A Fuzzy Locally Adaptive Bayesian Segmentation Approach for Volume Determination in PET
A Fuzzy Locally Adaptive Bayesian Segmentation Approach for Volume Determination in PET
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DOI：
10.1109/tmi.2008.2012036
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发表时间：
2009-06-01
影响因子：
10.6
通讯作者：
Visvikis, Dimitris
中科院分区：
文献类型：
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作者：
Hatt, Mathieu;le Rest, Catherine Cheze;Visvikis, Dimitris
正电子发射断层扫描（PET）中准确的体积估计对于不同的肿瘤学应用至关重要。我们的研究目的是开发一种新的模糊局部自适应贝叶斯（FLAB）分割自动病变体积划定。FLAB与阈值方法以及先前提出的模糊隐马尔可夫链（FHMC）和模糊C均值（FCM）算法进行了比较。在IEC体模的采集数据集上评估了算法的性能，涵盖了一系列球形病变尺寸（10-37 mm）、对比度（4：1和8：1）、噪声水平（1、2和5 min采集）和体素尺寸（8和64 mm（3））。此外，在模拟患者病变的真实非均匀和非球形体积上评估了FLAB模型的性能。结果表明，FLAB的性能优于其他方法，特别是对较小的对象。考虑到对比度和图像质量，不同球体尺寸（低至13 mm）的体积误差为5%-15%，具有高再现性（变化
Accurate volume estimation in positron emission tomography (PET) is crucial for different oncology applications. The objective of our study was to develop a new fuzzy locally adaptive Bayesian (FLAB) segmentation for automatic lesion volume delineation. FLAB was compared with a threshold approach as well as the previously proposed fuzzy hidden Markov chains (FHMC) and the fuzzy C-Means (FCM) algorithms. The performance of the algorithms was assessed on acquired datasets of the IEC phantom, covering a range of spherical lesion sizes (10-37 mm), contrast ratios (4:1 and 8:1), noise levels (1, 2, and 5 min acquisitions), and voxel sizes (8 and 64 mm(3)). In addition, the performance of the FLAB model was assessed on realistic nonuniform and nonspherical volumes simulated from patient lesions. Results show that FLAB performs better than the other methodologies, particularly for smaller objects. The volume error was 5%-15% for the different sphere sizes (down to 13 mm), contrast and image qualities considered, with a high reproducibility (variation