title: SEGMENTATION OF MITOCHONDRIA IN ELECTRON MICROSCOPY IMAGES USING ALGEBRAIC CURVES.
abstract: High-resolution microscopy techniques have been used to generate large volumes of data with enough details for understanding the complex structure of the nervous system. However, automatic techniques are required to segment cells and intracellular structures in these multi-terabyte datasets and make anatomical analysis possible on a large scale. We propose a fully automated method that exploits both shape information and regional statistics to segment irregularly shaped intracellular structures such as mitochondria in electron microscopy (EM) images. The main idea is to use algebraic curves to extract shape features together with texture features from image patches. Then, these powerful features are used to learn a random forest classifier, which can predict mitochondria locations precisely. Finally, the algebraic curves together with regional information are used to segment the mitochondria at the predicted locations. We demonstrate that our method outperforms the state-of-the-art algorithms in segmentation of mitochondria in EM images.
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah.
authors.forename: Mojtaba
authors.initials: M
authors.lastname: Seyedhosseini
authors.affiliation: National Center for Microscopy and Imaging Research, University of California, San Diego.
authors.forename: Mark H
authors.initials: MH
authors.lastname: Ellisman
authors.affiliation: Scientific Computing and Imaging Institute, University of Utah.
authors.forename: Tolga
authors.initials: T
authors.lastname: Tasdizen
keywords: Mitochondria segmentation; algebraic curves; electron microscopy imaging; random forest