Advance on curvelet application to prostate cancer tissue image classification

Wen Chyi Lin, Ching Chung Li, Jonathan Ira Epstein, Robert W. Veltri

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

Abstract

The approach of multi-resolution curvelet transform has been applied in our study of computer-aided classification of four critical Gleason patterns in prostate histological images. In the current study, we consider the maximum curvelet coefficients for the texture feature extraction to obtain more discriminative capability. A two-level classifier is re-designed, its excellent performance has been demonstrated.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2017-October
ISBN (Print)9781538625941
DOIs
StatePublished - Nov 16 2017
Event7th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2017 - Orlando, United States
Duration: Oct 19 2017Oct 21 2017

Other

Other7th IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2017
CountryUnited States
CityOrlando
Period10/19/1710/21/17

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Keywords

  • Curvelets
  • Gleason grading
  • Gleason scores
  • maximum curvelet coefficient
  • prostate cancer
  • tissue texture classification

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics

Cite this

Lin, W. C., Li, C. C., Epstein, J. I., & Veltri, R. W. (2017). Advance on curvelet application to prostate cancer tissue image classification. In 2017 IEEE 7th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2017 (Vol. 2017-October). [8114306] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCABS.2017.8114306