Magnetic resonance imaging (MRI) is an information-rich research tool used in diagnostics using image processing applications (IPAs), and the results are utilized in machine learning. Therefore, testing of IPAs for credible results is vital. A deficient IPA would cause the related taxonomies of the machine learning to be defective as well and diagnosis will not be perfect. Accurate disease detection by IPA, without surgical intervention, leads to improved quality of treatment. Current challenges for testing of IPA include an absence of a test oracle. One way to alleviate the test oracle problem is metamorphic testing which identifies the specific properties called metamorphic relations of the system under test. Previously metamorphic testing approaches have been applied and evaluated on IPAs, but there is no previous work on evaluation of metamorphic testing on MRI images. In this work, we have evaluated effectiveness of metamorphic testing on edge detection of MRI images. The aim of this study is to determine which metamorphic relations are more effective for metamorphic testing of edge detection in MRI images such as T1, T2 and flair images. Our results show that the fault detection rate of MR4 is highest and MR2 is the lowest among all type of MRI images at the threshold of 0.95.
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.