Automated Quantification of Non-Calcified Coronary Plaques in Cardiac CT Angiographic Imagery
| Autor: | Sanam Narejo, Nasrullah Pirzada, Muhammad Moazzam Jawaid, Greg Slabaugh, Constantino Carlos Reyes-Aldasoro, Junaid Baloch |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
General Computer Science medicine.diagnostic_test Computer science business.industry Lumen (anatomy) 030204 cardiovascular system & hematology computer.software_genre Coronary heart disease Intensity (physics) 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Voxel medicine Nuclear medicine business computer Computed tomography angiography |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 9 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2018.090731 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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