Unsupervised content classification based nonrigid registration of differently stained histology images

Registration of histopathology images of consecutive tissue sections stained with different histochemical or immunohistochemical stains is an important step in a number of application areas, such as the investigation of the pathology of a disease, validation of MRI sequences against tissue images, multiscale physical modeling, etc. In each case, information from each stain needs to be spatially aligned and combined to ascertain physical or functional properties of the tissue. However, in addition to the gigabyte-size images and nonrigid distortions present in the tissue, a major challenge for registering differently stained histology image pairs is the dissimilar structural appearance due to different stains highlighting different substances in tissues. In this paper, we address this challenge by developing an unsupervised content classification method that generates multichannel probability images from a roughly aligned image pair. Each channel corresponds to one automatically identified content class. The probability images enhance the structural similarity between image pairs. By integrating the classification method into a multiresolution-block-matching-based nonrigid registration scheme (N. Roberts, D. Magee, Y. Song, K. Brabazon, M. Shires, D. Crellin, N. Orsi, P. Quirke, and D. Treanor, “Toward routine use of 3D histopathology as a research tool,” Amer. J. Pathology, vol. 180, no. 5, 2012.), we improve the performance of registering multistained histology images. Evaluation was conducted on 77 histological image pairs taken from three liver specimens and one intervertebral disc specimen. In total, six types of histochemical stains were tested. We evaluated our method against the same registration method implemented without applying the classification algorithm (intensity-based registration) and the state-of-the-art mutual information based registration. Superior results are obtained with the proposed method.