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.
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.
A low profile, rectangular E-shaped microstrip patch antenna is designed and proposed for radio-frequency identification (RFID) based intelligent transportation system (ITS) in this paper. The proposed antenna design aims to achieve high gain and low return loss at 0.96 GHz as it is suitable for ultra-high frequency (UHF) RFID tags. The proposed antenna composed of a radiating patch on one side of the dielectric substrate and the ground plane on the other side, copper is used to produce the main radiator. The simulation of the proposed antenna is performed employing the high-frequency structure simulator (HFSS). The dielectric substrate used for the suggested antenna is an FR4 substrate with dielectric constant of 4.3 and height 1.5 mm. The performance of the proposed antenna is measured in terms of gain, return loss, voltage standing wave ratio (VSWR), radiation pattern and the bandwidth. The antenna gain and the return loss of the suggested antenna at 0.96 GHz are 7.3 dB and -12.43 dB, respectively.
Keywords-E-shaped microstrip patch antenna; antenna gain; return loss; voltage standing wave ratio (VSWR); high-frequency structure simulator (HFSS)I.
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