Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK.
Med Phys. 2017 Nov;44(11):5782-5794. doi: 10.1002/mp.12511. Epub 2017 Aug 31.
Automated and accurate tissue classification in three-dimensional brain magnetic resonance images is essential in volumetric morphometry or as a preprocessing step for diagnosing brain diseases. However, noise, intensity in homogeneity, and partial volume effects limit the classification accuracy of existing methods. This paper provides a comparative study on the contributions of three commonly used image information priors for tissue classification in normal brains: image intensity, local, and multi-atlas priors.
We compared the effectiveness of the three priors by comparing the four methods modeling them: K-Means (KM), KM combined with a Markov Random Field (KM-MRF), multi-atlas segmentation (MAS), and the combination of KM, MRF, and MAS (KM-MRF-MAS). The key parameters and factors in each of the four methods are analyzed, and the performance of all the models is compared quantitatively and qualitatively on both simulated and real data.
The KM-MRF-MAS model that combines the three image information priors performs best.
The image intensity prior is insufficient to generate reasonable results for a few images. Introducing local and multi-atlas priors results in improved brain tissue classification. This study provides a general guide on what image information priors can be used for effective brain tissue classification.
在三维脑磁共振图像中进行自动且准确的组织分类，对于体积形态计量学或作为诊断脑疾病的预处理步骤至关重要。然而，噪声、不均匀的强度和部分容积效应限制了现有方法的分类准确性。本文对三种常用于正常脑组织分类的常用图像信息先验的贡献进行了比较研究：图像强度、局部和多图谱先验。
我们通过比较四种建模方法来比较三种先验的有效性：K-Means（KM）、与马尔可夫随机场（MRF）相结合的 KM（KM-MRF）、多图谱分割（MAS）以及 KM、MRF 和 MAS 的组合（KM-MRF-MAS）。分析了这四种方法中的关键参数和因素，并对模拟和真实数据进行了定量和定性比较。
结合三种图像信息先验的 KM-MRF-MAS 模型表现最佳。
图像强度先验不足以对少数图像产生合理的结果。引入局部和多图谱先验可改善脑组织结构分类。本研究为有效脑组织结构分类可使用哪些图像信息先验提供了一般指南。
