The brain consists of massive regions with different functions and the precise delineation of brain region boundaries is important for brain region identification and atlas illustration. In this paper we propose a hierarchical Markov random field (MRF) model for brain region segmentation, where a MRF is applied to the downsampled low-resolution images and the result is used to initialize another MRF for the original high-resolution images. A fractional differential feature and a gray level co-occurrence matrix are extracted as the observed vector for the MRF and a new potential energy function, which can capture the spatial characteristic of brain regions, is proposed as well. A fuzzy entropy criterion is used to fine-tune the boundary from the hierarchical MRF model. We test the model both on synthetic images and real histological mouse brain images. The result suggests that the model can accurately identify target regions and even the whole mouse brain outline as a special case. An interesting observation is that the model cannot only segment regions with different cell density but also can segment regions with similar cell density and different cell morphology texture. Thus this model shows great potential for building the high-resolution 3D brain atlas.

中文翻译：

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基于马尔可夫随机场的单细胞分辨率组织学图像自动脑区域分割。

大脑由功能各异的块状区域组成，准确划分大脑区域边界对于大脑区域识别和地图集说明很重要。在本文中，我们提出了用于脑区域分割的分层马尔可夫随机场（MRF）模型，其中将MRF应用于下采样的低分辨率图像，并将结果用于初始化原始高分辨率图像的另一个MRF。提取分数微分特征和灰度共生矩阵作为MRF的观测向量，并提出了一个新的势能函数，它可以捕获大脑区域的空间特征。模糊熵准则用于微调分层MRF模型的边界。我们在合成图像和真实的组织学小鼠大脑图像上测试该模型。结果表明，该模型可以准确地识别目标区域，甚至可以识别整个老鼠的大脑轮廓。有趣的观察是该模型不仅可以分割具有不同细胞密度的区域，而且可以分割具有相似细胞密度和不同细胞形态纹理的区域。因此，该模型显示出构建高分辨率3D脑图集的巨大潜力。