Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
J Magn Reson Imaging. 2011 Oct;34(4):852-60. doi: 10.1002/jmri.22673. Epub 2011 Jul 18.
To introduce and describe the feasibility of a novel method for abdominal fat segmentation on both water-saturated and non-water-saturated MR images with improved absolute fat tissue quantification.
A general fat distribution model which fits both water-saturated (WS) and non-water-saturated (NWS) MR images based on image gray-level histogram is first proposed. Next, a novel fuzzy c-means clustering step followed by a simple thresholding is proposed to achieve automated and accurate abdominal quantification taking into consideration the partial-volume effects (PVE) in abdominal MR images. Eleven subjects were scanned at central abdomen levels with both WS and NWS MRI techniques. Synthesized "noisy" NWS (nNWS) images were also generated to study the impact of reduced SNR on fat quantification using the novel approach. The visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) amounts of the WS MR images were quantified with a traditional intensity thresholding method as a reference to evaluate the performance of the novel method on WS, NWS, and nNWS MR images.
The novel approach resulted in consistent SAT and VAT amounts for WS, NWS, and nNWS images. Automatic segmentation and incorporation of spatial information during segmentation improved speed and accuracy. These results were in good agreement with those from the WS images quantified with a traditional intensity thresholding method and accounted for PVE contributions.
The proposed method using a novel fuzzy c-means clustering method followed by thresholding can achieve consistent quantitative results on both WS and NWS abdominal MR images while accounting for PVE contributing inaccuracies.
介绍并描述一种新的方法，用于对水饱和和非水饱和磁共振图像进行腹部脂肪分割，以提高绝对脂肪组织定量的准确性。
首先提出了一种基于图像灰度直方图的通用脂肪分布模型，该模型同时适用于水饱和（WS）和非水饱和（NWS）MR 图像。接下来，提出了一种新的模糊 C 均值聚类步骤，然后是简单的阈值处理，以实现考虑腹部 MR 图像中的部分容积效应（PVE）的自动和准确的腹部定量。11 名受试者在中央腹部水平分别进行了 WS 和 NWS MRI 技术扫描。还生成了合成的“噪声”NWS（nNWS）图像，以研究使用新方法对降低 SNR 对脂肪定量的影响。通过传统的强度阈值方法量化 WS MR 图像的内脏脂肪组织（VAT）和皮下脂肪组织（SAT）量，以评估新方法在 WS、NWS 和 nNWS MR 图像上的性能。
新方法在 WS、NWS 和 nNWS 图像上均得到了一致的 SAT 和 VAT 量。自动分割和分割过程中的空间信息纳入提高了速度和准确性。这些结果与使用传统强度阈值方法量化的 WS 图像结果非常吻合，并且考虑到了 PVE 的贡献。
使用新的模糊 C 均值聚类方法和阈值处理的提出的方法可以在考虑到 PVE 贡献不准确的情况下，对 WS 和 NWS 腹部磁共振图像进行一致的定量结果。
