On a hybrid watershed-neural network approach for the segmentation of high field MR images of the spinal cord

Access Full Text

On a hybrid watershed-neural network approach for the segmentation of high field MR images of the spinal cord

For access to this article, please select a purchase option:

Buy conference paper PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
6th International Conference on Image Processing and its Applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

  • DOI:  10.1049/cp:19970922
  • ISBN: 0 85296 692 X
  • Location: Dublin, Ireland
  • Conference date: 14-17 July 1997
  • Format: PDF
We describe a general method for extraction of an image object based on an approximate knowledge of its shape and its position in the image. The method is a combination of the watershed approach and neural networks. It is applied to high field MR images of a section of a spinal cord for the extraction of the shape of the grey matter.

Inspec keywords: feature extraction; Hopfield neural nets; medical image processing; biomedical NMR; image segmentation; neural nets

Subjects: Biology and medical computing; Biomagnetism; Neural computing techniques; Patient diagnostic methods and instrumentation; Radiation and radioactivity applications in biomedicine; Medical magnetic resonance imaging and spectroscopy; Computer vision and image processing techniques; Optical information, image and video signal processing

Related content

content/conferences/10.1049/cp_19970922
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading