A hybrid energy model for region based curve evolution – Application to CTA coronary segmentation

Jawaid, Muhammad Moazzam; Rajani, Ronak; Liatsis, Panos; Reyes-Aldasoro, Constantino Carlos; Slabaugh, Greg

doi:10.1016/j.cmpb.2017.03.020

Cited by 10 publications

(11 citation statements)

References 31 publications

(40 reference statements)

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“…The first step in the plaque detection process is the segmentation of the coronary tree in CTA using a hybrid energy formulation as proposed in Jawaid et al [28]. After tree extraction, we performed the skeletonization using fast marching implementation of the thinning algorithm of Van et al [29].…”

Section: Methodsmentioning

confidence: 99%

Detection of Soft Atheroscelarotic Plaques in Cardiac Computed Tomography Angiography

Jawaid¹,

Memon²,

Qureshi³

et al. 2018

ijacsa

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Computed tomography angiography (CTA) has turned non-invasive diagnosis of cardiovascular anomalies into a reality as state-of-the-art imaging equipment is capable of recording sub-millimeter details. Based on high intensity, the calcified plaques are easily identified in cardiac CTA; however, low density based non-calcified plaque detection has been a challenging problem in recent years. We propose an efficient method in this work for automated detection of the non-calcified plaques using discrete radial profiles. The plaque detection is accomplished using support vector machine to differentiate abnormal coronary segments. We investigated a total of 32 CTA volumes and the detection mean accuracy of 84.6% was achieved, which is in-line with the reported literature.

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Section: Methodsmentioning

confidence: 99%

Detection of Soft Atheroscelarotic Plaques in Cardiac Computed Tomography Angiography

Jawaid¹,

Memon²,

Qureshi³

et al. 2018

ijacsa

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“…PROPOSED MODEL Precise segmentation of the coronary vasculature serves as first step in plaque quantification. Accordingly, we employed hybrid energy model of [26] to extract the coronary tree as (a) DS04 seg1 Fig. 1.…”

Section: Related Workmentioning

confidence: 99%

Automated Quantification of Non-Calcified Coronary Plaques in Cardiac CT Angiographic Imagery

Jawaid¹,

Narejo²,

Pirzada³

et al. 2018

ijacsa

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The high mortality rate associated with coronary heart disease (CHD) has driven intensive research in cardiac image analysis. The advent of computed tomography angiography (CTA) has turned non-invasive diagnosis of cardiovascular anomalies into reality as calcified coronary plaques can be easily identified due to high intensity values. However, detection and quantification of the non-calcified plaques in CTA is still a challenging problem because of their lower intensity values, which are often similar to the nearby blood and muscle tissues. In this work, we propose Bayesian posterior based model for precise quantification of the non-calcified plaques in CTA imagery. The only indicator of non-calcified plaques in CTA is relatively lower intensity. Hence, we exploited intensity variations to discriminate voxels into lumen and plaque classes. Based on the normal coronary segments, we computed the vessel-wall thickness in first step. In the subsequent step, we removed vessel wall from the segmented tree and employed Gaussian Mixture Model to compute optimal distribution parameters. In the final step, distribution parameters were employed in Bayesian posterior model to classify voxels into lumen or plaque. A total of 18 CTA volumes were analyzed in this work using two different approaches. According to the experimental results, mean Jaccard overlap is around 88% with respect to the manual expert. In terms of sensitivity, specificity and accuracy, the proposed method achieves 84.13% ,79.15% and 82.02% success, respectively. Conclusion: According to the experimental results, it is shown that the proposed plaque quantification method achieves accuracy equivalent to human experts.

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“…2) Methodology: Starting with hybrid energy-based coronary tree segmentation [37], vertical radial profiles are computed around centre line for exploiting intensity variations. In the subsequent step, authors validated the use of computed radial profiles by associating non-calcified plaque intensities as illustrated in Fig.…”

Section: J Framework For Detection and Localization Of Coronary Non-mentioning

confidence: 99%

A Review of the State-of-the-Art Methods for Non-calcified Plaque Detection in Cardiac CT Angiography

Jawaid¹,

Narejo²,

Qureshi³

et al. 2018

IJCTE

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The non-invasive diagnosis for cardiac abnormalities has been turned into a reality in recent years. This is based on the fact that advanced imaging equipment can acquire sub-millimeter details of the internal organs. An important example is the use of state-of-the-art computed tomography (CT) as a substitute of conventional catheterization. It is interesting that calcium-based vascular deposits can be quickly identified in CT; however, non-calcified plaque detection remains a challenging task due to lower intensity values. In this context, a number of methods have been reported for efficient detection and segmentation of non-calcified plaques in recent years. In order to advance the existing knowledge and extend the operational efficiency in this domain, it is extremely important to review the state-of-the-art literature. Accordingly, we present a comprehensive review of non-calcified plaque detection method in this paper presents. We believe that this can serve as a starting point towards productive clinical research in this domain.Index Terms-Coronary segmentation, non-calcified plaques, plaque detection.

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A hybrid energy model for region based curve evolution – Application to CTA coronary segmentation

Cited by 10 publications

References 31 publications

Detection of Soft Atheroscelarotic Plaques in Cardiac Computed Tomography Angiography

Detection of Soft Atheroscelarotic Plaques in Cardiac Computed Tomography Angiography

Automated Quantification of Non-Calcified Coronary Plaques in Cardiac CT Angiographic Imagery

A Review of the State-of-the-Art Methods for Non-calcified Plaque Detection in Cardiac CT Angiography

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