Konstantin Simonov
Russian Academy of Sciences
51 Papers
49 Citations
Konstantin Simonov is an academic researcher from Russian Academy of Sciences. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 5, co-authored 30 publications. Previous affiliations of Konstantin Simonov include Russian Academy & Al Maaref University College.
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Papers
Edge detection in MRI brain tumor images based on fuzzy C-means clustering
TL;DR: The proposed strategy to detect the edges of brain tumor from patient's MRI scan images of the brain includes some noise removal functions improving features that provides better characteristics of medical images for reliable diagnosis using Balance Contrast Enhancement Technique (BCET).
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Lung boundary detection for chest X-ray images classification based on GLCM and probabilistic neural networks
TL;DR: This paper presents an automated approach for lung boundary detection and CXR classification in conventional poster anterior chest radiographs, and extracts the lung regions, sizes of regions, and shape irregularities with segmentation techniques used in image processing on chest radiograph.
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Techniques for Medical Images Processing Using Shearlet Transform and Color Coding
Alexander G. Zotin,Konstantin Simonov,Fedor Kapsargin,Tatyana Cherepanova,Alexey Kruglyakov,Luis Cadena +5 more
- 01 Jan 2018
TL;DR: Novel methodology for processing medical images using a color coding of contour representation obtained by Digital Shearlet Transform (DST) has been presented and allows to improve the accuracy and decrease the error of the sought parameters and characteristics by 10–20% on average without a lack of significant details in the structural features of the examined objects.
21
Brain's Tumor Edge Detection on Low Contrast Medical Images
Yousif Hamad,Konstantin Simonov,Mohammad B. Naeem +2 more
- 01 Nov 2018
TL;DR: An increase in the accuracy of solving the problems of geometric analysis and segmentation, in some cases of tumor pathology, was found to be up to 10-15% better relative to the corresponding expert estimates.
20
Detection of Brain Tumor in MRI Images, Using a Combination of Fuzzy C-Means and Thresholding
Yousif Hamad,Konstantin Simonov,Mohammad B. Naeem +2 more
- 01 Jan 2019
TL;DR: A noise removal technique is used, followed by improvement of medical images for a correct diagnosis using a balance contrast enhancement technique (BCET), and the Canny edge detection method is applied to detect the fine edges.
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