New Segmentation and Feature Extraction Algorithm for the Classification of White Blood Cells in Peripheral Smear Images

This article addresses a new method for 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.

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New Segmentation and Feature Extraction Algorithm for Classification of White Blood Cells in Peripheral Smear Images

10.21203/rs.3.rs-496778/v1 ◽

2021 ◽

Author(s):

Eslam Tavakoli ◽

Ali Ghaffari ◽

Zahra Mousavi Kouzehkanan ◽

Reshad Hosseini

Keyword(s):

Blood Cells ◽

White Blood Cells ◽

Dice Similarity Coefficient ◽

Second Phase ◽

Peripheral Smear ◽

Color Features ◽

Svm Model ◽

Extraction Algorithm ◽

Processing Techniques

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 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 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. 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. The obtained results demonstrate that the proposed method is robust, fast, and accurate.

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

Scientific Reports ◽

10.1038/s41598-021-98599-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Sajad Tavakoli ◽

Ali Ghaffari ◽

Zahra Mousavi Kouzehkanan ◽

Reshad Hosseini

Keyword(s):

Blood Cells ◽

White Blood Cells ◽

Dice Similarity Coefficient ◽

Second Phase ◽

Peripheral Smear ◽

Color Features ◽

Svm Model ◽

Extraction Algorithm ◽

Processing Techniques

AbstractThis 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.

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Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation

IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) ◽

10.22146/ijeis.49943 ◽

2019 ◽

Vol 9 (2) ◽

pp. 173

Author(s):

Apri Nur Liyantoko ◽

Ika Candradewi ◽

Agus Harjoko

Keyword(s):

Blood Cell ◽

White Blood Cell ◽

Blood Cells ◽

White Blood Cells ◽

Back Propagation ◽

Cell Types ◽

Diagnostic Process ◽

The Subject ◽

Processing Techniques

Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.

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Automatic classification of cells in microscopic fecal images using convolutional neural networks

Bioscience Reports ◽

10.1042/bsr20182100 ◽

2019 ◽

Vol 39 (4) ◽

Cited By ~ 3

Author(s):

Xiaohui Du ◽

Lin Liu ◽

Xiangzhou Wang ◽

Guangming Ni ◽

Jing Zhang ◽

...

Keyword(s):

Blood Cells ◽

Detection Efficiency ◽

Detection System ◽

White Blood Cells ◽

Principal Component ◽

Visible Image ◽

Single Target ◽

Extraction Algorithm ◽

Detection Schemes

Abstract The analysis of fecal-type components for clinical diagnosis is important. The main examination involves the counting of red blood cells (RBCs), white blood cells (WBCs), and molds under the microscopic. With the development of machine vision, some vision-based detection schemes have been proposed. However, these methods have a single target for detection, with low detection efficiency and low accuracy. We proposed an algorithm to identify the visible image of fecal composition based on intelligent deep learning. The algorithm mainly includes region proposal and candidate recognition. In the process of segmentation, we proposed a morphology extraction algorithm in a complex background. As for the candidate recognition, we proposed a new convolutional neural network (CNN) architecture based on Inception-v3 and principal component analysis (PCA). This method achieves high-average Precision of 90.7%, which is better than the other mainstream CNN models. Finally, the images within the rectangle marks were obtained. The total time for detection of an image was roughly 1200 ms. The algorithm proposed in the present paper can be integrated into an automatic fecal detection system.

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Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM

SN Applied Sciences ◽

10.1007/s42452-021-04485-9 ◽

2021 ◽

Vol 3 (4) ◽

Author(s):

Ahmet Çınar ◽

Seda Arslan Tuncer

Keyword(s):

Blood Cells ◽

Feature Vector ◽

White Blood Cells ◽

Structural Features ◽

The Body ◽

Diagnostic Process ◽

Support Vector ◽

Data Sets ◽

Svm Model

AbstractWhite blood cells (WBC), which form the basis of the immune system, protect the body from foreign invaders and infectious diseases. While the number and structural features of WBCs can provide important information about the health of people, the ratio of the subtypes of these cells and observable deformations are a good indicator in the diagnostic process. The recognition of cells of the type of lymphocytes, neutrophils, eosinophils, basophils and monocytes is critical. In this article, Deep Learning based Hybrid CNN (Convololutional Neural Network) model is proposed for classification of eosinophils, lymphocytes, monocytes, and neutrophils WBCs. The model presented is based on pretrained Alexnet and Googlenet architectures. The feature vector in the last pooling layer of both CNN architectures has been merged, and the resulting feature vector is classified by the Support Vector Machine. To determine the superiority of the proposed method, the classification was also performed and compared using pretrained Alexnet and Googlenet. Hybrid Alexnet-Googlenet-SVM model provides higher accuracy than pretrained Alexnet and Googlenet. The proposed method has been tested with WBC images from Kaggle and LISC database. Accuracy and F1-score were 99.73%, 0.99 and 98.23%, 0.98 for both data sets, respectively.

