Color Channel Selection when Using Wavelet Ideology for Edge Detection in Color Medical Images Represented in RGB Space.

Medical visualization is a fundamental method that guarantees the accuracy of data analysis for the reliable and prompt diagnosis of potential diseases, resulting in improving the public health. Among the instruments of medical visualization, the methods for delineating edges and identifying possible regions of interest are notable. The proposed study examines color medical images of blood smears exhibiting megaloblastic anemia. To perform the requisite analysis, the input image is enhanced using histogram equalization and segmented into multiple color channels of the RGB format. The wavelet theory of the db1 mother wavelet is employed to identify edges in picture objects. The evaluation of the acquired results relies on visual comparison and widely recognized metrics: niqe, brisque, and entropy. The results indicate that decomposing the original image into data corresponding to distinct color channels yields greater information. For example, the value of the entropy parameter for individual color channels exceeds the value of this parameter for the image as a whole, which enhances the findings, both within the context of each color channel and in comparison, to the grayscale format, which is crucial for employing wavelet theory in image processing. The acquired information can be utilized based on the issue formulation necessary for diagnosing the ailment and making educated decisions.