Medical imaging correction: A comparative study of five contrast and brightness matching methods

Matsopoulos, GK

dc.contributor.author	Matsopoulos, GK	en
dc.date.accessioned	2014-03-01T02:11:25Z
dc.date.available	2014-03-01T02:11:25Z
dc.date.issued	2012	en
dc.identifier.issn	01692607	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29896
dc.subject	Brightness matching	en
dc.subject	Contrast matching	en
dc.subject	Digital Subtraction Radiography	en
dc.subject	Features of merit	en
dc.subject	Statistical relevance	en
dc.subject.other	Brightness matching	en
dc.subject.other	Contrast matching	en
dc.subject.other	Features of merit	en
dc.subject.other	Statistical relevance	en
dc.subject.other	Subtraction radiography	en
dc.subject.other	Graphic methods	en
dc.subject.other	Least squares approximations	en
dc.subject.other	Medical imaging	en
dc.subject.other	Luminance	en
dc.subject.other	article	en
dc.subject.other	brightness	en
dc.subject.other	comparative study	en
dc.subject.other	computer assisted tomography	en
dc.subject.other	contrast	en
dc.subject.other	diagnostic imaging	en
dc.subject.other	film	en
dc.subject.other	filter	en
dc.subject.other	histogram	en
dc.subject.other	image processing	en
dc.subject.other	qualitative analysis	en
dc.subject.other	quantitative analysis	en
dc.title	Medical imaging correction: A comparative study of five contrast and brightness matching methods	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.cmpb.2011.03.011	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.cmpb.2011.03.011	en
heal.publicationDate	2012	en
heal.abstract	Contrast and brightness matching are often required in many medical imaging applications, especially when comparing medical data acquired over different time periods, due to dissimilarities in the acquisition process. Numerous methods have been proposed in this field, ranging from simple correction filters to more complicated recursive techniques. This paper presents a comprehensive comparison of five methods for matching the contrast and brightness of medical image pairs, namely, Contrast Stretching, Ruttimann's Robust Film Correction, Boxcar Filtering, Least-Squares Approximation and Histogram Registration. The five methods were applied to a total of 100 image pairs, divided into five sets, in order to evaluate the performance of the compared methods on images with different levels of contrast, brightness and combinational contrast and brightness variations. Qualitative evaluation was performed by means of visual assessment on the corrected images as well as on digitally subtracted images, in order to estimate the deviations relative to the reference data. Quantitative evaluation was performed by pair-wise statistical evaluation on all image pairs in terms of specific features of merit based on widely used metrics. Following qualitative and quantitative analysis, it was deduced that the Histogram Registration method systematically outperformed the other four methods in comparison in most cases on average. © 2011 Elsevier Ireland Ltd.	en
heal.journalName	Computer Methods and Programs in Biomedicine	en
dc.identifier.doi	10.1016/j.cmpb.2011.03.011	en
dc.identifier.volume	106	en
dc.identifier.issue	3	en
dc.identifier.spage	308	en
dc.identifier.epage	327	en