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The automatic identification of precise left ventricular endocardial surfacing using echocardiography and cardiac MRI for the quantification of ejection fraction continues to be difficult. Standard image processing techniques have not been completely successful, principally because not all edge data directly corresponds to anatomical boundaries. Trained physicians must use considerable a priori information regarding normal human anatomy to `fill in' the missing details of a typical cardiac study. In this paper, we describe a new method to identify borders within medical images that incorporates an expert system based approach. Throughout the design of this approach, we maintained the following constraints: the system must easily capture expert information from multiple experts, over a variety of cardiac image formats, be trainable on any patient, and once trained provide fast execution times. The method was initially tested on echocardiographic images. Using a series of 2D echo image sequences, an expert traced endo- and epicardial edges in order to 'teach' the computer which pixels were myocardium and which were left ventricular cavity; each identified pixel was then convolved so as to amplify correlations found between the pixel and its neighbors. The result, applied prospectively at near real time speed, identified all pixels as being myocardium, left ventricular cavity or uncertain. As a consequence, endocardial borders were generated. These borders were then used to calculate systolic and diastolic areas and an area ejection fraction which proved to be within 2% of an expert traced and calculated area ejection fraction. These findings suggest this method holds promise for the capturing of expert knowledge of 2D cardiac ultrasound interpretation, and, through preliminary testing, we have shown its potential in the segmentation of 3D cardiac MRI volume sets for subsequent analysis and display.
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