Cardiac view classification using speed up robust features

Abstract

Objectives: Automating cardiac view classification is the first step for automating computer aided cardiac disease diagnosis. In this paper automatic cardiac view classification system is proposed. Methods: This system attempts to classify four standard cardiac views in echocardiogram namely Parasternal Long Axis (PLAX), Parasternal Short Axis (PSAX), Apical Four Chamber (A4C), and Apical Two Chamber (A2C) views automatically using Speed Up Robust Features (SURF). Conclusion: The Speed Up Robust Features is effective in collecting more class-specific information and ro-bust in dealing with partial occlusion and viewpoint changes. To authenticate the generalizability and robustness, the proposed system is tested on a dataset of 200 echocardiogram images which achieve a classification rate of 90.7%.