Unsupervised content classification based nonrigid registration of differently stained histology images

Abstract

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.

Histopathology in 3D: From three-dimensional reconstruction to multi-stain and multi-modal analysis

“…The combined method has been demonstrated to outperform iterative optimization-based techniques both in terms of accuracy and run-time. [11] Our techniques have been applied in a number of different areas, and we continue to explore applications and collaborations in surgical planning, radiology sequence development validation, disease quantification, and a number of other areas.…”

“…These features in include the output of Gaussian filters on color and greyscale channels, and a novel derivative based texture feature. [11]Quantize the set of features separately for each image such that each pixel is represented by a prototype label (L1 x,y , L2 x,y ). Clustering is performed using a binary PCA-tree method.…”

“…Clustering is performed using a binary PCA-tree method. [11] This method was selected for its computational efficiency, and ability to work with variation of different scales.Consider a pair of hypothesized mapping functions that map the prototypes to a finite set of common tissue classes: C1 x,y = M1 (L1 x,y ) C2 x,y = M2 (L2 x,y ).For a given pair of mapping functions, it is possible to generate a tissue class co-occurrence matrix. The MI calculated from this matrix is a measure of the similarity between the two images under that pair of mapping functions (and that feature set).…”

“…These features in include the output of Gaussian filters on color and greyscale channels, and a novel derivative based texture feature. [11]…”

“…Clustering is performed using a binary PCA-tree method. [11] This method was selected for its computational efficiency, and ability to work with variation of different scales.…”