Parameters of Recognition Algorithms for the Background Subtraction of Color Medical Images


Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: Computer Science Online Conference, CSOC 2021

Год издания: 2021

Идентификатор DOI: 10.1007/978-3-030-77445-5_23

Ключевые слова: color medical images, laplacian of gaussian, unsharp masking, ziehl-neelsen method

Аннотация: Mycobacterium tuberculosis infection remains a major public health issue of global morbidity and mortality. One of the widely used methods for the finding of mycobacterium tuberculosis is the Ziehl-Neelsen method of microscopy. In this paper, a method for removing noise without producing image distortion for Ziehl-Neelsen stained iПоказать полностьюmages of sputum smear samples obtained using a light microscope is presented. The proposed approach is based on the convolution of the original image with the Laplacian of a Gaussian filter enhanced by high-frequency filtering. Used Laplacian of Gaussian filter was discretized as a 9x9 convolution kernel. If the original image is filtered with a simple Laplacian of Gaussian, the resulting output is rather noisy. Combining this result of filtration with the enhanced by high-frequency filtering will reduce the noise and will keep of mycobacterium tuberculosis for further analysis by automated medical diagnostic systems. To deal with the automatic determination of filtering quality the normalized color difference was proposed. The developed method of background subtraction can be used for images of microscopy and dermatology. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Ссылки на полный текст


Журнал: Lecture Notes in Networks and Systems

Выпуск журнала: Vol. 229

Номера страниц: 262-267

ISSN журнала: 23673370

Издатель: Springer Science and Business Media Deutschland GmbH


  • Shelomentseva I.G. (Krasnoyarsk State Medical University Named After Professor V.F. Voino-Yasenetsky, 1, Partizan Zheleznyak Avenue, Krasnoyrsk, 660022, Russian Federation, Siberian Federal University, 79, Svobodny Avenue, Krasnoyrsk, 660041, Russian Federation)
  • Chentsov S.V. (Siberian Federal University, 79, Svobodny Avenue, Krasnoyrsk, 660041, Russian Federation)
  • Yakasova N.V. (Siberian Federal University, 79, Svobodny Avenue, Krasnoyrsk, 660041, Russian Federation, Khakassia State University named after N. F. Katanov, 90, Lenin Avenue, Abakan, Republic of Khakassia, 655017, Russian Federation)

Вхождение в базы данных