title: Application of automatic boundary detection for computerized quantitative analysis of left ventricular regional wall motion by two-dimensional echocardiography.
abstract: This study was designed to set up a computer-aided image processing algorithm for boundary detection from two-dimensional echocardiography and to establish a computerized model for quantitative analysis of left ventricular wall motion with the application of automatic boundary detection. The four-chamber view echocardiographic images of seven normal subjects and five patients with acute myocardial infarction were investigated. The main steps of image processing in this algorithm included automatic threshold estimation, contrast stretching, radial search of endocardial boundary, and smoothing of the boundary. The displacements of the left ventricular endocardial contour from end-diastolic to end-systolic frame were measured using a sample point connection model. For analysis of the regional contraction, the left ventricular endocardial contour was divided equally into six segments. The wall motion curves in patients were compared with the normal wall motion pattern established from the normal subjects to identify the segments with normal or abnormal wall motion. The results of this quantitative method were compared with those of qualitative analysis. In the 30 segments of the five patients, quantitative analysis correctly identified nine of the 11 segments with abnormal wall motion diagnosed by qualitative analysis (sensitivity, 82%) and identified 17 of the 19 segments with normal wall motion (specificity, 89%). The positive and negative predictive values of quantitative analysis were 82% (9 of 11) and 89% (17 of 19), respectively, and the diagnostic accuracy was 87% (26 of 30). Our preliminary results suggest that computer-aided boundary detection can be applied to establish an objective and useful model for quantitative analysis of left ventricular regional wall motion.
authors.affiliation: Department of Internal Medicine, National Cheng Kung University, Tainan, Taiwan, Republic of China.
authors.forename: L M
authors.initials: LM
authors.lastname: Tsai
authors.forename: T P
authors.initials: TP
authors.lastname: Chen
authors.forename: T S
authors.initials: TS
authors.lastname: Chen
authors.forename: J H
authors.initials: JH
authors.lastname: Chen
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D004452:Echocardiography / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000000981:diagnostic imaging / Q000503:physiopathology; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D018487:Ventricular Dysfunction, Left / Q000000981:diagnostic imaging* / Q000503:physiopathology