NDLI: Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

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Multi-Atlas Library for Eliminating Normalization Failures in Non-Human Primates

Advanced Connectivity Analysis (ACA): a Large Scale Functional Connectivity Data Mining Environment

Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

Adaptive and Background-Aware GAL4 Expression Enhancement of Co-registered Confocal Microscopy Images

A Fast Method for the Segmentation of Synaptic Junctions and Mitochondria in Serial Electron Microscopic Images of the Brain

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Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

Content Provider	Springer Nature Link
Author	Radojević, Miroslav Smal, Ihor Meijering, Erik
Copyright Year	2015
Abstract	Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process.
Starting Page	201
Ending Page	219
Page Count	19
File Format	PDF
ISSN	15392791
Journal	Neuroinformatics
Volume Number	14
Issue Number	2
e-ISSN	15590089
Language	English
Publisher	Springer US
Publisher Date	2015-12-23
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Neuron reconstruction Junction detection Bifurcation detection Termination detection Fuzzy logic Fluorescence microscopy Image analysis Neurosciences Bioinformatics Computational Biology/Bioinformatics Computer Application in Life Sciences Neurology
Content Type	Text
Resource Type	Article
Subject	Neuroscience Information Systems Software

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Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

Fuzzy logic based detection of neuron bifurcations in microscopy images

Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

Automated neuron morphology reconstruction using fuzzy-logic detection and Bayesian tracing algorithms

Automatic Morphological Reconstruction of Neurons from Multiphoton and Confocal Microscopy Images Using 3D Tubular Models

Neuronal Tracing for Connectomic Studies

Oriented Markov Random Field Based Dendritic Spine Segmentation for Fluorescence Microscopy Images

Ensemble Neuron Tracer for 3D Neuron Reconstruction

Semi-Automated Neuron Boundary Detection and Nonbranching Process Segmentation in Electron Microscopy Images

Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

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