Small mammographic lesions evaluation based on neural gas network

Oliver Lange; Anke Meyer-Baese; Axel Wismueller

doi:10.1117/12.719314

1 May 2007 Small mammographic lesions evaluation based on neural gas network

Oliver Lange, Anke Meyer-Baese, Axel Wismueller

Proceedings Volume 6560, Intelligent Computing: Theory and Applications V; 65600V (2007) https://doi.org/10.1117/12.719314
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

An application of an unsupervised self-organizing neural network, the neural gas network, is reported for the detection and characterization of small indeterminate breast lesions in dynamic contrast-enhanced MRI. This technique enables the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By revealing regional properties of contrast-agent uptake characterized by subtle differences of signal amplitude and dynamics, this method provides both a set of prototypical time-series and a corresponding set of cluster assignment maps which further provides a segmentation with regard to identification and regional subclassification of pathological breast tissue lesions. We present two different segmentation methods for the evaluation of signal intensity time courses for the differential diagnosis of enhancing lesions in breast MRI. Starting from the conventional methodology, we proceed by introducing the separate concepts of threshold segmentation and cluster analysis based on the neural gas network, and in the last step by combining those two concepts. The results suggest that the neural gas network has the potential to increase the diagnostic accuracy of MRI mammography by improving the sensitivity without reduction of specificity.

Citation Download Citation

Oliver Lange, Anke Meyer-Baese, and Axel Wismueller "Small mammographic lesions evaluation based on neural gas network", Proc. SPIE 6560, Intelligent Computing: Theory and Applications V, 65600V (1 May 2007); https://doi.org/10.1117/12.719314

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