Automatic classification of atypical lymphoid B cells using digital blood image processing. (11th December 2013)

• Record Type: Journal Article
• Authors: Alférez, S.; Merino, A.; Mujica, L. E.; Ruiz, M.; Bigorra, L.; Rodellar, J.
• Is Part Of: International journal of laboratory hematology. Volume 36:Number 4(2014:Aug.)
• Page Start: 472
• Page End: 480
• Publication Date: 2013-12-11
• Subjects: Hematology -- Periodicals; Blood -- Diseases -- Periodicals; Hematology -- Periodicals; 616.15005
• DOI: 10.1111/ijlh.12175
• Languages: English
• ISSNs: 1751-5521

Abstract

<abstract abstract-type="main" id="ijlh12175-abs-0001"> <title>Summary</title> <sec id="ijlh12175-sec-0001" sec-type="section"> <title>Introduction</title> <p>There are automated systems for digital peripheral blood (PB) cell analysis, but they operate most effectively in nonpathological blood samples. The objective of this work was to design a methodology to improve the automatic classification of abnormal lymphoid cells.</p> </sec> <sec id="ijlh12175-sec-0002" sec-type="section"> <title>Methods</title> <p>We analyzed 340 digital images of individual lymphoid cells from PB films obtained in the CellaVision DM96:150 chronic lymphocytic leukemia (CLL) cells, 100 hairy cell leukemia (HCL) cells, and 90 normal lymphocytes (N). We implemented the <italic>Watershed Transformation</italic> to segment the nucleus, the cytoplasm, and the peripheral cell region. We extracted 44 features and then the clustering Fuzzy C‐Means (FCM) was applied in two steps for the lymphocyte classification.</p> </sec> <sec id="ijlh12175-sec-0003" sec-type="section"> <title>Results</title> <p>The images were automatically clustered in three groups, one of them with 98% of the HCL cells. The set of the remaining cells was clustered again using FCM and texture features. The two new groups contained 83.3% of the N cells and 71.3% of the CLL cells, respectively.</p> </sec> <sec id="ijlh12175-sec-0004" sec-type="section"> <title>Conclusion</title> <p>The approach has been able to automatically classify with high precision three types of lymphoid cells. The addition of more descriptors and other classification techniques will allow extending the classification to other classes of atypical lymphoid cells.</p> </sec> </abstract>