MODELING OF DIGITAL PROCESSING OF SKIN CANCER MEDICAL IMAGES BASED ON GAUSSIAN DIGITAL FILTER
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Abstract
This article analyzes the possibilities of developing and applying a filtering algorithm for image denoising based on one of the digital filters, the Gaussian digital filter. The Gaussian digital filter is characterized by the ability to effectively reduce noise in image and signal processing, while preserving the contours and structures of images. This approach, taking into account the effective aspects of the digital filter, allows for maximum noise removal and complete signal recovery in the field of biomedical signal and image processing. The article analyzes in detail the theoretical foundations of the Gaussian filter, its mathematical model, and its role in image smoothing. Studies are conducted on the optimization of the parameters of the Gaussian filter, the effect of the sigma value, and the contribution of the filter to image quality. Using the developed algorithm, the performance indicators in biomedical image processing, in particular, image smoothness, noise minimization, and interpolation accuracy, are analyzed. The article also evaluates the practical application of the Gaussian filter and its role in improving diagnostic results in real-life medical image processing. New possibilities for applying the approach to biomedical and other technical fields are considered. This approach more clearly reflects the effectiveness of the Gaussian filter and its importance in noise reduction.
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