Automatic left ventricular boundary detection in digital two-dimensional echocardiography using fuzzy reasoning techniques

Extraction of left ventricular endocardial and epicardial boundaries from digital two-dimensional echocardiography is essential in quantitative analysis of cardiac function. Automatic detection of these boundaries is difficult due to poor intensity contrast and noise inherent in ultrasonic images. In this paper, we present a new approach that employs fuzzy reasoning techniques to detect the boundaries automatically. In the proposed method, the image is firstly enhanced by applying the Laplasian-of-Gaussian edge detector. Secondly, the center of the left ventricle is determined automatically by analyzing the original image. Next, a search process radiated from the estimated center is performed to locate the endocardial boundary by using the zero-crossing points. After this step, the estimation of the range of radius of possible epicardial boundary is carried out by comparing the high-level knowledge of intensity changes along all directions with the actual image intensity changes. The high-level knowledge of global intensity change in the image is acquired from experts in advance and is represented in the form of fuzzy linguistic descriptions and relations. Knowledge of local intensity change can therefore be deduced from the knowledge of global intensity change through fuzzy reasoning. After the comparison, multiple candidate ranges as well as the grades of membership indicating confidence levels are obtained along each direction. The most consistent range in each direction is selected to guide the epicardial boundary search. Multiple candidate epicardial boundaries are then found by locating the zero-crossing points in the range. The one with best consistency is selected as the epicardial boundary. Both endocardial and epicardial boundaries are then smoothed based upon the radii of their spatial neighbors. The final boundaries are obtained by applying the cardinal spline interpolation algorithm. Since our approach is based on fuzzy reasoning techniques and takes global information into consideration, an accurate and smooth result is obtained.