Burn injury diagnostic imaging device's accuracy improved by outlier detection and removal

Weizhi Li, Weirong Mo, Xu Zhang, Yang Lu, John J. Squiers, Eric W. Sellke, Wensheng Fan, J. Michael DiMaio, Jeffery E. Thatcher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Multispectral imaging (MSI) was implemented to develop a burn diagnostic device that will assist burn surgeons in planning and performing burn debridement surgery by classifying burn tissue. In order to build a burn classification model, training data that accurately represents the burn tissue is needed. Acquiring accurate training data is difficult, in part because the labeling of raw MSI data to the appropriate tissue classes is prone to errors. We hypothesized that these difficulties could be surmounted by removing outliers from the training dataset, leading to an improvement in the classification accuracy. A swine burn model was developed to build an initial MSI training database and study an algorithm's ability to classify clinically important tissues present in a burn injury. Once the ground-truth database was generated from the swine images, we then developed a multi-stage method based on Z-test and univariate analysis to detect and remove outliers from the training dataset. Using 10-fold cross validation, we compared the algorithm's accuracy when trained with and without the presence of outliers. The outlier detection and removal method reduced the variance of the training data from wavelength space, and test accuracy was improved from 63% to 76%. Establishing this simple method of conditioning for the training data improved the accuracy of the algorithm to match the current standard of care in burn injury assessment. Given that there are few burn surgeons and burn care facilities in the United States, this technology is expected to improve the standard of burn care for burn patients with less access to specialized facilities.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSPIE
Volume9472
ISBN (Print)9781628415889
DOIs
StatePublished - 2015
EventAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI - Baltimore, United States
Duration: Apr 21 2015Apr 23 2015

Other

OtherAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI
CountryUnited States
CityBaltimore
Period4/21/154/23/15

Keywords

  • Burn
  • Machine learning
  • Medical imaging
  • Model accuracy
  • Multispectral imaging
  • Outlier detection

ASJC Scopus subject areas

  • Applied Mathematics
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

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  • Cite this

    Li, W., Mo, W., Zhang, X., Lu, Y., Squiers, J. J., Sellke, E. W., Fan, W., DiMaio, J. M., & Thatcher, J. E. (2015). Burn injury diagnostic imaging device's accuracy improved by outlier detection and removal. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 9472). [947206] SPIE. https://doi.org/10.1117/12.2177433