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A Segmentation Method Based on Dynamic Programming for Breast Mass in MRI Images.

Authors :
Hutchison, David
Kanade, Takeo
Kittler, Josef
Kleinberg, Jon M.
Mattern, Friedemann
Mitchell, John C.
Naor, Moni
Nierstrasz, Oscar
Pandu Rangan, C.
Steffen, Bernhard
Sudan, Madhu
Terzopoulos, Demetri
Tygar, Doug
Vardi, Moshe Y.
Weikum, Gerhard
Zhang, David
Jihong Liu
Weina Ma
Soo-Young Lee
Source :
Medical Biometrics; 2008, p307-313, 7p
Publication Year :
2008

Abstract

The tumor segmentation in Breast MRI image is difficult due to the complicated galactophore structure. The work in this paper attempts to accurately segment the abnormal breast mass in MRI(Magnetic resonance imaging) Images. The ROI (Region of Interest) is segmented using a novel DP (Dynamic Programming) based optimal edge detection technique. DP is an optimal approach in multistage decision-making. The method presented in this paper processes the object image to get the minimum cumulative cost matrix combining with LUM nonlinear enhancement filter, Gaussian preprocessor, non-maximum suppression and double-threshold filtering, and then trace the whole optimal edge. The experimental results show that this method is robust and efficient on image edge detection and can segment the breast tumor area more accurately. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540774105
Database :
Complementary Index
Journal :
Medical Biometrics
Publication Type :
Book
Accession number :
34018535
Full Text :
https://doi.org/10.1007/978-3-540-77413-6_39