Automated plaque classification using computed tomography angiography and Gabor transformations

Publication date: Available online 14 September 2019Source: Artificial Intelligence in MedicineAuthor(s): U. Rajendra Acharya, Kristen M. Meiburger, Joel En Wei Koh, Jahmunah Vicnesh, Edward J. Ciaccio, Oh Shu Lih, Sock Keow Tan, Raja Rizal Azman Raja Aman, Filippo Molinari, Kwan Hoong NgAbstractCardiovascular 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 acqui...
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research