Robot-Guided Atomic Force Microscopy for Mechano-Visual Phenotyping of Cancer Specimens.
The analysis of protein-level multigene expression signature maps computed from the fusion of differently stained immunohistochemistry images is an emerging tool in cancer management. Creating these maps requires registering sets of histological images, a challenging task due to their large size, the non-linear distortions existing between consecutive sections and to the fact that the images correspond to different histological stains and thus, may have very different appearance. In this manuscript, we present a novel segmentation-based registration algorithm that exploits a multi-class pyramid and optimizes a fuzzy class assignment specially designed for this task. Compared to a standard nonrigid registration, the proposed method achieves an improved matching on both synthetic as well as real histological images of cancer lesions.