Detection of Neuron Membranes in Electron Microscopy Images Using Multi-scale Context and Radon-Like Features

Journal Article · Sat Oct 01 00:00:00 EDT 2011 · Lecture Notes in Computer Science, 6891:670-677

DOI:https://doi.org/10.1007/978-3-642-23623-5_84· OSTI ID:1130735

Seyedhosseini, Mojtaba; Kumar, Ritwik; Jurrus, Elizabeth R.; Giuly, Richard J.; Ellisman, Mark; Pfister, Hanspeter; Tasdizen, Tolga

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.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1130735
• Report Number(s): PNNL-SA-98708; WN0219080
• Journal Information: Lecture Notes in Computer Science, 6891:670-677, Journal Name: Lecture Notes in Computer Science, 6891:670-677
• Country of Publication: United States
• Language: English