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Texture features in the Shearlet domain for histopathological image classification.

Sadiq Alinsaif1, Jochen Lang2

  • 1EECS, University of Ottawa, Ottawa, Canada. salin025@uottawa.ca.

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Summary

This study introduces a new texture feature representation in the Shearlet domain for automated histopathological image classification. The novel approach integrates multiple texture descriptors, achieving high accuracy across four public datasets for cancer detection and staging.

Keywords:
ClassificationComplex shearletHistologySVMTexture descriptors

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Area of Science:

  • Digital pathology and computational imaging.
  • Machine learning applications in medical diagnostics.
  • Biomedical signal and image processing.

Background:

  • Histopathological slide image analysis is the gold standard for cancer diagnosis and staging.
  • Traditional methods often rely on handcrafted texture analysis for supervised machine learning classification.
  • Existing computational techniques for histopathology image analysis show varying success rates.

Purpose of the Study:

  • To develop a novel feature space for automated classification of histopathological tissues.
  • To integrate diverse texture features within the complex Shearlet domain for enhanced representation.
  • To improve the accuracy of cancer classification in histology images using machine learning.

Main Methods:

  • Constructed a new texture feature representation by integrating multiple descriptors in the complex Shearlet domain.
  • Investigated the effectiveness of both magnitude and relative phase (RP) coefficients.
  • Extracted four texture-based descriptors: co-occurrence features, Local Binary Patterns, Local Oriented Statistic Information Booster, and segmentation-based Fractal Texture Analysis.
  • Reduced feature dimensionality using principal component analysis (PCA).
  • Evaluated the proposed feature space using Support Vector Machine (SVM) and Decision Tree Bagger (DTB) classifiers.

Main Results:

  • The novel feature representation achieved high performance on four public datasets.
  • Achieved maximum accuracies of 92.56% (Kather), 91.73% (BreakHis), 98.04% (Epistroma), and 96.29% (Warwick-QU).
  • The integrated feature set demonstrated superior classification accuracy compared to individual descriptors.

Conclusions:

  • The proposed Shearlet domain method is effective for classifying histopathological images.
  • The approach demonstrated robustness across datasets with varying complexity.
  • This automated classification method shows significant potential for cancer diagnosis and staging.