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Saha, M., Agarwal, S., Arun, I., Ahmed, R., Chatterjee, S., Mitra, P. and Chakraborty, C. (2015) Histogram Based Thresholding for Automated Nucleus Segmentation Using Breast Imprint Cytology. In: Advancements of Medical Electronics, Springer, India, New Delhi, 49-57.
https://doi.org/10.1007/978-81-322-2256-9_5

has been cited by the following article:

• TITLE: Using of Color Model and Contrast Variation in Wavelet Ideology for Study Megaloblastic Anemia Cells

  AUTHORS: Vyacheslav V. Lyashenko, Asaad MA. Babker

  KEYWORDS: Wavelet Analysis, Image, Contrast Enhancement, Medicine, Color Model, Megaloblastic Anemia Cells

  JOURNAL NAME: Open Journal of Blood Diseases, Vol.7 No.3, September 29, 2017

  ABSTRACT: Processing and analyzing of medical images is one of the priority research areas. At the same time, the processing of images of cells occupies a special place. This is due to the fact that such studies allow for a comprehensive diagnosis of the state of human health, identify and prevent the development of diseases in the early stages. We investigate the effectiveness of using wavelet analysis in color models, taking into account the preliminary change in the contrast of the input image. We consider the HSV color model and the image contrast modification procedure, which is based on the histogram change in the specified range with gamma correction. As a criterion for choosing parameters for changing the contrast of the image, we consider the entropy of the image. We also showed the advisability of using the value of the entropy index for the subsequent improvement of image analysis based on the wavelet ideology. We examined the general sequence of action for the analysis of image of megaloblastic anemia cells. This sequence is based on the choice of parameters for changing the contrast of the image and application of wavelet ideology.