Improved CTA Coronary Segmentation with a Volume-Specific Intensity Threshold
Jawaid, M. M., Rajani, R., Liatsis, P. , Reyes-Aldasoro, C. C. ORCID: 0000-0002-9466-2018 & Slabaugh, G. G. (2017). Improved CTA Coronary Segmentation with a Volume-Specific Intensity Threshold. Paper presented at the Medical Image Understanding and Analysis (MIUA) 2017, 11 Jul 2017, Edinburgh, UK.
Abstract
State-of-the-art CTA imaging equipment has increased increased clinician's ability to make non-invasive diagnoses of coronary heart disease; however, an effective interpretation of the cardiac CTA becomes cumbersome due to large amount of imaged data. Intensity based background suppression is often used to enhance the coronary vasculature but setting a fixed threshold to discriminate coronaries from fatty muscles could be misleading due to non-homogeneous response of contrast medium in CTA volumes. In this work, we propose a volumespecific model of the contrast medium in the coronary segmentation process to improve the segmentation accuracy. The influence of the contrast medium in a CTA volume was modelled by approximating the intensity histogram of the descending aorta with Gaussian approximation. It should be noted that a significant variation in Gaussian mean for 12 CTA volumes validates the need of volume-wise exclusive intensity threshold for accurate coronary segmentation. Moreover, the effectiveness of the adaptive intensity threshold is illustrated with the help of qualitative and quantitative results.
Publication Type: | Conference or Workshop Item (Paper) |
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Additional Information: | The final publication is available at Springer via https://link.springer.com/chapter/10.1007/978-3-319-60964-5_18. |
Publisher Keywords: | computed tomography angiography, contrast medium, curve evolution, coronary segmentation |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine |
Departments: | School of Science & Technology > Computer Science School of Science & Technology > Computer Science > giCentre School of Science & Technology > Engineering |
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