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Analyzing Segmented Neurons: Methods for Cell Body Identification and Projection Measurement

This study focuses on the analysis of segmented neurons, aiming to identify cell bodies, measure the number of projections, and determine cell locations along with their proximity to other cell types. With approximately 28 billion neurons in the human body, effective image segmentation is critical for advanced neuroscience research. Using the Skeletonize method, we measure the average and maximum lengths of neuron projections and classify segments into endpoints, junctions, and slabs. Future work involves developing a plugin for more accurate segmentation through seeded region growing.

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Analyzing Segmented Neurons: Methods for Cell Body Identification and Projection Measurement

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Presentation Transcript

  1. NeuronClassification SASRUSHA KUSHAL SHASHANK

  2. Goal • To analyze segmented cells • Identify cell body • Number of projections • Cell location • Proximity to other cell types

  3. Neurons: The basic unit of the nervous system is neuron. The basic function of the neuron is to transmit information. There are approximately 28 billion neurons in the human body penetrating every tissue in every part. 

  4. Structure of Neuron:

  5. Segmentation • Goal of segmentation is to simplify and change the representation of an image into something that is more meaningful and easier to analyze. • Image Segmentation is typically used to locate objects and boundaries in images.

  6. Original Image

  7. Number Of Projections • To find number of projections and to measure their average and maximum length we used the Skeletonize method. • In skeletonized image the voxels are classified into three different categories • End –point • Junction • Slab

  8. Future Works We are developing a plugin for seeded region growing to do segmentation by using level set to find results more accurately.

  9. References • http://fiji.sc/Segmentation • http://www.csee.usf.edu/~manohar/Papers/Segmentation/Seeded%20Region%20growing.pdf • http://rsbweb.nih.gov/ij/docs/user-guide.pdf

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