低分辨率医学图像的多图谱分割方法

基于多图谱的图像分割方法因其分割精度高和鲁棒性强,在医学图像分割领域被广泛研究,主要包含图像配准和标签融合两个步骤.目前对多图谱分割方法的研究通常都是在图谱图像和待分割目标图像具有相同分辨率的情况下展开的.然而,由于受图像采集时间,采集设备等影响,临床实践中采集的影像大多是低分辨率数据,使得目前在影像研究中广泛使用的方法无法有效应用于临床实践.因此,针对这一问题,我们结合图像超分辨率恢复方法,提出了精确鲁棒的低分辨率医学图像的多图谱分割方法,实验结果显示提出的方法显著地提高了多图谱分割方法的分割精度.

Multi-atlas image segmentation for the low-resolution medical images

Due to the high segmentation accuracy and robustness,the multi-atlas based image segmentation method is currently a hot topic.It consists of two main components which are the image registration and the label fusion.The most of current multi-atlas based image segmentation methods consider the situation that the atlas images and the target image have the same resolution.But,we will always obtain the low-resolution target images because of the restriction on the acquisition time and collecting equipment.On the other hand,the atlases are generated before the target images,and we often use high-resolution images to obtain high-resolution atlases.Since the registration from high-resolution atlases to the low-resolution target image may not obtain the exact results,the accuracy of the multi-atlas based image segmentation methods will be reduced when applied to segment the low-resolution target images.In order to solve this problem,we present an accurate and robust image segmentation method for low-resolution target images by combining the advantages of the image super-resolution method and the multi-atlas segmentation method.The experiment results show that the proposed method significantly improves the accuracy of the original multi-atlas based image segmentation method.