Variational level set and fuzzy clustering for enhanced thermal image segmentation and damage assessment

One of fundamental steps in the infrared thermographic process is the accurate segmentation of defects displayed on thermal images. State-of-the art segmentation algorithms are still inefficient to background noise, which may cause poor damage detection. In this study, an infrared image segmentation algorithm combined with artificial intelligence-based technology such as the variational level set and fuzzy clustering algorithm is proposed to enhance the quality of thermal images for damage assessment. Local Shannon entropy and fuzzy membership functions are introduced into the external clustering energy in order to make the algorithm robust to the clustering segmentation of noisy images. A higher-order derivative edge detection operator is used in the regularization energy to solve the singularity in the evolution of the level set function. An internal penalty energy is also introduced into the energy functional to avoid the re-initialization of the level set function and reduce the computational time. Experimental results on thermographic data are shown to demonstrate the efficiency and robustness of the proposed methodology.