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Related Experiment Video

Updated: Jun 10, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Automatic classification of lymphoma images with transform-based global features.

Nikita V Orlov1, Wayne W Chen, David Mark Eckley

  • 1National Institute on Aging, NIH, Baltimore, MD 21224, USA. norlov@nih.gov

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|July 28, 2010
PubMed
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This study introduces an automated method for classifying three lymphoma types using computer vision. The approach achieved high accuracy (98%-99%) in distinguishing malignant lymphoma subtypes from H&E stained images.

Area of Science:

  • Computational pathology
  • Medical image analysis
  • Oncology

Background:

  • Accurate classification of malignant lymphomas is crucial for effective treatment.
  • Distinguishing between chronic lymphocytic leukemia, follicular lymphoma, and mantle cell lymphoma presents diagnostic challenges.

Purpose of the Study:

  • To develop an automated system for classifying three common malignant lymphoma types.
  • To identify image-based patterns indicative of lymphoma malignancy and subtype.

Main Methods:

  • A two-stage computer vision approach was utilized for quantitative image characterization.
  • Spectral planes were generated using Fourier, Chebyshev, and wavelet transforms.
  • Multipurpose global features were extracted and fused into a single vector for classification.

Related Experiment Videos

Last Updated: Jun 10, 2026

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Main Results:

  • Experiments were conducted across various color spaces, including RGB, grayscale, CIE-L*a*b*, and H&E specific spaces.
  • The best classification performance (98%-99% accuracy on unseen images) was achieved using the HE, H, and E channels of the H&E dataset.

Conclusions:

  • The proposed computer vision method demonstrates high efficacy in the automatic classification of malignant lymphoma subtypes.
  • This technique holds promise for improving diagnostic accuracy and efficiency in hematopathology.