Contour Tracking Method Research Articles

1037-141 Simplified three-dimensional measurement of left ventricular volume and ejection fraction using automated contour tracking method in patients

1037-141 Simplified three-dimensional measurement of left ventricular volume and ejection fraction using automated contour tracking method in patients

Rapid and accurate noninvasive assessment of global left ventricular systolic function using biplane advanced automated contour tracking method

Background The advanced automated contour tracking (AACT) method has been newly developed for automated detection of the left ventricular endocardial boundary. Left ventricular ejection fraction (LVEF) may be estimated by applying the AACT method to 2 orthogonal planes of patients even when regional wall-motion abnormalities exist. The purpose of this study was to examine the reliability of the biplane AACT method in the measurement of LVEF in patients with suggested ischemic heart disease with use of quantitative gated single photon emission computed tomography (QGS) as a reference standard. Methods The study population consisted of 47 consecutive patients with suggested ischemic heart disease. All patients underwent 2-dimensional echocardiography and QGS. Biplane LVEF from apical 4- and 2-chamber views was measured offline by the AACT method using disk summation method. The accuracy of the AACT method for LVEF measurement was determined in comparison with QGS. Results In 41 (29 with and 12 without regional wall-motion abnormalities) of 47 patients (87%), automated tracing of the endocardial border was adequately achieved with the AACT method. LVEF measured by the AACT method correlated well with that measured by QGS (y = 0.97x +2.4, r = 0.91). The mean difference between AACT and QGS was 0.6 ± 5.5% (mean ± SD). The mean time required for analysis of 1 set of images during 1 cardiac cycle by the AACT method was much shorter than that required by manual tracing method (7 ± 1 vs 37 ± 4 seconds, P < .0001). Conclusion The biplane AACT method provides accurate and quick measurement of LVEF in patients even with regional wall-motion abnormalities.

Recognition of English Calling Cards by Using Projection Method and Enhanced RBE Network

In this paper, we proposed the novel method for the recognition of English calling cards by using the projection method and the enhanced RBF (Radial Basis Function) network. The recognition of calling cards consists of the extraction phase of character areas and the recognition phase of extracted characters. In the extraction phase, first of all, noises are removed from the images of calling cards, and the feature areas including character strings are separated from the calling card images by using the horizontal smearing method and the 8-directional contour tracking method. And using the image projection method, the feature areas are split into the areas of individual characters. We also proposed the enhanced RBF network that organizes the middle layer effectively by using the enhanced ART1 neural network adjusting the vigilance threshold dynamically according to the homogeneity between patterns. In the recognition phase, the proposed neural network is applied to recognize individual characters. Our experiment result showed that the proposed recognition algorithm has higher success rate of recognition and faster learning time than the existing neural network based recognition.

Accurate and quick assessment of left ventricular function in patients with ischemic heart disease using biplane advanced automated contour tracking method

Accurate and quick assessment of left ventricular function in patients with ischemic heart disease using biplane advanced automated contour tracking method

Tracking nonparameterized object contours in video

We propose a new method for contour tracking in video. The inverted distance transform of the edge map is used as an edge indicator function for contour detection. Using the concept of topographical distance, the watershed segmentation can be formulated as a minimization. This new viewpoint gives a way to combine the results of the watershed algorithm on different surfaces. In particular, our algorithm determines the contour as a combination of the current edge map and the contour, predicted from the tracking result in the previous frame. We also show that the problem of background clutter can be relaxed by taking the object motion into account. The compensation with object motion allows to detect and remove spurious edges in background. The experimental results confirm the expected advantages of the proposed method over the existing approaches.

Echocardiographic estimation of left ventricular cavity area with a newly developed automated contour tracking method

Development of an automated contour tracking method provides detection and tracking of the endocardial boundary using the energy minimization method without tracing a region of interest. The purpose of this study was to compare the automated contour tracking method and manually drawn methods for the measurement of left ventricular cavity areas and fractional area change. Apical four-chamber view was visualized and recorded for off-line analysis in 11 patients by means of two-dimensional echocardiography. The automated contour tracking method automatically traces the endocardial border from the recorded images and calculates left ventricular cavity areas (end-diastole and end-systole) and fractional area change. In the same images selected as end-diastole and end-systole in the automated contour tracking method, left ventricular endocardial border was manually traced to calculate left ventricular cavity areas and fractional area change. Both methods were compared by linear regression analysis for the measurement of cavity areas and fractional area change. Left ventricular areas measured by the automated contour tracking method showed an excellent correlation with those by the manual method (end-diastole: r = 0.99, y = 0.83x + 2.6, standard error of the estimate = 1.5 cm 2; end-systole: r = 0.99, y = 0.96x –0.8, standard error of the estimate = 1.2 cm 2). The mean differences between the automated contour tracking and manual methods were –3.1 ± 5.1 cm 2 and –1.6 ± 2.4 cm 2 at end-diastole and end-systole, respectively. Fractional area change determined by the automated contour tracking method correlated well with that by the manual method ( r = 0.95, y = 1.17x –6.5, standard error of the estimate = 3.4%). The mean difference between the automated contour tracking and manual methods was –0.8% ± 7.1%. In conclusion, a newly developed automated contour tracking method correlates highly with the manual method for the estimation of left ventricular cavity areas and fractional area change in high-quality images. This suggests that this new technique may be useful in the automated quantitation of left ventricular function in patients with high-quality images with no dropout and no intercavity artifact or structure. (J Am Soc Echocardiogr 1997;10:822-9.)

A contour tracking method using an elastic contour model and an energy‐minimization approach

AbstractThis paper proposes a method for tracking an object contour in a sequence of images. In this method, a contour model is defined by elastics. The contour tracking problem is formulated as an optimization problem to find the position that minimizes both the elastic energy of its model and the potential energy derived from the edge potential image that includes a target object contour. The contour model is updated sequentially according to the tracking result in the previous frame.In the proposed approach, the contour model not only moves toward the object contour in the current frame, but also preserves the shape of the tracked contour in the previous frame. Therefore, the proposed method makes it possible to establish object tracking even when complex texture and occluding edges exist in or near the target object. Moreover, in this method, since smooth potential fields extending over a long distance are generated by distance transformation, unlike conventional methods, long‐range motion tracking can be performed.An algorithm is newly presented which efficiently solves energy minimization problems. The validity and usefulness of the proposed method is shown with some experimental results.