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Please use this identifier to cite or link to this item: http://hdl.handle.net/2328/25735

Title: Automatic Tuning of MST Segmentation of Mammograms for Registration and Mass Detection Algorithms
Authors: Bajger, Mariusz
Ma, Fei
Bottema, Murk Jan
Keywords: Breast tissue
Cancer
Databases
Detection algorithms
Issue Date: 2009
Publisher: Institute of Electrical and Electronics Engineers Computer Society (IEEE Publishing)
Citation: Bajger, M., Ma, F. and Bottema, M. 2009. Automatic Tuning of MST Segmentation of Mammograms for Registration and Mass Detection Algorithms. 2009 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 400-407.
Abstract: A technique utilizing an entropy measure is developed for automatically tuning the segmentation of screening mammograms by minimum spanning trees (MST). The lack of such technique has been a major obstacle in previous work to segment mammograms for registration and applying mass detection algorithms. The proposed method is tested on two sets of mammograms: a set of 55 mammograms chosen from a publicly available Mini-MIAS database, and a set of 37 mammograms selected from a local database. The method performance is evaluated in conjunction with three different preprocessing filters: gaussian, anisotropic and neutrosophic. Results show that the automatic tuning has the potential to produce state-of-the art segmentation of mass-like objects in mammograms. The neutrosophic filtering provided the best performance.
URI: http://hdl.handle.net/2328/25735
ISBN: 9781424452972
Appears in Collections:Computer Science, Engineering and Mathematics - Collected Works

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