Oriented Markov Random Field Based Dendritic Spine Segmentation for Fluorescence Microscopy Images
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Oriented Markov Random Field Based Dendritic Spine Segmentation for Fluorescence Microscopy Images Jie Cheng & Xiaobo Zhou & Eric L. Miller & Veronica A. Alvarez & Bernardo L. Sabatini & Stephen T. C. Wong Published online: 29 June 2010 # Springer Science+Business Media, LLC 2010
Abstract Dendritic spines have been shown to be closely related to various functional properties of the neuron. Usually dendritic spines are manually labeled to analyze their morphological changes, which is very time-consuming and susceptible to operator bias, even with the assistance of computers. To deal with these issues, several methods have been recently proposed to automatically detect and measure the dendritic spines with little human interaction. However, problems such as degraded detection performance for images with larger pixel size (e.g. 0.125μm/pixel instead of 0.08μm/pixel) still exist in these methods. Moreover, the shapes of detected spines are also distorted. For example, the “necks” of some spines are missed. Here we present an oriented Markov random field (OMRF) based algorithm which improves spine detection as well as their geometric characterization. We begin with the identification of a region of interest (ROI) containing all the dendrites and spines to be analyzed. For this purpose, we introduce an J. Cheng : X. Zhou : S. T. C. Wong (*) The Center for Bioengineering and Informatics, The Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX 77030, USA e-mail: [email protected] V. A. Alvarez Section on Neuronal Structure (SNS), Laboratory for Integrative Neuroscience (LIN) NIAAA / NIH, 5625 Fishers Lane, Room TS-24, MSC9411, Bethesda, MD 20892-9411, USA B. L. Sabatini Department of Neurobiology, Harvard Medical School, 220 Longwood Ave, Boston, MA 02115, USA E. L. Miller Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA
adaptive procedure for identifying the image background. Next, the OMRF model is discussed within a statistical framework and the segmentation is solved as a maximum a posteriori estimation (MAP) problem, whose optimal solution is found by a knowledge-guided iterative conditional mode (KICM) algorithm. Compared with the existing algorithms, the proposed algorithm not only provides a more accurate representation of the spine shape, but also improves the detection performance by more than 50% with regard to reducing both the misses and false detection. Keywords Dendritic spine . Segmentation . Automatic detection . Microscopy image
Introduction The dendrites are cellular extensions of a neuron with many branches, where more than 90% of input to the neuron occurs. The dendritic spine is a small (sub-micrometer) membranous extrusion of the dendrites that contains the molecules and organelles involved in the postsynaptic processing of the synaptic information. The remarkable ability of dendritic spines to change shape rapidly, viz. the spine plasticity, is implicated in motivation, learnin
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