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Medical & Biological Engineering & Computing

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07.02.2019 | Original Article

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

verfasst von: Santiago Alférez, Anna Merino, Andrea Acevedo, Laura Puigví, José Rodellar

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 6/2019

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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.

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Titel: Color clustering segmentation framework for image analysis of malignant lymphoid cells in peripheral blood
verfasst von: Santiago Alférez
Anna Merino
Andrea Acevedo
Laura Puigví
José Rodellar
Publikationsdatum: 07.02.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Medical & Biological Engineering & Computing / Ausgabe 6/2019
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-019-01954-7

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