Computer-aided gleason grading of prostate cancer histopathological images using texton forests

Parmeshwar Khurd, Claus Bahlmann, Peter Maday, Ali Kamen, Summer Gibbs-Strauss, Elizabeth M. Genega, John V. Frangioni

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

43 Scopus citations

Abstract

The Gleason score is the single most important prognostic indicator for prostate cancer candidates and plays a significant role in treatment planning. Histopathological imaging of prostate tissue samples provides the gold standard for obtaining the Gleason score, but the manual assignment of Gleason grades is a labor-intensive and errorprone process. We have developed a texture classification system for automatic and reproducible Gleason grading. Our system characterizes the texture in images belonging to a tumor grade by clustering extracted filter responses at each pixel into textons (basic texture elements). We have used random forests to cluster the filter responses into textons followed by the spatial pyramid match kernel in conjunction with an SVM classifier. We have demonstrated the efficacy of our system in distinguishing between Gleason grades 3 and 4.

Original languageEnglish (US)
Title of host publication2010 7th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2010 - Proceedings
Pages636-639
Number of pages4
DOIs
StatePublished - Aug 9 2010
Externally publishedYes
Event7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Rotterdam, Netherlands
Duration: Apr 14 2010Apr 17 2010

Publication series

Name2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings

Other

Other7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010
CountryNetherlands
CityRotterdam
Period4/14/104/17/10

Keywords

  • Gleason grading
  • Prostate cancer
  • Texture classification

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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    Khurd, P., Bahlmann, C., Maday, P., Kamen, A., Gibbs-Strauss, S., Genega, E. M., & Frangioni, J. V. (2010). Computer-aided gleason grading of prostate cancer histopathological images using texton forests. In 2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings (pp. 636-639). [5490096] (2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings). https://doi.org/10.1109/ISBI.2010.5490096