurement by utilizing parallel computation, which is enable by the use of Markov random field models.

show abstract

An Improved Approach for Accurate and Efficient Measurement of Common Carotid Artery Intima-Media Thickness in Ultrasound Images

Cited by 12 publications

References 27 publications

Arterial Parameters and Elasticity Estimation in Common Carotid Artery Using Deep Learning Approach

Arterial Parameters and Elasticity Estimation in Common Carotid Artery Using Deep Learning Approach

A wearable carotid ultrasound assembly for early detection of cardiovascular diseases

Automated Measurement Method of Common Carotid Artery Intima-Media Thickness in Ultrasound Image Based on Markov Random Field Models

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