Investigation of automated feature extraction techniques for applications in cancer detection from multispectral histopathology images

Neal R. Harvey, Richard M Levenson, David L. Rimm

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

14 Scopus citations

Abstract

Recent developments in imaging technology mean that it is now possible to obtain high-resolution histological image data at multiple wavelengths. This allows pathologists to image specimens over a full spectrum, thereby revealing (often subtle) distinctions between different types of tissue. With this type of data, the spectral content of the specimens, combined with quantitative spatial feature characterization may make it possible not only to identify the presence of an abnormality, but also to classify it accurately. However, such are the quantities and complexities of these data, that without new automated techniques to assist in the data analysis, the information contained in the data will remain inaccessible to those who need it. We investigate the application of a recently developed system for the automated analysis of multi-/hyper-spectral satellite image data to the problem of cancer detection from multispectral histopathology image data. The system provides a means for a human expert to provide training data simply by highlighting regions in an image using a computer mouse. Application of these feature extraction techniques to examples of both training and out-of-training-sample data demonstrate that these, as yet unoptimized, techniques already show promise in the discrimination between benign and malignant cells from a variety of samples.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
EditorsM. Sonka, J.M. Fitzpatrick
Pages557-566
Number of pages10
Volume5032 I
DOIs
StatePublished - 2003
Externally publishedYes
EventMedical Imaging 2003: Image Processing - San Diego, CA, United States
Duration: Feb 17 2003Feb 20 2003

Other

OtherMedical Imaging 2003: Image Processing
CountryUnited States
CitySan Diego, CA
Period2/17/032/20/03

Keywords

  • Cancer
  • Classification
  • Histopathology
  • Machine learning
  • Multispectral

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

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    Harvey, N. R., Levenson, R. M., & Rimm, D. L. (2003). Investigation of automated feature extraction techniques for applications in cancer detection from multispectral histopathology images. In M. Sonka, & J. M. Fitzpatrick (Eds.), Proceedings of SPIE - The International Society for Optical Engineering (Vol. 5032 I, pp. 557-566) https://doi.org/10.1117/12.480831