Recognition of Occlusions in Ct Images Using a Curve-Based Parameterization Method

Abstract

It is an important way for segmentations of CT images to extract contours of objects slice-by-slice. For such a way, an important idea is analogy. That is to say, correct the contour in current slice (current contour) according to the contour in previous slice (previous contour). The key to properly correct the current contour is the ability to recognize occlusions (or say leaking parts) in the current contour. We present a curvebased curve parameterization method to recognize occlusions. The previous contour is evolved to the current contour using line projections. In the process of evolution, the parameterization is realized, which includes two types of information for every point in the evolved contour: the arc length parameter on the previous contour, and distance moved from the initial position to the present position. Using these two parameters, we are able to recognize occlusions in the current contour. Many experiments indicate that the method can recognize all of the occlusions in a given contour. Consequently, the method is robust and can be used as a part of an algorithm to automatically extract contours for CT images.

Improve accuracy for automatic acetabulum segmentation in CT images

“…Our method is actually a heuristic method using an ellipsoid as the prior knowledge. There are also other methods using the previous contours as the prior knowledge (also called as heuristic contours) [10][11][12][13][14]26,27]. However, error diffusion makes them suffer from two disadvantages: (1) low automation degree due to low extracted contour accuracy.…”