A general technique for automatic left ventricle boundary validation: Relation between gray scale cardioangiograms and observed boundary errors

Abstract

This article presents an automatic left ventricle boundary validation technique using the gray scale cardioangiograms, the observed boundary errors, and the left ventricle boundaries from any source that needs to be validated. This validation technique is based on the gray scale information near the boundary of the left ventricle in the cardioangiograms. Using a mutually exclusive window of fixed size, which is centered on the left ventricle boundary vertex and along the left ventricle contour, we compute a difference in contrast value for areas of the window both inside and outside the left ventricle region. These contrast values then are regressed against the observed boundary errors. The observed boundary errors are computed using the polyline distance measure [3], [4] by comparing two sets of boundaries: boundaries estimated from any boundary estimation algorithm, and the origina| ground truth boundaries as traced by the cardiologist. We performed our experiments on a database of 245 patient studies, each having two frames: end-diastole (ED) and end-systole (ES). The mean boundary error before running the validation system was 4.4 mm. Using our boundary validation system, by rejecting 5% to 10% of the patient studies, the validation system results in ah error of 4.0 mm for the crossvalidation case and 3.85 mm for the ideal case. We show the reliability curves of our validation system by computing the probability of false alarm, probability of mis-detection, and mean predicted errors when a total of n patients ate rejected from the database of 245 studies. performance of the estimated boundaries, but little attention has been given to its validation, For the LV boundary estimation system, the boundary validation is a crucial step because it determines those LV boundaries from the database (which could be coming fi'om any source) whose boundary error is above a given threshold. In this articte we develop a general and automatic validation technique to detect the LV boundaries whose mean end frame (end-diastole [ED] and end-systole [ES]) boundary errors ((ED + ES)/ 2) are above a given threshold, R,h. This validation scheme has several features and advantages. It determines those LV boundaries from the database whose ((ED + ES)/2) error is above a given threshold error. The scheme provides feedback to the boundary estimation system so that the system knows which LV boundaries can be rejected. The validation technique estimates the overall performance of the boundary estimation system without taking the rejected boundaries into consideration. The validation technique provides a check for consistency and retiability of the output boundary estimation algorithms (eg, pixel classification, calibration, active contour, and statistica] segmentation algorithms). For this test, we need three types of inputs: the boundary coordinates (x, y) of the LV boundaries that need to be validated, the gray scale cardioangiograms, and the binary indicator for the LV region (1 for inside the LV region and 0 for outside the LV regi...

Advanced Algorithmic Approaches to Medical Image Segmentation

“…Suri et al [247] developed a general and automatic validation technique that could detect the LV boundaries whose mean end frame boundary errors were above a forced before calibration. * shows the best performance.…”

“…Singh et al [210], Wikins et al [209] and McInerney et al [228] showed the implementation of fitting snakes to cardiac data but lacked a validation scheme. Suri et al [247] presented a validation scheme to find whether the estimated curve was at its correct position. (d) Robustness can only be confirmed once the algorithm is able to handle a large volume of data sets with normal and abnormal studies.…”

“…This is especially necessary when more than four classes of tissue types exist in complex brain pathology and have different kinds of neurological disorders. This becomes even more necessary when the functional information gathered from fMRI has started to be investigated (see Suri et al [247], Zwehl et al [248], Aboutanous et al [623] and Vannier et al [624]). (8) Integrating Fast 3-D Segmentation in Daily Therapy in Real-Time: interventional MR techniques require fast and robust 3-D segmentation, which is still a challenge to be conquered.…”

“…Suri et al showed the linear calibration with convex information[236],[238], [101],[247],[246] for different data sets (N) ranging from 245 to 377. Using the cross-validation protocol discussed in Sub-section 3.4.2.3, the polyline mean error in linear calibrator for InCM was 3.47 mm when N=291.…”