Automatic Bubble Detection in Cardiac Video Imaging

bubble recognition is a challenging problem in a broad range from mechanics to medicine. these gas-filled structures whose pattern and morphology alter in their surrounding environment would be counted either manually or with computational recognition procedures. in cardiology, user dependent bubble detection and temporal counting in videos require special trainings and experience due to ultra fast movement, inherent noise and video quality. in this study, we propose an efficient recognition routine to increase the objectivity of emboli detection. firstly, we started to compare five different methods on two synthetic data sets emulating cardiac chamber environment with increasing speckle noise levels. secondly, real echocardiographic video records were segmented by variational active contours and left atria (la) were extracted. finally, three successful methods in simulation were applied to las in order to reveal candidate bubbles on video frames. our detection rate of proposed method was 95.7% and the others were 86.2% and 88.3%. we conclude that our approach would be useful in long lasting video processing and would be applied in other disciplines.