earticle

영어

Image segmentation is a fundamental and challenging problem in image processing and often a vital step for high level analysis. Considering of the inefficient curve evolution against weak boundary and intensity heterogeneous images, an improved level set segmentation framework guided by the image gradient function is proposed. In this framework, the edges and regions of the image are roughly divided by using the image gradient sample function. Compare to the Local Binary Fitting (LBF) model, local and global intensity fitting (LGIF) model, and Edge-flow based active contour model, this algorithm may improve efficient of curve evolution in a large extent. After that, we compare this algorithm with the other active contour model to show that segmenting the noisy blurry boundary and intensity heterogeneous images can be achieved, and still go on an in-depth comparison of these models. Finally, we show the results on challenging images to illustrate the accurate segmentations.