Left-ventricular boundary detection from short-axis echocardiograms: the use of active contour models

Universityof Washington, Seattle, WA 98195ABSTRACTAccurate identification of boundaries of the left ventricle lets the cardiologist determine important physiological parameterslike the left-ventricular ejection fraction, volume of the left ventricle, and regional heart wall thickening; all of which aid inbetter diagnosis of heart diseases. We have developed a new semi-automated method to determine the left-ventricularboundaries from short-axis echocardiograms. Our method is based on the active contour models, also known as snakes,originallyproposed by Kass et al. [1]. Our method was tested on images obtained from 18 patients; manual outlining wasused as reference for comparison. Our results were also compared to Detmer et al. [2], who used the same images to test theiralgorithm. The errors in detecting boundaries by using our algorithm were found to be within the reproducibility of manualoutlining. We also implemented a 3-D extension of the active contour algorithm, where the third dimension is time, and arecurrently working on a clinical validation of this algorithm. The 3-D algorithm partly alleviates the problems encountered inthe 2-D algorithm due to missing boundaries in echocardiograms.1. INTRODUCTIONUltrasound is being increasingly used in the diagnosis of heart diseases. Ultrasound imaging is appealing for suchapplications because it is a noninvasive imaging technique; it is a safe imaging modality since it does not involve any ionizingradiation; it is a relatively inexpensive imaging modality; and, it allows real-time imaging of the body. Cardiac ultrasoundimages can be used to measure global functions like left-ventricular volume, stroke volume, ejection fraction and cardiacoutput; or, they can be used to measure local functions like regional left-ventricular wall thickening [3, 4].One way of measuring these functions quantitatively is by tracing the borders of the left ventricle of the heart. From theseborders, the area and the wall thickness for one particular cross section of the heart can be measured. Using this measuredarea and based on certain models of the shape of the heart, the volume of the left ventricle can be estimated. Volumesmeasured at end-diastole and end-systole can help in computing ejection fraction and stroke volume. This tracing hastraditionally been done manually and is a tedious and time-consuming task.Automated left-ventricular boundary detection from echocardiographic images has been a long-sought goal in cardiology.However, very few of the methods for automatic tracing have gained clinical acceptance. The most probable reasons for thisare that either the boundary segmentation has not been performed very accurately, or the methods require a significant amountof human interaction.We have attempted to solve the problem of boundary detection by using a computer vision technique known as active contourmodels [1] which mitigates many of the problems encountered in the earlier algorithms. We have also conducted a clinicalvalidation of our algorithm by applying the algorithm on image data from 12 patients, using manually-outlined borders as agold standard. We have extended the active contour model to 3-D, thus making use of the information available in thetemporal domain in echocardiograms. A few researchers have previously attempted to use active contour models forboundary detection in ultrasound images [5,