This paper presents an integrated image registration algorithm to correct the motion induced by patient breathing for dynamic renal perfusion MR images. Registration of kidneys through the MR image sequence is a challenging task due to rapidly changing image contrast over the course of contrast enhancement. Our algorithm achieves temporal image registration in a multi-step fashion. We first roughly register the images by detecting large-scale motion, and then refine the registration results by integrating region information and local gradient information with auxiliary image segmentation results. We have tested the proposed algorithm on several real patients and obtained excellent registration results.
It is stated that a closer intervention of experts in knowledge discovery can complement and improve the effectiveness of results. Normally, in data mining, automated methods display final results through visualization methods. A more active intervention of experts on automated methods can bring enhancements to the analysis; No meanwhile that approach raises questions about what is a relevant stopping stage. In this work, efforts are made to couple automatic methods with visualization methods in the context of partitioning algorithms applied to spatial data. A data mining workflow is presented with the following concepts: data mining transaction, data mining save point and data mining snapshot. Moreover to display results, novel visual metaphors are changed allowing a better exploration of clustering. In knowledge discovery, experts validate final results; certainly it would be appropriate to them validate intermediate results, avoiding, for instance, losing time, when in disagreement, starting it with new hypnoses or allow data reduction by disable an intermediate cluster from the next stage.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.