Information Fusion in Biomedical Image Analysis: Combination of Data vs. Combination of Interpretations

• Published in: Information Processing in Medical Imaging Vol. 19; pp. 150 - 161
• Main Authors: Rohlfing, T., Pfefferbaum, A., Sullivan, E. V., Maurer, C. R.
• Format: Book Chapter Journal Article
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005
• Series: Lecture Notes in Computer Science
• Subjects: Algorithms; Artificial Intelligence; Average Image; Biomedical Engineering - methods; Brain - anatomy & histology; Databases, Factual; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Information Fusion; Information Storage and Retrieval - methods; Information Theory; Magnetic Resonance Imaging - methods; Multiple Classifier System; Nonrigid Registration; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Subject Image; Subtraction Technique
• ISBN: 9783540265450; 3540265457
• ISSN: 0302-9743; 1011-2499; 1611-3349
• DOI: 10.1007/11505730_13

Information fusion has, in the form of multiple classifier systems, long been a successful tool in pattern recognition applications. It is also becoming increasingly popular in biomedical image analysis, for example in computer-aided diagnosis and in image segmentation. In this paper, we extend the principles of multiple classifier systems by considering information fusion of classifier inputs rather than on their outputs, as is usually done. We introduce the distinction between combination of data (i.e., classifier inputs) vs. combination of interpretations (i.e., classifier outputs). We illustrate the two levels of information fusion using four different biomedical image analysis applications that can be implemented using fusion of either data or interpretations: atlas-based image segmentation, “average image” tissue classification, multi-spectral classification, and deformation-based group morphometry.