Nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography

Авторы: Dolganova IN, Chernomyrdin NV, Aleksandrova PV, Beshplav ST, Potapov AA, Reshetov IVANOVA, Kurlov VN, Tuchin VV, Zaytsev KI
Журнал: Journal of Biomedical Optics (SPIE),
Номер: 9
Год издания: 2018
Первая страница: 1
Последняя страница: 9
DOI: 10.1117/1.JBO.23.9.091406
Аннотация: We present the nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography (OCT). It employs an experimental training algorithm based on imaging of a test-object, made of the colloidal suspension of the monodisperse nanoparticles and contains the microscale inclusions. The geometry and the scattering properties of the test-object are known a priori allowing us to set the criteria for the training algorithm. Using a wide set of the wavelet kernels and the wavelet-domain filtration approaches, the appropriate filter is constructed based on the test-object imaging. We apply the proposed approach and chose an efficient wavelet denoising procedure by considering the combinations of the decomposition basis from five wavelet families with eight types of the filtration threshold. We demonstrate applicability of the wavelet-filtering for the in vitro OCT image of human brain meningioma. The observed results prove high efficiency of the proposed OCT image denoising technique.
Добавил в систему: Бешплав Шейх-Ислям Тимур оглы

	ИСТИНА	Войти в систему Регистрация
	ИПМех РАН
	Главная Поиск Статистика О проекте Помощь

ИСТИНА