Non-parametric image subtraction using grey level scattergrams

被引:15
作者
Bromiley, PA [1 ]
Thacker, NA [1 ]
Courtney, P [1 ]
机构
[1] Univ Manchester, Manchester M13 9PT, Lancs, England
关键词
image subtraction; grey-level scattergrams; multiple sclerosis;
D O I
10.1016/S0262-8856(02)00050-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Image subtraction is used in many areas of machine vision to identify small changes between equivalent pairs of images. Often only a small subset of the differences will be of interest. Simple image subtraction detects all differences regardless of their source, and is therefore, problematic to use. Superior techniques, analogous to standard statistical tests, can isolate localised differences due, for example, to motion from global differences due, for example, to illumination changes. Four such techniques are described. In particular, we introduce a new non-parametric statistical measure, which allows a direct probabilistic interpretation of image differences. We expect this to be applicable to a wide range of image formation processes. Its application to medical images is discussed. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:609 / 617
页数:9
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