Кафедра медіасистем та технологій (МСТ)

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https://openarchive.nure.ua/handle/document/57

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Перегляд Кафедра медіасистем та технологій (МСТ) за автором "Asaad Babker"

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  • Зображення мініатюри
    Публікація
    Combined use of Edge Selection Operators in Individual Color Channels for the Analysis of Cytological Images Presented in RGB Format
    (Biomedical and Pharmacology Journal, 2025) Asaad Babker; Rania Saad Suliman; Aisha Ali M Ghazwani; Wiam Al Harbi; Lyashenko, V.
    Digital medical images study, analysis and processing is one of medical visualization areas, which helps to improve diagnostic diseases and detection, monitoring their progression and treatment. In this regard, the analysis of cytological images is important and effective, it allows to study various cellular structures. One of the components of such research is the allocation of potential areas of interest, considering the specifics of color medical images presentation. The paper proposes a new combined approach for identifying potential areas of interest based on edge detection operators, which is compared with the corresponding classical methods. It is shown that the proposed combined approach gives results no worse than classical methods, and for some types of images, even better. At the same time, resulting images quality assessments compactness is achieved, the range of the obtained results for making effective decisions is expanded depending on the specification of the analysis goals. It is shown that for images with a semi-transparent background, low-contrast difference between the background and objects of interest the proposed approach, in comparison with classical approaches, provides superiority in terms of niqe quality assessment of at least 10%, brisque quality assessment of more than 20%, derivative assessments (ME and AE) – 7.5% and 1%, respectively. The analysis of potential areas of interest details based on the processed images visualization results is the best for the combined approach in all cases. At the same time, the study provides an answer to the best combination of edge detection operators in individual color channels and without using pre-processing methods of the original image. This allows for increased efficiency of clinical trials in making a diagnosis.
  • Зображення мініатюри
    Публікація
    Edge Detection and Contrast Enhancement in the Examination of Megaloblastic Anemia Cells in Medical Images with Comparative Analysis of Different Approaches
    (Biomedical and Pharmacology Journal, 2024) Asaad Babker; Anass Abbas; Manar Shalabi; Khalid Abdelsamea Mohamedahmed; Lyashenko, V.
    Medical imaging and digital image analysis are essential tools in diagnosing and detecting various diseases. One key application is the examination of blood smears, where specific cell types, such as those indicative of megaloblastic anemia, can be identified. A critical component of this process involves analyzing and studying relevant images, as well as conducting experiments to evaluate the effectiveness of different methods and approaches in addressing this diagnostic challenge. As a result of the comparative analysis, it was found that the most effective method for the purpose of isolating the edge with megaloblastic anemia cells is the approach based on the wavelet ideology. This approach has the best indicators of assessing the quality of the resulting images in comparison with other edge detection methods. In some cases, the value of such indicators exceeds similar values for other methods by more than 2 times. In some cases, the indicators for images after contrasting are higher than without contrasting. This is also typical for other approaches to edge detection in images with megaloblastic anemia cells. First, this is typical for images with a uniform background and the absence of multiple peaks in the histogram of the input image brightness distribution. In general, the issue of contrasting the original image for subsequent processing in order to detect edge remains open. At the same time, this study provides an answer to the most effective method for edge detection for images with megaloblastic anemia cells, using the original images contrasting procedure.