Rational Variety Mapping for Contrast-Enhanced Nonlinear Unsupervised Segmentation of Multispectral Images of Unstained Specimen

Abstract

A methodology is proposed for nonlinear contrastenhanced unsupervised segmentation of multispectral (color) microscopy images of principally unstained specimens. The methodology exploits spectral diversity and spatial sparseness to find anatomical differences between materials (cells, nuclei, and background) present in the image. It consists of rth-order rational variety mapping (RVM) followed by matrix/tensor factorization. Sparseness constraint implies duality between nonlinear unsupervised segmentation and multiclass pattern assignment problems. Classes not linearly separable in the original input space become separable with high probability in the higher-dimensional mapped space. Hence, RVM mapping has two advantages: it takes implicitly into account nonlinearities present in the image (ie, they are not required to be known) and it increases spectral diversity (ie, contrast) between materials, due to increased dimensionality of the mapped space. This is expected to improve performance of systems for automated classification and analysis of microscopic histopathological images. The methodology was validated using RVM of the second and third orders of the experimental multispectral microscopy images of unstained sciatic nerve fibers (nervus ischiadicus) and of unstained white pulp in the spleen tissue, compared with a manually defined ground truth labeled by two trained pathophysiologists. The methodology can also be useful Staining of the specimen in the slide preparation process has been standard procedure for many years, because it increases contrast between the cell and the background. However, staining involves hours of preprocessing of the specimen, and can also add chemical effects to the nature of the cells, cause their shrinkage, and alter their morphology.1 For example, in studying effects of DNA damage on cell viability, fluorescent probes must not be used to stain the cell nuclei, to avoid compromising the viability of the cultures.2 Similarly, when studying the effects of inhibitor compounds designed to block the replication of cancerous cells, fluorescent dyes must not be used to mark nuclei, because the dyes themselves have toxicity.3 The subcellular localization of genetically encoded proteins imposes constraints on the cell recognition methods used to draw conclusion about function of a protein; again, staining of the cell is not allowed, to preserve the quality of the specimen and to avoid influencing the result of an investigation. 4 When staining is not allowed, contrast between the cell and the background will be poor, and it is challenging for either a trained pathologist or an automated image processing system to achieve good results. Furthermore, manual classification of cells by trained pathologists in images of stained specimens shows interobserver variability up to 20%.5 These findings prompted us to explore the possibility of developing a contrast enhancement methodology based on nonlinear unsupervised segmentation of multispectral images with poor contrast between the materials (ie...

Sequential dimensionality reduction for extracting localized features

“…NMU was used successfully for example in hyperspectral [10] and medical imaging [16,17], and for document classification [3].…”

Priors in sparse recursive decompositions of hyperspectral images

“…8 Moreover, it was theoretically and experimentally shown to be able to extract automatically constitutive materials in HSI. [9][10][11] However, sometimes, NMU fails at extracting all endmembers and mixes some of them. A possible way to improve NMU performances is to add prior information into the model.…”