A kernel-based framework for the estimation of the laser ablation area

This paper reports an automated method for accurately estimating the area of laser ablation patterns in optical microscopy (OM) images. The proposed technique employs clustering analysis and Depth-First Search (DFS) algorithms to identify and quantify the ablation area. Polynomial shading correction compensates for shading effects caused by high-magnification objective lenses, enhancing image quality. The method overcomes challenges from nonlinear separability and suboptimal solutions in conventional clustering algorithms. This approach offers a more efficient and accurate automated ablation area recognition solution by leveraging computer-vision capabilities.