一种新的肝脏CT序列图像区域生长算法

Novel segmentation algorithm of region growing based on CT image sequences of liver

为了提高区域生长的分割精度,减少种子点选取对分割结果的影响和用户交互量。提出一种通过置信区间和区域竞争计算目标区域最优阈值区间,用于医学序列图像的区域生长分割算法。在方法上区域生长方法考虑的是图像的局部信息,而置信区间和区域竞争方法考虑的是图像的全局信息。该文的算法融合了两者的优点。通过在一张图片上选择目标对象和背景对象的多个种子点,实现了复杂背景下的序列图像分割。使用一组腹部CT原始图片进行的实验结果表明,算法在只需很少交互的情况下,有效地提高了分割精度。

In order to improve the accuracy of segmentation of region growing and reduce the number of user interaction,a segmentation algorithm of region growing based on confidence interval and region competition is presented.Region growing method focuses on local variations of an image while confidence interval and region competition can extract a global property of an image.This approach combines both advantages.And the segmentation from image-sequences of complex background can be achieved by selecting some seeds from object and background in an image of image-sequences.The experimental results with a serial of abdominal CT images show that the proposed algorithm can improve the accuracy of segmentation effectively with only very little interaction.