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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3 >Segmentation of magnetic resonance images of the thighs for a new National Institutes of Health initiative
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Segmentation of magnetic resonance images of the thighs for a new National Institutes of Health initiative

机译：一项新的美国国立卫生研究院倡议的大腿磁共振图像分割

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This paper describes a new system for semi-automatically segmenting the background, subcutaneous fat, interstitial fat, muscle, bone, and bone marrow from magnetic resonance images (MRI's) of volunteers for a new osteoarthritis study. Our system first creates separate right and left thigh images from a single MR image containing both legs. The subcutaneous fat boundary is very difficult to detect in these images and is therefore interactively defined with a single boundary. The volume within the boundary is then automatically processed with a series of clustering and morphological operations designed to identify and classify the different tissue types required for this study. Once the tissues have been identified, the volume of each tissue is determined and a single, false colored, segmented image results. We quantitatively compare the segmentation in three different ways. In our first method we simply compare the tissue volumes of the resulting segmentations performed independently on both the left and right thigh. A second quantification method compares our results temporally with three image sets of the same volunteer made one month apart including a month of leg disuse. Our final quantification methodology compares the volumes of different tissues detected with our system to the results of a manual segmentation performed by a trained expert. The segmented image results of four different volunteers using images acquired at three different times suggests that the system described in this paper provides more consistent results than the manually segmented set. Furthermore, measurements of the left and right thigh and temporal results for both segmentation methods follow the anticipated trend of increasing fat and decreasing muscle over the period of disuse.
机译：本文介绍了一种新系统，用于从志愿者的磁共振图像（MRI）进行半自动分割背景，皮下脂肪，间质脂肪，肌肉，骨骼和骨髓，以进行新的骨关节炎研究。我们的系统首先从包含双腿的单个MR图像创建单独的左右大腿图像。皮下脂肪边界很难在这些图像中检测到，因此可以通过单个边界交互式定义。然后使用一系列聚类和形态学操作自动处理边界内的体积，这些操作旨在识别和分类本研究所需的不同组织类型。一旦确定了组织，就确定每个组织的体积，并得到一个单一的，伪彩色的，分割的图像。我们以三种不同的方式定量比较细分。在我们的第一种方法中，我们简单地比较左大腿和右大腿分别进行的分割结果的组织体积。第二种量化方法在时间上将我们的结果与间隔一个月（包括一个月的腿部停用）的同一位志愿者的三组图像进行比较。我们的最终定量方法将我们的系统检测到的不同组织的体积与经过培训的专家进行的手动分割的结果进行比较。使用在三个不同时间获取的图像对四个不同志愿者进行的分割图像结果表明，与手动分割集相比，本文描述的系统提供了更一致的结果。此外，两种分割方法的左右大腿和颞部结果的测量均遵循了在停用期间脂肪增加和肌肉减少的预期趋势。

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