Spectral Image Processing with Applications in Biotechnology and Pathology

• Main Author: Gavrilovic, Milan
• Format: Doctoral Thesis
• Language: English
• Published: Uppsala universitet, Centrum för bildanalys 2011
• Subjects: color theory; light microscopy; spectral imaging; image analysis; digital image processing; mathematical modeling; estimation; noise models; spectral decomposition; color decomposition; colocalization; cross-talk; autofluorescence; tissue separation; prostate cancer; biomedical applications; molecular biotechnology; histopathology
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-160574; http://nbn-resolving.de/urn:isbn:978-91-554-8209-1

Color theory was first formalized in the seventeenth century by Isaac Newton just a couple of decades after the first microscope was built. But it was not until the twentieth century that technological advances led to the integration of color theory, optical spectroscopy and light microscopy through spectral image processing. However, while the focus of image processing often concerns modeling of how images are perceived by humans, the goal of image processing in natural sciences and medicine is the objective analysis. This thesis is focused on color theory that promotes quantitative analysis rather than modeling how images are perceived by humans. Color and fluorescent dyes are routinely added to biological specimens visualizing features of interest. By applying spectral image processing to histopathology, subjectivity in diagnosis can be minimized, leading to a more objective basis for a course of treatment planning. Also, mathematical models for spectral image processing can be used in biotechnology research increasing accuracy and throughput, and decreasing bias. This thesis presents a model for spectral image formation that applies to both fluorescence and transmission light microscopy. The inverse model provides estimates of the relative concentration of each individual component in the observed mixture of dyes. Parameter estimation for the model is based on decoupling light intensity and spectral information. This novel spectral decomposition method consists of three steps: (1) photon and semiconductor noise modeling providing smoothing parameters, (2) image data transformation to a chromaticity plane removing  intensity variation while maintaining chromaticity differences, and (3) a piecewise linear decomposition combining advantages of spectral angle mapping and linear decomposition yielding relative dye concentrations. The methods described herein were used for evaluation of molecular biology techniques as well as for quantification and interpretation of image-based measurements. Examples of successful applications comprise quantification of colocalization, autofluorescence removal, classification of multicolor rolling circle products, and color decomposition of histological images.