Analysis of Topological Distribution of Skin Lesions

In this thesis, we explore the application of edge detection and border detection algorithms in MATLAB for the purpose of skin lesion detection, focusing on melanoma. Specifically, we utilize the Canny and Sobel algorithms to identify and delineate the edges and borders of lesions in dermoscopic images. To assess the similarity of images and effectively detect lesions, we employ Normalized Cross-Correlation (NCC) and Mean Squared Error (MSE) methods. These techniques allow for the comparison of suspected melanoma regions against known samples, facilitating accurate diagnosis. Additionally, we investigate the use of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for object detection within the images. However, the results from DBSCAN were not satisfactory for our specific application, highlighting the challenges in clustering-based approaches for skin lesion detection. To enhance the accuracy of edge and border detection, we implement AI tools to remove image backgrounds, thereby reducing noise and improving the clarity of lesion boundaries. In addition to the edge and border detection, we comprehensively analyze the shape and distribution of moles across different regions of the body. This involves examining the geography of moles, mapping their cartography on the skin, and studying the topographic distribution of moles. Our algorithm is designed to detect lesions in various parts of the body, taking into account how the cartography of the skin can affect the detection accuracy. By understanding the topographic distribution, we can better predict lesion characteristics and improve detection accuracy. Our findings indicate that while traditional edge detection methods like Canny and Sobel are effective in identifying lesion borders, the integration of AI-assisted preprocessing steps significantly enhances the detection accuracy. The geographic and cartographic analysis of moles also provides valuable insights into how different body regions impact lesion formation and detection. This research contributes to the development of more reliable and precise image processing techniques for early and accurate detection of melanoma and other skin cancers.