Comparison of sparse coding and kernel methods for histopathological classification of gliobastoma multiforme

Ju Han, Hang Chang, Leandro Loss, Kai Zhang, Fredrick L. Baehner, Joe W. Gray, Paul Spellman, Bahram Parvin

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

22 Scopus citations

Abstract

This paper compares the performance of redundant representation and sparse coding against classical kernel methods for classifying histological sections. Sparse coding has been proven an effective technique for restoration, and has recently been extended to classification. The main issue with histology sections classification is inherent heterogeneity, which is a result of technical and biological variations. Technical variations originate from sample preparation, fixation, and staining from multiple laboratories, whereas biological variations originate from tissue content. Image patches are represented with invariant features at local and global scales, where local refers to responses measured with Laplacian of Gaussians, and global refers to measurements in the color space. Experiments are designed to learn dictionaries through sparse coding, and to train classifiers through kernel methods using normal, necrotic, apoptotic, and tumor regions with characteristics of high cellularity. Two different kernel methods, that of a support vector machine (SVM) and a kernel discriminant analysis (KDA), were used for comparative analysis. Preliminary investigation on the histological samples of Glioblastoma multiforme (GBM) indicates the kernel methods perform as good, if not better, than sparse coding with redundant representation.

Original languageEnglish (US)
Title of host publication2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI'11
Pages711-714
Number of pages4
DOIs
StatePublished - 2011
Event2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States
Duration: Mar 30 2011Apr 2 2011

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
CountryUnited States
CityChicago, IL
Period3/30/114/2/11

Keywords

  • Histology sections
  • dictionary learning
  • kernel methods
  • sparse coding

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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