Automatic kidney segmentation from abdominal CT images is a key step in computer-aided diagnosis for kidney CT as well as computeraided surgery. However, kidney segmentation from CT images is generally performed manually or semi-autornatically because of gray levels similarities of adjacent organs/tissues in abdominal CT images. This paper presents an efficient algorithm for segmenting kidney from serials of abdominal CT images. First, we extracted estimated kidney position （EKP） according to the statistical geometric location of kidney within the abdomen. Second, we analyzed the intensity distribution of EKP for several abdominal CT images and exploit an adaptive threshold searching algorithm to eliminate many other organs/tissues in the EKP. Finally, a novel region growing approach based on labeling is used to obtain the fine kidney regions. Experimental results are comparable to those of manual tracing radiologist and shown to be efficient.
为了更好的了解血管生理形态,常需要从医学图像中分割出血管。采用传统的单阈值区域生长算法进行血管分割,如果选取的阈值太高可能导致血管末梢的丢失,而如果选取的阈值太低则可能会导致一些不属于血管的点被生长到血管区域中。因此,提出了一种基于Ordered Region Growing(ORG)算法的双阈值区域生长算法来克服这个缺点,实验证明这种算法能够获得较好的血管分割效果。
