Left ventricular border recognition using a dynamic search algorithm.

Abstract

Initial results obtained with a simple, fully automated algorithm for detection of left ventricular boundaries are presented. The strength of this approach is the use of dynamic programming search techniques, which allow determination of local border points to be influenced by the entire global border location. The relative contributions of mask mode subtraction and the dynamic search technique are evaluated with respect to accurate border definition. These computer-determined ventricular borders are compared with hand-traced borders on subtracted and unsubtracted images. The modular dynamic search algorithm is shown to perform better than previously described algorithms, which generally require operator interaction. It is also shown that for both manual and automated techniques, ventricular borders derived from subtracted images may be significantly different from borders derived from nonsubtracted images.

Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach

“…In summary, the intersurface arc set E s for modeling two noncrossing surfaces is constructed as: (4) In other situations, we may allow the two interacting surfaces to cross each other. This is encountered when tracking a moving surface over time.…”

“…Many computer-based methods have been developed for optimal segmentation of 2D medical image data. Twodimensional boundary-based segmentation utilizing graph-searching principles [1], [2], [3], [4], [5], [6], [7] has become one of the best understood and frequently utilized medical image segmentation tools. As a result, 3D medical images were usually analyzed as sequences of 2D image slices forming the 3D data.…”

An object location strategy using shape and grey-level models

“…In that work an implicit circular shape model was used, candidate points were not assessed with respect to any global boundary description and ad-hoc rather than trained values were used in the cost function. Similar comments apply to work described by Pope et al 19 in which circular models and dynamic programming are used in the context of ven- tricular border recognition. In that paper only edge likelihood is used as a matching feature.…”

Automated left ventricle boundary delineation

“…But tracing the LV boundaries from LVG is a tedious job. Although automated boundary delineation (ABD) in LVGs has long been sought [3,4,1,13,7,19,12,18,17], no methods have been reported in terms of the accuracy of volume and ejection fraction (EF) derived from the ABD border, due to the challenges of LVG variation. Moreover, most previous methods were tested on only a limited number of LVG samples so that no consistent accuracy evaluation was available.…”

“…First, most of the methods only processed a single image at a time to detect the boundary [3,4,13]. Such methods ignored the helpful LV movement information through time in the cardiac cycle.…”