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Medical & Biological Engineering & Computing

, Volume 57, Issue 6, pp 1265–1283 | Cite as

Color clustering segmentation framework for image analysis of malignant lymphoid cells in peripheral blood

  • Santiago AlférezEmail author
  • Anna Merino
  • Andrea Acevedo
  • Laura Puigví
  • José Rodellar
Original Article
  • 103 Downloads

Abstract

Current computerized image systems are able to recognize normal blood cells in peripheral blood, but fail with abnormal cells like the classes of lymphocytes associated to lymphomas. The main challenge lies in the subtle differences in morphologic characteristics among these classes, which requires a refined segmentation. A new efficient segmentation framework has been developed, which uses the image color information through fuzzy clustering of different color components and the application of the watershed transformation with markers. The final result is the separation of three regions of interest: nucleus, entire cell, and peripheral zone around the cell. Segmentation of this zone is crucial to extract a new feature to identify cells with hair-like projections. The segmentation is validated, using a database of 4758 cell images with normal, reactive lymphocytes and five types of malignant lymphoid cells from blood smears of 105 patients, in two ways: (1) the efficiency in the accurate separation of the regions of interest, which is 92.24%, and (2) the accuracy of a classification system implemented over the segmented cells, which is 91.54%. In conclusion, the proposed segmentation framework is suitable to distinguish among abnormal blood cells with subtile color and spatial similarities.

Graphical Abstract

The segmentation framework uses the image color information through fuzzy clustering of different color components and the application of the watershed transformation with markers (Top). The final result is the separation of three regions of interest: nucleus, entire cell, and peripheral zone around the cell. The procedure is also validated by the implementation of a system to automatically classify different types of abnormal blood cells (Bottom)

Keywords

Biomedical image processing Cell morphology Clustering methods Image segmentation Optical microscopy Clinical pathology 

Notes

Acknowledgements

This work was partially supported by the Spanish Ministry of Economy and Competitiveness under Grant DPI2015-64493-R (MINECO/FEDER) and by the Generalitat de Catalunya under Grant SGR-859-2014.

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Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  1. 1.Department of Mathematics, EEBETechnical University of CataloniaBarcelonaSpain
  2. 2.Biomedical Diagnostic Center in the Hospital ClinicBarcelonaSpain

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