Novel Discrete Compactness-Based Training for Vector Quantization Networks : Enhancing Automatic Brain Tissue Classification
Author
Source
Advances in Artificial Neural Systems
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-30
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Information Technology and Computer Science
Abstract EN
An approach for nonsupervised segmentation of Computed Tomography (CT) brain slices which is based on the use of Vector Quantization Networks (VQNs) is described.
Images are segmented via a VQN in such way that tissue is characterized according to its geometrical and topological neighborhood.
The main contribution rises from the proposal of a similarity metric which is based on the application of Discrete Compactness (DC) which is a factor that provides information about the shape of an object.
One of its main strengths lies in the sense of its low sensitivity to variations, due to noise or capture defects, in the shape of an object.
We will present, compare, and discuss some examples of segmentation networks trained under Kohonen’s original algorithm and also under our similarity metric.
Some experiments are established in order to measure the effectiveness and robustness, under our application of interest, of the proposed networks and similarity metric.
American Psychological Association (APA)
Pérez-Aguila, Ricardo. 2013. Novel Discrete Compactness-Based Training for Vector Quantization Networks : Enhancing Automatic Brain Tissue Classification. Advances in Artificial Neural Systems،Vol. 2013, no. 2013, pp.1-18.
https://search.emarefa.net/detail/BIM-459722
Modern Language Association (MLA)
Pérez-Aguila, Ricardo. Novel Discrete Compactness-Based Training for Vector Quantization Networks : Enhancing Automatic Brain Tissue Classification. Advances in Artificial Neural Systems No. 2013 (2013), pp.1-18.
https://search.emarefa.net/detail/BIM-459722
American Medical Association (AMA)
Pérez-Aguila, Ricardo. Novel Discrete Compactness-Based Training for Vector Quantization Networks : Enhancing Automatic Brain Tissue Classification. Advances in Artificial Neural Systems. 2013. Vol. 2013, no. 2013, pp.1-18.
https://search.emarefa.net/detail/BIM-459722
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-459722