Computer-aided diagnosis in breast MRI based on ICA and unsupervised clustering techniques

Anke Meyer-Baese; Oliver Lange; Axel Wismuller; Gerda Leinsinger

doi:10.1117/12.601007

28 March 2005 Computer-aided diagnosis in breast MRI based on ICA and unsupervised clustering techniques

Anke Meyer-Baese; Oliver Lange; Axel Wismuller; Gerda Leinsinger

Author Affiliations +

Proceedings Volume 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III; (2005) https://doi.org/10.1117/12.601007
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

Exploratory data analysis techniques are applied to the segmentation of lesions in MRI mammography as a first step of a computer-aided diagnosis system. ICA and clustering techniques are tested on biomedical time-series representing breast MRI scans. This techniques enable 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, these methods provide both a set of prototypical time-series and a corresponding set of cluster assignment maps which further provide a segmentation with regard to identification and regional subclassification of pathological breast tissue lesions.

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Anke Meyer-Baese, Anke Meyer-Baese, Oliver Lange, Oliver Lange, Axel Wismuller, Axel Wismuller, Gerda Leinsinger, Gerda Leinsinger, } "Computer-aided diagnosis in breast MRI based on ICA and unsupervised clustering techniques", Proc. SPIE 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, (28 March 2005); doi: 10.1117/12.601007; https://doi.org/10.1117/12.601007

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