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Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach.

Humayun Irshad1, Sepehr Jalali, Ludovic Roux

  • 1University of Joseph Fourier, Grenoble, France.

Journal of Pathology Informatics
|June 15, 2013
PubMed
Summary

Automated mitosis detection in breast cancer histopathology aids prognosis by overcoming manual counting variability. This study explores texture features and a biologically inspired model for accurate mitosis identification.

Keywords:
ClassificationHierarchical Model and XScale-invariant feature transformhistopathologymitosis detectiontexture analysis

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Area of Science:

  • Computational pathology
  • Medical image analysis
  • Machine learning in oncology

Background:

  • Mitosis counting in breast cancer histopathology is crucial for grading and prognosis per the Nottingham system.
  • Manual mitosis counting is time-consuming and prone to significant reader variability.

Purpose of the Study:

  • To investigate texture features and a biologically inspired Hierarchical Model and X (HMAX) approach for automated mitosis detection.
  • To develop a machine learning-based system to assist pathologists in mitosis counting.

Main Methods:

  • Proposed an automated mitosis detection framework utilizing optimal texture features and color space analysis (blue-ratio channel).
  • Extracted co-occurrence, run-length, and Scale-Invariant Feature Transform (SIFT) features for classification.
  • Evaluated Decision Tree, linear SVM, and non-linear SVM classifiers, comparing with a modified HMAX model and dense SIFT.

Main Results:

  • The proposed framework was tested on the MITOS dataset for breast cancer histological images.
  • Achieved a recall of 76%, precision of 75%, and F-measure of 76% in mitosis detection.

Conclusions:

  • Evaluated various classification frameworks for mitosis detection, demonstrating the potential of texture and HMAX-based approaches.
  • Future work will focus on computing features from mitosis contour segmentation to enhance detection accuracy.