The development of a multi-stage learning scheme using new tissue descriptors for automatic grading of prostatic carcinoma

Clara Mosquera-Lopez, Sos Agaian, Alejandro Velez-Hoyos

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

6 Scopus citations

Abstract

This paper introduces a new system for the automated classification of prostatic carcinomas from biopsy images. The important components of the proposed system are (1) the new features for tissue description based on hyper-complex wavelet analysis, quaternion color ratios, and modified local binary patterns; and (2) a new framework for multi-stage learning that integrates both multi-class and binary classifiers. The system performance is estimated by employing Hold-out cross-validation in a dataset of 71 prostate cancer biopsy images with different Gleason grades. Simulation results show that the presented technique is able to correctly classify images in 98.89% of the test cases. Furthermore, the system is robust in terms of sensitivity (0.9833) and specificity (0.9917). We have demonstrated the efficacy of our system in distinguishing between Gleason grades 3, 4 and 5.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3586-3590
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - 2014
Externally publishedYes
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period5/4/145/9/14

Keywords

  • Automated Gleason grading
  • histopathology image analysis
  • multi-classifier systems
  • quaternion features

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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