Edge Detection and Contrast Enhancement in the Examination of Megaloblastic Anemia Cells in Medical Images with Comparative Analysis of Different Approaches

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