Image segmentation with implicit color standardization using spatially constrained expectation maximization: detection of nuclei.

TitleImage segmentation with implicit color standardization using spatially constrained expectation maximization: detection of nuclei.
Publication TypeJournal Article
Year of Publication2012
AuthorsMonaco, JP, Hipp J, Lucas D, Smith SC, Balis U, Madabhushi A
JournalInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
IssuePt 1
Date Published2012
KeywordsAlgorithms, Area Under Curve, Bayes Theorem, Cell Nucleus, Color, Computer Simulation, Diagnostic Imaging, Gastrointestinal Tract, Humans, Image Processing, Computer-Assisted, Markov Chains, Models, Statistical, Pattern Recognition, Automated, ROC Curve

Color nonstandardness--the propensity for similar objects to exhibit different color properties across images--poses a significant problem in the computerized analysis of histopathology. Though many papers propose means for improving color constancy, the vast majority assume image formation via reflective light instead of light transmission as in microscopy, and thus are inappropriate for histological analysis. Previously, we presented a novel Bayesian color segmentation algorithm for histological images that is highly robust to color nonstandardness; this algorithm employed the expectation maximization (EM) algorithm to dynamically estimate for each individual image the probability density functions that describe the colors of salient objects. However, our approach, like most EM-based algorithms, ignored important spatial constraints, such as those modeled by Markov random field (MRFs). Addressing this deficiency, we now present spatially-constrained EM (SCEM), a novel approach for incorporating Markov priors into the EM framework. With respect to our segmentation system, we replace EM with SCEM and then assess its improved ability to segment nuclei in H&E stained histopathology. Segmentation performance is evaluated over seven (nearly) identical sections of gastrointestinal tissue stained using different protocols (simulating severe color nonstandardness). Over this dataset, our system identifies nuclear regions with an area under the receiver operator characteristic curve (AUC) of 0.838. If we disregard spatial constraints, the AUC drops to 0.748.

PDF Link

Alternate JournalMed Image Comput Comput Assist Interv

 *IEEE COPYRIGHT NOTICE: 1997 IEEE. * Personal use of this material is permitted. However, permission to reprint/ republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

*COPYRIGHT NOTICE:* These materials are presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.