From quantitative microscopy to automated image understanding

Abstract

Quantitative microscopy has been extensively used in biomedical research and has provided significant insights into structure and dynamics at the cell and tissue level. The entire procedure of quantitative microscopy is comprised of specimen preparation, light absorption/reflection/emission from the specimen, microscope optical processing, optical/electrical conversion by a camera or detector, and computational processing of digitized images. Although many of the latest digital signal processing techniques have been successfully applied to compress, restore, and register digital microscope images, automated approaches for recognition and understanding of complex subcellular patterns in light microscope images have been far less widely used. In this review, we describe a systematic approach for interpreting protein subcellular distributions using various sets of Subcellular Location Features (SLF) in combination with supervised classification and unsupervised clustering methods. These methods can handle complex patterns in digital microscope images and the features can be applied for other purposes such as objectively choosing a representative image from a collection and performing statistical comparison of image sets.

High content image cytometry in the context of subnuclear organization

“…However, we must remain cautious as this was merely a small-scale proof of concept experiment. The analysis should be repeated on a larger number of cells and might benefit from the inclusion of less intuitive attributes, such as Zernike moments or Haralick texture features (66). In a regular transfection experiment, the outcome (the correct phenotype) is known, but in many high content screens the exact function and localization are not necessarily known.…”

Multimodal imaging of mouse development: Tools for the postgenomic era

“…Last, several research groups have focused on building new tools for image rendering and analysis for biological data mining. Although this is a vast area of research, I have included references for a small sampling of papers and reviews to provide examples of ongoing research (Lansford et al, 2001;AlKofahi et al, 2002AlKofahi et al, , 2003Jacobs et al, 2003;Megason and Fraser, 2003;Herskovits et al, 2004;Huang and Murphy, 2004;Warner et al, 2004;Ledesma-Carbayo et al, 2005;Liebling et al, 2005;Lin et al, 2005;Peng et al, 2005;Suhling et al, 2005;Toga, 2005;Tyszka et al, 2005a;Forouhar et al, 2006;Luders et al, 2006).…”

Cytomics and location proteomics: Automated interpretation of subcellular patterns in fluorescence microscope images

“…To meet the latter need, my colleagues and I began applying machine learning methods to subcellular pattern analysis a number of years ago. We initially demonstrated the feasibility of automated classification of subcellular patterns (8) and have extended and refined these results to the point that all major subcellular patterns can be recognized in two-and threedimensional images of single cultured cells with very high accuracy (9). An important conclusion from this work is that automated classifiers can not only be trained for this task but also can perform better than visual examination (10).…”