Sparsity-based shrinkage approach for practicability improvement of H-LBP-based edge extraction

• An novel sparsity-based shrinkage approach for edge extraction on digital radiography is proposed.
• The threshold of SS-LBP can be adaptive to the data sparsity.
• SS-LBP is the development of AH-LBP and H-LBP.
• Without boosting processing time and losing processing efficiency, SS-LBP can avoid elaborately artificial modulation of parameters provides.
• SS-LBP has more robust performance in edge extraction compared with the existing methods.

The local binary pattern with H function (H-LBP) technique enables fast and efficient edge extraction in digital radiography. In this paper, we reformulate the model of H-LBP and propose a novel sparsity-based shrinkage approach, in which the threshold can be adapted to the data sparsity. Using this model, we upgrade fast H-LBP framework and apply it to real digital radiography. The experiments show that the method improved using the new shrinkage approach can avoid elaborately artificial modulation of parameters and possess greater robustness in edge extraction compared with the other current methods without increasing processing time.