Wavelet-domain de-noising of OCT images of human brain malignant glioma I. N. Dolganova, P. V. Aleksandrova, S.-I.T. Beshplav [et al.]
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ArticleContent type: Текст Media type: электронный Subject(s): оптическая когерентная томография | вейвлет-анализ | опухоль головного мозга | злокачественная глиомаGenre/Form: статьи в журналах Online resources: Click here to access online
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Proceedings of SPIE Vol. 10717 : Saratov fall meeting 2017 : Laser physics and photonics XVIII; and Computational biophysics and analysis of biomedical data IV, 2017, Saratov, Russian Federation. P. 107171X-1-107171X-8Abstract: We have proposed a wavelet-domain de-noising technique for imaging of human brain malignant glioma by optical coherence tomography (OCT). It implies OCT image decomposition using the direct fast wavelet transform, thresholding of the obtained wavelet spectrum and further inverse fast wavelet transform for image reconstruction. By selecting both wavelet basis and thresholding procedure, we have found an optimal wavelet filter, which application improves differentiation of the considered brain tissue classes – i.e. malignant glioma and normal/intact tissue. Namely, it allows reducing the scattering noise in the OCT images and retaining signal decrement for each tissue class. Therefore, the observed results reveals the wavelet-domain de-noising as a prospective tool for improved characterization of biological tissue using the OCT.
Библиогр.: 59 назв.
We have proposed a wavelet-domain de-noising technique for imaging of human brain malignant glioma by optical coherence tomography (OCT). It implies OCT image decomposition using the direct fast wavelet transform, thresholding of the obtained wavelet spectrum and further inverse fast wavelet transform for image reconstruction. By selecting both wavelet basis and thresholding procedure, we have found an optimal wavelet filter, which application improves differentiation of the considered brain tissue classes – i.e. malignant glioma and normal/intact tissue. Namely, it allows reducing the scattering noise in the OCT images and retaining signal decrement for each tissue class. Therefore, the observed results reveals the wavelet-domain de-noising as a prospective tool for improved characterization of biological tissue using the OCT.
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