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WBC-CNN: Efficient CNN-Based Models to Classify White Blood Cells Subtypes

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v17i13.27373 ◽

2021 ◽

Vol 17 (13) ◽

pp. 135-150

Author(s):

Najla Alofi ◽

Wafa Alonezi ◽

Wedad Alawad

Keyword(s):

Feature Extraction ◽

Blood Cells ◽

Classification Accuracy ◽

White Blood Cells ◽

Hybrid Models ◽

Learning Technologies ◽

Support Vector ◽

Svm Model ◽

Vector Machines

Blood is essential to life. The number of blood cells plays a significant role in observing an individual’s health status. Having a lower or higher number of blood cells than normal may be a sign of various diseases. Thus it is important to precisely classify blood cells and count them to diagnose different health conditions. In this paper, we focused on classifying white blood cells subtypes (WBC) which are the basic parts of the immune system. Classification of WBC subtypes is very useful for diagnosing diseases, infections, and disorders. Deep learning technologies have the potential to enhance the process and results of WBC classification. This study presented two fine-tuned CNN models and four hybrid CNN-based models to classify WBC. The VGG-16 and MobileNet are the CNN architectures used for both feature extraction and classification in fine-tuned models. The same CNN architectures are used for feature extraction in hybrid models; however, the Support Vector Machines (SVM) and the Quadratic Discriminant Analysis (QDA) are the classifiers used for classification. Among all models, the fine-tuned VGG-16 performs best, its classification accuracy is 99.81%. Our hybrid models are efficient in detecting WBC as well. 98.44% is the classification accuracy of the VGG-16+SVM model, and 98.19% is the accuracy of the MobileNet+SVM.

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Classification of White Blood Cells Based on Cell Diameter, Specific Membrane Capacitance and Cytoplasmic Conductivity Leveraging Microfluidic Constriction Channel

2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers) ◽

10.1109/transducers50396.2021.9495490 ◽

2021 ◽

Author(s):

Huiwen Tan ◽

Minruihong Wang ◽

Yi Zhang ◽

Xukun Huang ◽

Deyong Chen ◽

...

Keyword(s):

Blood Cells ◽

White Blood Cells ◽

Membrane Capacitance ◽

Cell Diameter ◽

Specific Membrane

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Segmentation of Leukemia Cells Using Clustering

International Journal of Synthetic Emotions ◽

10.4018/ijse.2019070103 ◽

2019 ◽

Vol 10 (2) ◽

pp. 39-48

Author(s):

Eman Mostafa ◽

Heba A. Tag El-Dien

Keyword(s):

Bone Marrow ◽

Cancer Cells ◽

Blood Cells ◽

White Blood Cells ◽

Leukemia Cells ◽

Microscopic Image ◽

Processing Stage ◽

Clustering Techniques ◽

Fuzzy C Means

Leukemia is a blood cancer which is defined as an irregular augment of undeveloped white blood cells called “blasts.” It develops in the bone marrow, which is responsible for blood cell generation including leukocytes and white blood cells. The early diagnosis of leukemia greatly helps in the treatment. Accordingly, researchers are interested in developing advanced and accurate automated techniques for localizing such abnormal blood cells. Subsequently, image segmentation becomes an important image processing stage for successful feature extraction and classification of leukemia in further stages. It aims to separate cancer cells by segmenting the microscopic image into background and cancer cells that are known as the region of interested (ROI). In this article, the cancer blood cells were segmented using two separated clustering techniques, namely the K-means and Fuzzy-c-means techniques. Then, the results of these techniques were compared to in terms of different segmentation metrics, such as the Dice, Jac, specificity, sensitivity, and accuracy. The results proved that the k-means provided better performance in leukemia blood cells segmentation as it achieved an accuracy of 99.8% compared to 99.6% with the fuzzy c-means.

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