Skin Lesion Segmentation Using Adaptive Color Segmentation and Decision Tree

A decision tree operates as a predictive model in machine learning, utilizing a structured hierarchy of rules for the decision-making process. This model is graphically depicted through a tree-like structure, where each node symbolizes a decision point, and the ensuing branches represent the resultant outcomes of these decisions. This method proves particularly efficacious in addressing both classification and regression challenges, offering clear interpretability through its graphical illustration of the connections between input features and predicted outcomes. In the domain of image processing, color image segmentation serves as a crucial preliminary step for various applications. Specifically, the segmentation of skin lesions holds significant importance in image analysis, notably in the classification of lesions within dermoscopy images. Image segmentation involves the categorization of image pixels into uniform regions based on attributes like color, texture, and luminosity. The primary objective here is to delineate the area of interest from the healthy surrounding tissue. In methods reliant on threshold-based segmentation, the effectiveness of the segmentation hinges on the accurate selection of an appropriate threshold.