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Automatic classification of atypical lymphoid B cells using digital blood image processing.

S Alférez1, A Merino, L E Mujica

  • 1Universitat Politècnica de Catalunya, Barcelona, Spain.

International Journal of Laboratory Hematology
|December 12, 2013
PubMed
Summary

This study introduces a new method for automatically classifying abnormal lymphoid cells in peripheral blood (PB) samples. The approach successfully distinguishes between hairy cell leukemia (HCL), chronic lymphocytic leukemia (CLL), and normal lymphocytes (N) with high accuracy.

Keywords:
Atypical lymphoid cellsautomatic cell classificationdigital image processinghematological cytologymorphological analysisperipheral blood

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Area of Science:

  • Hematology
  • Computational Biology
  • Medical Image Analysis

Background:

  • Automated peripheral blood (PB) cell analysis systems are less effective with pathological samples.
  • Accurate classification of abnormal lymphoid cells is crucial for diagnosing hematological malignancies.

Purpose of the Study:

  • To develop an improved methodology for the automatic classification of abnormal lymphoid cells in digital PB images.
  • To enhance the diagnostic capabilities of automated cell analysis systems for leukemia.

Main Methods:

  • Analysis of 340 digital images of lymphoid cells: 150 chronic lymphocytic leukemia (CLL), 100 hairy cell leukemia (HCL), and 90 normal lymphocytes (N).
  • Implementation of Watershed Transformation for cell segmentation (nucleus, cytoplasm, peripheral region).
  • Extraction of 44 features followed by a two-step Fuzzy C-Means (FCM) clustering algorithm for classification.

Main Results:

  • Automatic clustering yielded three groups, with 98% of HCL cells correctly identified in one group.
  • Subsequent FCM clustering using texture features classified 83.3% of normal lymphocytes (N) and 71.3% of CLL cells into separate groups.
  • The methodology demonstrated high precision in distinguishing between the three cell types.

Conclusions:

  • The developed approach effectively classifies three types of lymphoid cells automatically.
  • Further enhancements with additional descriptors and classification techniques could extend this methodology to other atypical lymphoid cell types.
  • This automated classification method holds promise for improving the diagnosis of hematological disorders.