Study Of The Interactive Image Segmentation Algorithms Based On Fuzzy Connectedness

Image segmentation is the important foundation of higher-level computer vision.And it remains an open problem. However, image segmentation is an improperlyposed problem, which makes it one of the bottlenecks of computer vision. To solve theimpropriety, assistance of human is necessary. As a consequence, the interactivesegmentation has been arisen.A fast interactive segmentation algorithm of image-sequences based on relativefuzzy connectedness is presented, through modifying the existing one. In comparis-on with the existing algorithm, the proposed one, with the same accuracy, acceleratesthe segmentation speed by three times for single image. Meanwhile, this fastsegmentation algorithm is extended from single object to multiple objects and fromsingle-image to image-sequences. Thus the segmentation of multiple objects fromcomplex background and batch segmentation of image-sequences can be achieved. In order to improve the accuracy and speed of the segmentation algorithm, apre-segmentation scheme is presented to extract the ROI. In addition, a post-processing scheme is incorporated, in which we can extracts smooth edge with one-pixel-width for each segmented object. Pseudo-color processing is introduced todistinguish the fine structure of objects and the segmented multi-objects. And 3Dreconstruction is imported for the sake of illustrating image-sequences segmentationresults.Furthermore, a relative fuzzy connected interactive segmentation algorithmusing redundant wavelet transform is proposed to reduce the sensitivity to noise ofsegmentation. In contrast to the original scale-based algorithm, the proposed one,with almost the same accuracy, accelerates the segmentation speed by tens times forsingle image. And it has robustness to different noise models. In addition, a study ofthe influence of different redundant wavelet decomposition scales is also included. The experimental results illustrate that those proposed algorithms in this papercan obtain the ROI accurately from medical images as well as man-made images,which are nonnoised or noised, quickly and reliably with only a little interaction.And they have better performances over the traditional interactive segmentationalgorithms based on fuzzy connectedness.