基于局部特征约束的TEM图像分割算法

Local Feature-Constraint Information Based TEM Image Segmentation Algorithm

神经细胞图像分割对于神经科学研究具有重要应用价值。神经细胞亚显微结构的复杂性,以及透射电子显微成像(Transmission electron microscope,TEM)易出现的边界丢失、模糊等质量问题,使得神经细胞TEM图像的自动分割成为一个医学图像处理难题。基于神经细胞TEM图像的局部聚簇性特点,应用超像素技术,本文研究设计了一种基于局部特征约束的TEM图像分割算法。首先构建基于图模型的超像素图像结构表示,然后应用Markov随机场(Markov random field,MRF)模型提取超像素局部空间信息,从而有效地解决超像素图像分割方法中超像素点间邻域信息和空间结构复杂的问题,最后通过MRF模型优化和超像素合并处理获取图像分割结果。研究结果表明,该算法分割精度较高、鲁棒性强,且能很好地表征图像亚显微结构信息。

Nerve cells image segmentation is of great significance for neuroscience research,but the complexity of the nerve cells submicroscopic structure and the quality problem of the missing and fuzzy of the boundary produced by transmission electron microscope（ TEM） have making it being a problem in medical image processing.Based on the significant local clustering characteristics of nerve cell TEM images,a local feature-constraint information based TEM image segmentation algorithm is proposed by using the superpixels technology. First,superpixel structure is built based on the graph-based model. Then the local spatial information of superpixels based on MRF spatial neighborhood are extracted to solve the complex neighborhood information and space structure. Finally,the segmentation results can be obtained by the MRF model optimization and superpixels merging. The research results show that the proposed algorithm is accurate and robust with better describtation of submicroscopic structure.