Li Weifu, Deng Hao, Rao Qiang, Xie Qiwei, Chen Xi, Han Hua
* Faculty of Mathematics and Statistics, Hubei University, Wuhan 430062, China.
† Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
J Bioinform Comput Biol. 2017 Jun;15(3):1750015. doi: 10.1142/S0219720017500159. Epub 2017 May 26.
It is possible now to look more closely into mitochondrial physical structures due to the rapid development of electron microscope (EM). Mitochondrial physical structures play important roles in both cellular physiology and neuronal functions. Unfortunately, the segmentation of mitochondria from EM images has proven to be a difficult and challenging task, due to the presence of various subcellular structures, as well as image distortions in the sophisticated background. Although the current state-of-the-art algorithms have achieved some promising results, they have demonstrated poor performances on these mitochondria which are in close proximity to vesicles or various membranes. In order to overcome these limitations, this study proposes explicitly modelling the mitochondrial double membrane structures, and acquiring the image edges by way of ridge detection rather than by image gradient. In addition, this study also utilizes group-similarity in context to further optimize the local misleading segmentation. Then, the experimental results determined from the images acquired by automated tape-collecting ultramicrotome scanning electron microscopy (ATUM-SEM) demonstrate the effectiveness of this study's proposed algorithm.
由于电子显微镜（EM）的迅速发展，现在有可能更仔细地研究线粒体的物理结构。线粒体的物理结构在细胞生理学和神经元功能中都起着重要作用。不幸的是，由于存在各种亚细胞结构以及复杂背景中的图像失真，从EM图像中分割线粒体已被证明是一项困难且具有挑战性的任务。尽管当前最先进的算法已经取得了一些有希望的结果，但它们在靠近囊泡或各种膜的这些线粒体上表现不佳。为了克服这些限制，本研究提出明确地对线粒体双膜结构进行建模，并通过脊检测而不是图像梯度来获取图像边缘。此外，本研究还利用上下文的组相似性来进一步优化局部误导性分割。然后，由自动胶带收集超薄切片扫描电子显微镜（ATUM-SEM）获取的图像所确定的实验结果证明了本研究提出的算法的有效性。
