Intensity-based nonrigid endomicroscopic image mosaicking incorporating texture relevance for compensation of tissue...

Image mosaicking has emerged as a universal technique to broaden the field-of-view of the probe-based confocal laser endomicroscopy (pCLE) imaging system. However, due to the influence of probe-tissue contact forces and optical components on imaging quality, existing mosaicking methods remain insufficient to deal with practical challenges. In this paper, we present the texture encoded sum of conditional variance (TESCV) as a novel similarity metric, and effectively incorporate it into a sequential mosaicking scheme to simultaneously correct rigid probe shift and nonrigid tissue deformation. TESCV combines both intensity dependency and texture relevance to quantify the differences between pCLE image frames, where a discriminative binary descriptor named fully cross-detected local derivative pattern (FCLDP) is designed to extract more detailed structural textures. Furthermore, we also analytically derive the closed-form gradient of TESCV with respect to the transformation variables. Experiments on the circular dataset highlighted the advantage of the TESCV metric in improving mosaicking performance compared with the other four recently published metrics. The comparison with the other four state-of-the-art mosaicking methods on the spiral and manual datasets indicated that the proposed TESCV-based method not only worked stably at different contact forces, but was also suitable for both low- and high-resolution imaging systems. With more accurate and delicate mosaics, the proposed method holds promises to meet clinical demands for intraoperative optical biopsy.