Automatic Tuning of MST Segmentation of Mammograms for Registration and Mass Detection Algorithms

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 01 Jan 2009 Australia Publisher:IEEEJournal:2009 Digital Image Computing: Techniques and Applications

Authors: Mariusz Bajger; Fei Ma 0002; Murk J. Bottema;

doi: 10.1109/dicta.2009.72 , 10.5281/zenodo.854421 , 10.5281/zenodo.34778 , 10.5281/zenodo.854422

handle: 2328/25735

Automatic Tuning of MST Segmentation of Mammograms for Registration and Mass Detection Algorithms

- Summary
- Subjects
- Metrics

Abstract

In this presentation, 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.

Country

Australia

Related Organizations

Flinders University
Australia

Keywords

Databases, Breast tissue, Detection algorithms, mammograms, neutrosophic filtering, technique, entropy, Cancer

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	7
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average