RT Journal Article
SR Electronic
T1 New Segmentation and Feature Extraction Algorithm for the Classification of White Blood Cells in Peripheral Smear Images
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.04.29.441751
DO 10.1101/2021.04.29.441751
A1 Eslam Tavakoli
A1 Ali Ghaffari
A1 Seyedeh-Zahra Mousavi Kouzehkanan
A1 Reshad Hosseini
YR 2021
UL http://biorxiv.org/content/early/2021/04/30/2021.04.29.441751.abstract
AB This article addresses a new method for the classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it which is located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shape and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.47 %, 92.21 %, and 94.20%, respectively. It is worth mentioning that the hyperparameters of the classifier are fixed only with Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets. The obtained results demonstrate that the proposed method is robust, fast, and accurate.Competing Interest StatementThe authors have declared no competing interest.