Akira Shiozaki1, Tatsuya Omori2, Yutaka Hirano3, Hisakazu Uehara3, Tohru Masuyama4. 1. Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuencho, Sakai, 599-8531, Japan. shiozaki@cs.osakafu-u.ac.jp. 2. Government & Public Corporation Information Systems Division, Hitachi, Tokyo, Japan. 3. Department of Cardiology, Kinki University School of Medicine, Osakasayama, Japan. 4. Department of Internal Medicine, Hyogo College of Medicine, Nishinomiya, Japan.
Abstract
PURPOSE: The aim of this study was to develop a method for early, accurate differentiation between old myocardial infarction (OMI) and angina pectoris (AP) using color kinesis (CK) images. We first extracted exact end-diastolic and end-systolic contours from CK images and then extracted the features of cardiac function from two CK images (one at rest, the other after exercise) and investigated their effectiveness in differentiating old myocardial infarction and angina pectoris. We then evaluated the effectiveness of several features in recognizing coronary artery disease and used the effective features to show the differentiation results. METHODS: First, we extracted exact end-diastolic and end-systolic contours from CK images with an active contour model. Second, we defined the features that seemed to be effective in recognizing coronary artery disease. The features are extracted from the region between the end-diastolic endocardial contour and end-systolic endocardial contour in two CK images: one obtained when the subject was at rest and the other after exercise. Nine features were considered effective for differentiating old myocardial infarction and angina pectoris, and the effectiveness in recognizing coronary artery disease, which includes old myocardial infarction and angina pectoris, was evaluated. Third, coronary artery disease is recognized by the effective features. RESULTS: Contours near a manual trace by a skilled physician were obtained using the proposed method. Multiple comparisons of the mean values of the extracted features were drawn among three groups: a healthy-subject group; an old myocardial infarction patient group; and an angina pectoris patient group. The feature effective in differentiating old myocardial infarction was the "area at rest"; those effective in differentiating angina pectoris were a "decrease in area" and a "decrease in movement." These effective features have almost always differentiated old myocardial infarction and angina pectoris. CONCLUSIONS: This study used the endocardial contour extraction technique with the dynamic contour model and evaluated the validity of the features of cardiac function; it then recognized coronary artery disease from the effective features. Multiple comparisons of the mean value of the extracted features among the healthy-subject group, the old myocardial infarction patient group, and the angina pectoris patient group has proved that the "area at rest" is effective in differentiating old myocardial infarction, and the "decrease in area" and "decrease in movement" are effective for differentiating angina pectoris.
PURPOSE: The aim of this study was to develop a method for early, accurate differentiation between old myocardial infarction (OMI) and angina pectoris (AP) using color kinesis (CK) images. We first extracted exact end-diastolic and end-systolic contours from CK images and then extracted the features of cardiac function from two CK images (one at rest, the other after exercise) and investigated their effectiveness in differentiating old myocardial infarction and angina pectoris. We then evaluated the effectiveness of several features in recognizing coronary artery disease and used the effective features to show the differentiation results. METHODS: First, we extracted exact end-diastolic and end-systolic contours from CK images with an active contour model. Second, we defined the features that seemed to be effective in recognizing coronary artery disease. The features are extracted from the region between the end-diastolic endocardial contour and end-systolic endocardial contour in two CK images: one obtained when the subject was at rest and the other after exercise. Nine features were considered effective for differentiating old myocardial infarction and angina pectoris, and the effectiveness in recognizing coronary artery disease, which includes old myocardial infarction and angina pectoris, was evaluated. Third, coronary artery disease is recognized by the effective features. RESULTS: Contours near a manual trace by a skilled physician were obtained using the proposed method. Multiple comparisons of the mean values of the extracted features were drawn among three groups: a healthy-subject group; an old myocardial infarctionpatient group; and an angina pectorispatient group. The feature effective in differentiating old myocardial infarction was the "area at rest"; those effective in differentiating angina pectoris were a "decrease in area" and a "decrease in movement." These effective features have almost always differentiated old myocardial infarction and angina pectoris. CONCLUSIONS: This study used the endocardial contour extraction technique with the dynamic contour model and evaluated the validity of the features of cardiac function; it then recognized coronary artery disease from the effective features. Multiple comparisons of the mean value of the extracted features among the healthy-subject group, the old myocardial infarctionpatient group, and the angina pectorispatient group has proved that the "area at rest" is effective in differentiating old myocardial infarction, and the "decrease in area" and "decrease in movement" are effective for differentiating angina pectoris.
Entities:
Keywords:
active contour model; color kinesis; coronary artery disease; echocardiography; endocardial wall motion