Contrast limited adaptive histogram equalization
Graphics gems IV
Case-based reasoning
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Scaling up Dynamic Time Warping to Massive Dataset
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Case-Based Reasoning in Knowledge Discovery and Data Mining
Case-Based Reasoning in Knowledge Discovery and Data Mining
Clustering Time Series with Clipped Data
Machine Learning
Medical applications in case-based reasoning
The Knowledge Engineering Review
A Sequential Hybrid Forecasting System for Demand Prediction
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Mining Frequent Trajectories of Moving Objects for Location Prediction
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Case-based reasoning in the health sciences: What's next?
Artificial Intelligence in Medicine
Ultrafast Localization of the Optic Disc Using Dimensionality Reduction of the Search Space
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Corpus callosum MR image classification
Knowledge-Based Systems
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This paper describes an approach to Case Based Reasoning (CBR) for image categorisation. The technique is founded on a time series analysis mechanism whereby images are represented as time series (curves) and compared using time series similarity techniques. There are a number of ways in which images can be represented as time series, this paper explores two. The first considers the entire image whereby the image is represented as a sequence of histograms. The second considers a particular feature (region of interest) contained across an image collection, which can then be represented as a time series. The proposed techniques then use dynamic time warping to compare image curves contained in a case base with that representing a new image example. The focus for the work described is two medical applications: (i) retinal image screening for Age-related Macular Degeneration (AMD) and (ii) the classification of Magnetic Resonance Imaging (MRI) brain scans according to the nature of the corpus callosum, a particular tissue feature that appears in such images. The proposed technique is described in detail together with a full evaluation in terms of the two applications.