Texture based image recognition in microscopy images of diffuse gliomaswith multi-class gentle boosting mechanism

Jun Kong*, Lee Cooper, Ashish Sharma, Tahsin Kurc, Daniel J. Brat, Joel H. Saltz

*Corresponding author for this work

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

22 Scopus citations

Abstract

The diagnosis of diffuse gliomas requires the careful inspection of large amounts of visual data. Identifying tissue regions that inform diagnosis is a cumbersome task for human reviewers and is a process prone to inter-reader variability. In this paper we present an automatic method for identifying critical diagnostic regions within whole-slide microscopy images of gliomas. We frame the problem of critical region identification as a texture-based content retrieval task in the sense that each image is represented by a set of texture features. Both linear and nonlinear dimensionality reduction techniques are utilized to explore the intrinsic dimensionality of the feature space where images are classified by classification and regression trees with performances improved by a newly extended multi-class gentle boosting (MCGB) mechanism. The proposed method is demonstrated on 1200 sample regions using a five-fold cross validation, achieving a 96.25% classification accuracy.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages457-460
Number of pages4
DOIs
StatePublished - 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Conference

Conference2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Country/TerritoryUnited States
CityDallas, TX
Period3/14/103/19/10

Keywords

  • Dimensionality reduction
  • Gentle boosting
  • Glioma
  • Microscopy image processing
  • Texture analysis

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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