A geometric snake model for segmentation of medical imagery.

In this note, we employ the new geometric active contour models formulated in [25] and [26] for edge detection and segmentation of magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound medical imagery. Our method is based on defining feature-based metrics on a given image which in turn leads to a novel snake paradigm in which the feature of interest may be considered to lie at the bottom of a potential well. Thus, the snake is attracted very quickly and efficiently to the desired feature.

The value of the ejection fraction as a predictor of postoperative cardiac mortality in patients undergoing peripheral vascular surgery.

Retrospective evaluation was undertaken in 131 patients who underwent peripheral vascular operations before which a multigated scan had been obtained. The patients were assigned to one of three groups according to their pre-operatively determined ejection fraction; 82 patients had an ejection fraction greater than 55%, in 38 patients the ejection fraction was between 36% and 54% and in 11 patients it was 35% or less. Two cardiac deaths occurred in each of the three groups. Although mortality in the three groups just failed to achieve statistical significance, metanalysis of published reports indicates that significant mortality exists for patients with an ejection fraction of less than 35% compared with those with an ejection fraction of over 55%. In this series, if the ejection fraction was used as the sole indicator, it was not an accurate predictor of peri-operative cardiac mortality. However, when used in conjunction with patient factors, such as cardiac history and status of the resting ECG, it did define a group of patients in whom there was a significant clinical risk of peri-operative cardiac mortality.