Border Detection of Skin Cancer Cells over Fractal Dimension Analysis and Image Processing Techniques

Abstract

Fractal dimension analysis is a novel technique that uses the self-similarity qualities of fractals to identify irregular forms, such as those prevalent in diseased tissues, in order to detect the borders of skin cancer cells. Acquire detailed pictures of skin tissue samples that have cancer cells in them. A variety of imaging methods, including microscopy and medical imaging tools like MRIs and CT scans, can be used for this. Determine the image's fractal dimension by applying suitable methods, like the fractal signature method or box-counting. A geometric shape's complexity is measured by its fractal dimension, and because malignant cells have uneven edges, they typically show higher complexity. To increase the border recognition process' accuracy, clean the photos to get rid of noise and boost contrast. Here, methods such as morphological procedures, histogram equalization, and median filtering can be used. To increase the border detection system's accuracy and resilience, fine-tune the parameters and algorithms in light of the validation results. A reliable approach for identifying the borders of skin cancer cells can be created by fusing fractal dimension analysis with image processing methods. This will help with early detection and therapy planning. Based on the fractal dimension, choose an appropriate threshold value to divide the image into zones of interest. This stage aids in the malignant cells' separation from the surrounding tissue.