Publication
Title
Bias field reduction by localized LloydMax quantization
Author
Abstract
Bias field reduction is a common problem in medical imaging. A bias field usually manifests itself as a smooth intensity variation across the image. The resulting image inhomogeneity is a severe problem for posterior image processing and analysis techniques such as registration or segmentation. In this article, we present a novel debiasing technique based on localized LloydMax quantization (LMQ). The local bias is modeled as a multiplicative field and is assumed to be slowly varying. The method is based on the assumption that the global, undegraded histogram is characterized by a limited number of gray values. The goal is then to find the discrete intensity values such that spreading those values according to the local bias field reproduces the global histogram as good as possible. We show that our method is capable of efficiently reducing (even strong) bias fields in 3D volumes.
Language
English
Source (journal)
Magnetic resonance imaging. - New York
Publication
New York : 2011
ISSN
0730-725X
Volume/pages
29:4(2011), p. 536-545
ISI
000290000300008
Full text (Publishers DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 09.02.2011
Last edited 08.04.2017
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