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Research article summary:
Automatic landmark extraction from image data using modified growing neural gas network.
Abstract Extract:
A new method for automatic landmark extraction from MR brain images is presented. In this method, landmark extraction is accomplished by modifying growing neural gas (GNG), which is a neural-network-based cluster-seeking algorithm. Using modified GNG ... (Full abstract text below) Published 2003Jun
in Journal: IEEE Trans Inf Technol Biomed
(Language : eng)
Full Pubmed Extract
This information was retrieved, real-time, on your behalf from the public area of the Pubmed website:
1. IEEE Trans Inf Technol Biomed.
2003 Jun;7(2):77-85
Automatic landmark extraction from image data using modified growing neural gas network.
Fatemizadeh E, Lucas C, Soltanian-Zadeh H
Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran. emad@ipm.ir
A new method for automatic landmark extraction from MR brain images is presented. In this method, landmark extraction is accomplished by modifying growing neural gas (GNG), which is a neural-network-based cluster-seeking algorithm. Using modified GNG (MGNG) corresponding dominant points of contours extracted from two corresponding images are found. These contours are borders of segmented anatomical regions from brain images. The presented method is compared to: 1) the node splitting-merging Kohonen model and 2) the Teh-Chin algorithm (a well-known approach for dominant points extraction of ordered curves). It is shown that the proposed algorithm has lower distortion error, ability of extracting landmarks from two corresponding curves simultaneously, and also generates the best match according to five medical experts.
PMID : 12834162 [PubMed - Indexed for MEDLINE]
This information is obtained from the National Library of Medicine (NLM). Abstract text and other information may be subject to copyright. Type "NLM copyright" into Google for more information.
Full Author Information
| First Name | LastName | Initials |
| Emad | Fatemizadeh | E |
| Caro | Lucas | C |
| Hamid | Soltanian-Zadeh | H |
Affiliation: Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran. emad@ipm.ir
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MESH categories and related page links
This article was linked to the MESH categories shown on the left below. The links on the right are related Memletics pages.
Category links from this article:- Algorithms
- Brain - anatomy & histology
- Cluster Analysis
- Computer Simulation
- Humans
- Image Enhancement - methods
- Image Interpretation, Computer-Assisted - methods
- Magnetic Resonance Imaging - methods
- Neural Networks (Computer)
- Observer Variation
- Pattern Recognition, Automated
- Reproducibility of Results
- Sensitivity and Specificity
- Subtraction Technique
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