Seyedhosseini Mojtaba, Kumar Ritwik, Jurrus Elizabeth, Giuly Rick, Ellisman Mark, Pfister Hanspeter, Tasdizen Tolga
Electrical and Computer Engineering Department, University of Utah, USA.
Med Image Comput Comput Assist Interv. 2011;14(Pt 1):670-7. doi: 10.1007/978-3-642-23623-5_84.
Automated neural circuit reconstruction through electron microscopy (EM) images is a challenging problem. In this paper, we present a novel method that exploits multi-scale contextual information together with Radon-like features (RLF) to learn a series of discriminative models. The main idea is to build a framework which is capable of extracting information about cell membranes from a large contextual area of an EM image in a computationally efficient way. Toward this goal, we extract RLF that can be computed efficiently from the input image and generate a scale-space representation of the context images that are obtained at the output of each discriminative model in the series. Compared to a single-scale model, the use of a multi-scale representation of the context image gives the subsequent classifiers access to a larger contextual area in an effective way. Our strategy is general and independent of the classifier and has the potential to be used in any context based framework. We demonstrate that our method outperforms the state-of-the-art algorithms in detection of neuron membranes in EM images.
通过电子显微镜（EM）图像进行自动神经回路重建是一个具有挑战性的问题。在本文中，我们提出了一种新颖的方法，该方法利用多尺度上下文信息以及类拉东特征（RLF）来学习一系列判别模型。主要思想是构建一个框架，该框架能够以计算高效的方式从EM图像的大上下文区域中提取有关细胞膜的信息。为了实现这一目标，我们提取了可以从输入图像中高效计算的RLF，并生成在该系列中每个判别模型的输出处获得的上下文图像的尺度空间表示。与单尺度模型相比，上下文图像的多尺度表示的使用使后续分类器能够有效地访问更大的上下文区域。我们的策略是通用的，与分类器无关，并且有可能用于任何基于上下文的框架中。我们证明，在EM图像中检测神经元膜方面，我们的方法优于当前的先进算法。
