New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images

Abstract 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 a...

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Autores principales:	Sajad Tavakoli, Ali Ghaffari, Zahra Mousavi Kouzehkanan, Reshad Hosseini
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ee7dcef46f894a8c81da499e0aaa5b0c
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spelling	oai:doaj.org-article:ee7dcef46f894a8c81da499e0aaa5b0c2021-12-02T19:16:47ZNew segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images10.1038/s41598-021-98599-02045-2322https://doaj.org/article/ee7dcef46f894a8c81da499e0aaa5b0c2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98599-0https://doaj.org/toc/2045-2322Abstract 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 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 shapes 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 94.65%, 92.21%, and 94.20%, respectively. Besides, we show that the proposed method possesses more generalization power than pre-trained CNN models. It is worth mentioning that the hyperparameters of the classifier are fixed only with the Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets.Sajad TavakoliAli GhaffariZahra Mousavi KouzehkananReshad HosseiniNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sajad Tavakoli Ali Ghaffari Zahra Mousavi Kouzehkanan Reshad Hosseini New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images
description	Abstract 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 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 shapes 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 94.65%, 92.21%, and 94.20%, respectively. Besides, we show that the proposed method possesses more generalization power than pre-trained CNN models. It is worth mentioning that the hyperparameters of the classifier are fixed only with the Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets.
format	article
author	Sajad Tavakoli Ali Ghaffari Zahra Mousavi Kouzehkanan Reshad Hosseini
author_facet	Sajad Tavakoli Ali Ghaffari Zahra Mousavi Kouzehkanan Reshad Hosseini
author_sort	Sajad Tavakoli
title	New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images
title_short	New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images
title_full	New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images
title_fullStr	New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images
title_full_unstemmed	New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images
title_sort	new segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ee7dcef46f894a8c81da499e0aaa5b0c
work_keys_str_mv	AT sajadtavakoli newsegmentationandfeatureextractionalgorithmforclassificationofwhitebloodcellsinperipheralsmearimages AT alighaffari newsegmentationandfeatureextractionalgorithmforclassificationofwhitebloodcellsinperipheralsmearimages AT zahramousavikouzehkanan newsegmentationandfeatureextractionalgorithmforclassificationofwhitebloodcellsinperipheralsmearimages AT reshadhosseini newsegmentationandfeatureextractionalgorithmforclassificationofwhitebloodcellsinperipheralsmearimages
_version_	1718376946905120768

New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images

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