Acharya, U. Rajendra and Meiburger, Kristen Mariko and Wei Koh, Joel En and Vicnesh, Jahmunah and Ciaccio, Edward J. and Shu Lih, Oh and Tan, Sock Keow and Raja Aman, Raja Rizal Azman and Molinari, Filippo and Ng, Kwan Hoong (2019) Automated plaque classification using computed tomography angiography and Gabor transformations. Artificial Intelligence in Medicine, 100. p. 101724. ISSN 0933-3657, DOI https://doi.org/10.1016/j.artmed.2019.101724.
Full text not available from this repository.Abstract
Cardiovascular diseases are the primary cause of death globally. These are often associated with atherosclerosis. This inflammation process triggers important variations in the coronary arteries (CA) and can lead to coronary artery disease (CAD). The presence of CA calcification (CAC) has recently been shown to be a strong predictor of CAD. In this clinical setting, computed tomography angiography (CTA) has begun to play a crucial role as a non-intrusive imaging method to characterize and study CA plaques. Herein, we describe an automated algorithm to classify plaque as either normal, calcified, or non-calcified using 2646 CTA images acquired from 73 patients. The automated technique is based on various features that are extracted from the Gabor transform of the acquired CTA images. Specifically, seven features are extracted from the Gabor coefficients: energy, and Kapur, Max, Rényi, Shannon, Vajda, and Yager entropies. The features were then ordered based on the F-value and input to numerous classification methods to achieve the best classification accuracy with the least number of features. Moreover, two well-known feature reduction techniques were employed, and the features acquired were also ranked according to F-value and input to several classifiers. The best classification results were obtained using all computed features without the employment of feature reduction, using a probabilistic neural network. An accuracy, positive predictive value, sensitivity, and specificity of 89.09%, 91.70%, 91.83% and 83.70% was obtained, respectively. Based on these results, it is evident that the technique can be helpful in the automated classification of plaques present in CTA images, and may become an important tool to reduce procedural costs and patient radiation dose. This could also aid clinicians in plaque diagnostics. © 2019 Elsevier B.V.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Automated classification; Computed tomography angiography; Gabor transformation; Image processing; Machine learning; Deep learning |
| Subjects: | R Medicine T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Medicine |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 06 Feb 2020 02:45 |
| Last Modified: | 06 Feb 2020 02:45 |
| URI: | http://eprints.um.edu.my/id/eprint/23691 |
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