1 / 22

NEURON CLASSIFICATION

NEURON CLASSIFICATION. By Divya Sai Jaladi Ravi Chandu K Padmini Krishna N. Classifications of a neuron …? . In general there are 4 major classifications of neuron. They are: Multi- Polar Neuron Bi- Polar Neuron Uni - Polar Neuron (Psuedo Neuron) Anaxonic Neuron.

kassia
Download Presentation

NEURON CLASSIFICATION

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. NEURON CLASSIFICATION By DivyaSaiJaladi Ravi Chandu K Padmini Krishna N

  2. Classifications of a neuron …? • In general there are 4 major classifications of neuron. They are: • Multi- Polar Neuron • Bi- Polar Neuron • Uni- Polar Neuron (Psuedo Neuron) • Anaxonic Neuron

  3. General Structure of a neuron:

  4. Main Goal Of our Project is… ! • Automatically Segment the neurons • Threshold • Quantify neurons • Extract noise from the image • Find seed points • And then extend them to larger images

  5. For segmentation • We can find types of segmentation plugin and currently we took Robust Automatic Threshold Selection.

  6. Color Threshold

  7. Analyzing particles..

  8. Finding surface area..

  9. Here comes the output…. !!

  10. Results

  11. screen play image… • Here we need to select a pixel where we could find a nucleus and there we should change the modifications of an image to bring the neuron out. • Type – 32 bits • Adjust threshold • Finding edges • Process – binary – erode

  12. Segmented and skeletonized

  13. Inverted..

  14. Resulted image.

  15. Still working on…. • Find the seed points • Quantify individual neurons • Find the volume and surface area • Should segment multiple cells for larger images.

  16. References • Haykin S. Neural Networks and Learning Machines.3rd Ed. London: Prentice Hall, 2009.  • D. Michael and J. Houchin  "Automatic EEG analysis: A segmentation procedure based on the autocorrelation function",  Electroenceph. Clin. Neurophysiol.,  vol. 46,  pp.232 -235 1979 [CrossRef] • http://www.cse.unr.edu/~bebis/CS791E/Notes/Thresholding.pdf

More Related