Noise Estimation Using Back Propagation Neural Networks

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dc.contributor.authorSingh, D.
dc.contributor.authorKaur, A.
dc.date.accessioned2024-01-21T10:48:38Z
dc.date.accessioned2024-08-14T05:05:58Z
dc.date.available2024-01-21T10:48:38Z
dc.date.available2024-08-14T05:05:58Z
dc.date.issued2022-04-29T00:00:00
dc.description.abstractIn this paper, a new Backpropagation Neural Network-based noise estimation method is proposed to estimate Rician noise from MRI images. To train BNN features of MRI images such as, contrast, homogeneity, dissimilarity, asm, energy, entropy, meanx, meany, meanglcm, varx, vary, varglcm, correlation, skewx, skewy, skew, kurtosisx, kurtosisy, kurtosis etc are used. For training BNN, four hundred fifty images are used which are downloaded from Brain web. � The Electrochemical Societyen_US
dc.identifier.doi10.1149/10701.18761ecst
dc.identifier.isbn9781607685395
dc.identifier.issn19386737
dc.identifier.urihttp://10.2.3.109/handle/32116/3904
dc.identifier.urlhttps://iopscience.iop.org/article/10.1149/10701.18761ecst
dc.language.isoen_USen_US
dc.publisherInstitute of Physicsen_US
dc.subjectBackpropagationen_US
dc.subjectMagnetic resonance imagingen_US
dc.subjectBack-propagation neural networksen_US
dc.subjectEnergy-entropyen_US
dc.subjectEstimation methodsen_US
dc.subjectMRI Imageen_US
dc.subjectNetwork-baseden_US
dc.subjectNoise estimationen_US
dc.subjectNeural networksen_US
dc.titleNoise Estimation Using Back Propagation Neural Networksen_US
dc.title.journalECS Transactionsen_US
dc.typeConference paperen_US
dc.type.accesstypeClosed Accessen_US

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