Plasma cell identification based on evidential segmentation and supervised learning
by Ismahan Baghli; Mourtada Benazzouz; Mohamed Amine Chikh
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 32, No. 4, 2020

Abstract: Myeloma disease is among the most common type of cancer, it is characterised by proliferation of plasma cells, kind of white blood cell (WBC). Early diagnosis of the disease can improve the patient's survival rate. The manual diagnosis involves clinicians to visually examine microscopic bone marrow images for any signs of cells proliferation. This step is often laborious and can be highly subjective due to clinician's expertise. Automatic system based on WBC identification and counting provides more accurate result than manual method. This system is mainly based on three major steps: cell's segmentation, cell's characterisation and cell's classification. In the proposed system, microscopic images of bone marrow blood are segmented by the combination of watershed transform and the evidence theory, the segmented cells are characterised with shape and colour texture features, and then classified into plasma cells or not plasma cells with three supervised classifiers; support vector machines, K-nearest neighbour and decision tree. Experimental results show that the recognition of plasma cells with the K-nearest neighbour achieved 97% of correct rate with 100% of specificity.

Online publication date: Mon, 11-May-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com