Object auto-segmentation based on watershed and graph cut
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摘要: 为快速、准确的提取CT序列图像中目标物体,把分水岭和图割相结合.首先选择目标物体的内外轮廓,对内外轮廓之间的区域用分水岭算法预分割为若干小区域,把每一个小区域作为一个节点,建立图结构.把多源点和多汇点简化成单源点和单汇点,建立新的图结构.然后利用最大流/最小割定理进行切割,提取目标物体.最后把上一张CT目标物体的轮廓映射到下一张CT上,分别扩大和缩小该轮廓作为该CT的内外轮廓.根据上述方法提取轮廓,对整个CT序列依次循环操作.通过实验证明该算法在分割效果和分割时间上优于其它传统算法,同时,实现了三维空间上序列轮廓的自动提取.Abstract: In order to segment CT slices mostly and accurately, watershed algorithm and graph cut were combined. Firstly, the inner contour and the outer contour of the target object were selected, and then watershed algorithm was applied to divide the region between contours into series of smaller regions. Each smaller region was regarded as a node to establish the graph. Multiple sources and multiple sinks can be converted to the single source and the single sink to refine the graph. Secondly, the target object of the first CT can be extracted by the maximal-flow cut. Thirdly, mapping the contour to the next CT, the contour was reduced and expanded to regard as the inner contours and the outer contours. Then the next CT was segmented. Followed by the cycling, the entire sequence of CT will be operated. The experiment proves that this algorithm is more effective in the segmentation and the running time than the other traditional algorithms. Meanwhile, the contours of the series of CT were extracted automatically.
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