Automatic landmark extraction from image data using modified growing neural gas network.

Emad Fatemizadeh; Caro Lucas; Hamid Soltanian-Zadeh

doi:10.1109/titb.2003.808501

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Automatic landmark extraction from image data using modified growing neural gas network.

Emad Fatemizadeh·Caro Lucas·Hamid Soltanian-Zadeh

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Abstract

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.

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Algorithms Brain Cluster Analysis Computer Simulation Humans Image Enhancement Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Neural Networks, Computer Observer Variation Pattern Recognition, Automated Reproducibility of Results Sensitivity and Specificity Subtraction Technique Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't Validation Study

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Emad Fatemizadeh, Caro Lucas, Hamid Soltanian-Zadeh. 2003. Automatic landmark extraction from image data using modified growing neural gas network.. https://doi.org/10.1109/titb.2003.808501

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