Unsupervised Neural Techniques Applied to MR Brain Image Segmentation
Joint Authors
Saez, Juan Manuel Gorriz
Salas-González, Diego
Ortiz, Andrés
Ramírez, Javier
Source
Advances in Artificial Neural Systems
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-06-07
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
The primary goal of brain image segmentation is to partition a given brain image into different regions representing anatomical structures.
Magnetic resonance image (MRI) segmentation is especially interesting, since accurate segmentation in white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders such as dementia, schizophrenia or Alzheimer’s disease (AD).
Then, image segmentation results in a very interesting tool for neuroanatomical analyses.
In this paper we show three alternatives to MR brain image segmentation algorithms, with the Self-Organizing Map (SOM) as the core of the algorithms.
The procedures devised do not use any a priori knowledge about voxel class assignment, and results in fully-unsupervised methods for MRI segmentation, making it possible to automatically discover different tissue classes.
Our algorithm has been tested using the images from the Internet Brain Image Repository (IBSR) outperforming existing methods, providing values for the average overlap metric of 0.7 for the white and grey matter and 0.45 for the cerebrospinal fluid.
Furthermore, it also provides good results for high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain).
American Psychological Association (APA)
Ortiz, Andrés& Saez, Juan Manuel Gorriz& Ramírez, Javier& Salas-González, Diego. 2012. Unsupervised Neural Techniques Applied to MR Brain Image Segmentation. Advances in Artificial Neural Systems،Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-473058
Modern Language Association (MLA)
Ortiz, Andrés…[et al.]. Unsupervised Neural Techniques Applied to MR Brain Image Segmentation. Advances in Artificial Neural Systems No. 2012 (2012), pp.1-7.
https://search.emarefa.net/detail/BIM-473058
American Medical Association (AMA)
Ortiz, Andrés& Saez, Juan Manuel Gorriz& Ramírez, Javier& Salas-González, Diego. Unsupervised Neural Techniques Applied to MR Brain Image Segmentation. Advances in Artificial Neural Systems. 2012. Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-473058
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-473058