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A Feature-Based Approach to Big Data Analysis of Medical Images

Part of the Lecture Notes in Computer Science book series (LNIP,volume 9123)

Abstract

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches in O(log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of \(89\,\%\) if both exact and one-off predictions are considered correct.

Keywords

  • Near Neighbor
  • Kernel Density Estimation
  • Image Patch
  • Breathing State
  • Medical Image Data

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Acknowledgements

This research was supported by NIH grants P41EB015902, P41EB015898 5K25HL104085, 5R01HL116931 and 5R01HL116473.

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Correspondence to Matthew Toews .

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Toews, M., Wachinger, C., Estepar, R.S.J., Wells, W.M. (2015). A Feature-Based Approach to Big Data Analysis of Medical Images. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_26

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  • DOI: https://doi.org/10.1007/978-3-319-19992-4_26

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