Scaling images using their background ratio. An application in statistical comparisons of images
- 20 May 2003
- journal article
- research article
- Published by IOP Publishing in Physics in Medicine & Biology
- Vol. 48 (11) , 1539-1549
- https://doi.org/10.1088/0031-9155/48/11/304
Abstract
Comparison of two medical images often requires image scaling as a pre-processing step. This is usually done with the scaling-to-the-mean or scaling-to-the-maximum techniques which, under certain circumstances, in quantitative applications may contribute a significant amount of bias. In this paper, we present a simple scaling method which assumes only that the most predominant values in the corresponding images belong to their background structure. The ratio of the two images to be compared is calculated and its frequency histogram is plotted. The scaling factor is given by the position of the peak in this histogram which belongs to the background structure. The method was tested against the traditional scaling-to-the-mean technique on simulated planar gamma-camera images which were compared using pixelwise statistical parametric tests. Both sensitivity and specificity for each condition were measured over a range of different contrasts and sizes of inhomogeneity for the two scaling techniques. The new method was found to preserve sensitivity in all cases while the traditional technique resulted in significant degradation of sensitivity in certain cases.Keywords
This publication has 8 references indexed in Scilit:
- Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery RateNeuroImage, 2002
- Statistical limitations in functional neuroimaging II. Signal detection and statistical inferencePhilosophical Transactions Of The Royal Society B-Biological Sciences, 1999
- A unified statistical approach for determining significant signals in images of cerebral activationHuman Brain Mapping, 1996
- Statistical parametric maps in functional imaging: A general linear approachHuman Brain Mapping, 1994
- A Three-Dimensional Statistical Analysis for CBF Activation Studies in Human BrainJournal of Cerebral Blood Flow & Metabolism, 1992
- Rapid Automated Algorithm for Aligning and Reslicing PET ImagesJournal of Computer Assisted Tomography, 1992
- The Relationship between Global and Local Changes in PET ScansJournal of Cerebral Blood Flow & Metabolism, 1990
- Enhanced Detection of Focal Brain Responses Using Intersubject Averaging and Change-Distribution Analysis of Subtracted PET ImagesJournal of Cerebral Blood Flow & Metabolism, 1988