White blood cell counting analysis of blood smear images using various segmentation strategies

Safuan, Syadia Nabilah Mohd; Tomari, Razali; Zakaria, Wan Nurshazwani Wan; Othman, Norzila

doi:10.1063/1.5002036

Cited by 16 publications

(10 citation statements)

References 7 publications

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“…At present, there are many computer-aided methods for the classifcation and counting of white blood cells. Early computer-aided methods were mainly based on morphology, in which the shape, color, and other characteristics of white blood cells are artifcially analyzed, and morphological processing is used to separate white blood cells from the background to achieve the purpose of classifcation, such as the method proposed in the literature [13][14][15][16]. With the development of machine learning technology, some machine learning-based white blood cell classifcation methods have emerged.…”

Section: Introductionmentioning

confidence: 99%

A Method for Expanding the Training Set of White Blood Cell Images

Yang

Zang

et al. 2022

Journal of Healthcare Engineering

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In medicine, the count of different types of white blood cells can be used as the basis for diagnosing certain diseases or evaluating the treatment effects of diseases. The recognition and counting of white blood cells have important clinical significance. But the effect of recognition based on machine learning is affected by the size of the training set. At present, researchers mainly rely on image rotation and cropping to expand the dataset. These methods either add features to the white blood cell image or require manual intervention and are inefficient. In this paper, a method for expanding the training set of white blood cell images is proposed. After rotating the image at any angle, Canny is used to extract the edge of the black area caused by the rotation and then fill the black area to achieve the purpose of expanding the training set. The experimental results show that after using the method proposed in this paper to expand the training set to train the three models of ResNet, MobileNet, and ShuffleNet, and comparing the original dataset and the method trained by the simple rotated image expanded dataset, the recognition accuracy of the three models is obviously improved without manual intervention.

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Section: Introductionmentioning

confidence: 99%

A Method for Expanding the Training Set of White Blood Cell Images

Yang

Zang

et al. 2022

Journal of Healthcare Engineering

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“…To date, many image‐processing algorithms have been developed to discern the nucleus from a white blood cell. Otsu's threshold involves segmenting the nucleus based on red, green and blue (RCB), cyan, magenta, yellow, and black (CMYK), and hue, saturation and value (HSV) color spaces 1 ; a skill of thresholding is to solve segmentation assignment 2 . Li et al 3 made use of adaptive binarization to modify the accuracy of segmentation in a white blood cell.…”

Section: Introductionmentioning

confidence: 99%

Hybrid adversarial‐discriminative network for leukocyte classification in leukemia

Zhang

Lu³

et al. 2020

Medical Physics

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Purpose: Leukemia is a lethal disease that is harmful to bone marrow and overall blood health. The classification of white blood cell images is crucial for leukemia diagnosis. The purpose of this study is to classify white blood cells by extracting discriminative information from cell segmentation and combining it with the fine-grained features. We propose a hybrid adversarial residual network with support vector machine (SVM), which utilizes the extracted features to improve the classification accuracy for human peripheral white cells. Methods: Firstly, we segment the cell and nucleus by utilizing an adversarial residual network, which contains a segmentation network and a discriminator network. To extract features that can handle the inter-class consistency problem effectively, we introduce the adversarial residual network. Then, we utilize convolutional neural network (CNN) features and histogram of oriented gradient (HOG) features, which can extract discriminative features from images of segmented cell nuclei. To utilize the representative features fully, a discriminative network is introduced to deal with neighboring information at different scales. Finally, we combine the vectors of HOG features with those of CNN features and feed them into a linear SVM to classify white blood cells into six types. Results: We used three methods to evaluate the effect of leukocyte classification based on 5000 leukocyte images acquired from a local hospital. The first approach is to use the CNN features as the input of SVM to classify leukocytes, which achieved 94.23% specificity, 95.10% sensitivity, and 94.41% accuracy. The use of the HOG features for SVM achieved 83.50% specificity, 87.50% sensitivity, and 85.00% accuracy. The use of combined CNN and HOG features achieved 94.57% specificity, 96.11% sensitivity, and 95.93% accuracy. Conclusions: We propose a novel hybrid adversarial-discriminative network for the classification of microscopic leukocyte images. It improves the accuracy of cell classification, reduces the difficulty and time pressure of doctors' work, and economizes the valuable time of doctors in daily clinical diagnosis.

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“…In (1) the preprocessing is done by standardizing the color space, and then the segmentation is performed by applying color component subtraction. Color component subtraction is experimented on RGB, CMYK, and HSV color spaces.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm is tested with 30 blood smear images and yields 93 per cent accuracy. In (1,6) the different types of WBCs are segmented amongst RBCs using Otsu's thresholding along with mathematical morphology and watershed transform, extracted geometrical features which define the shapes of WBC and then classified them by applying SVM in hierarchical stages. In their work, the erythrocytes are distinguished using histogram equalization.…”

Section: Introductionmentioning

confidence: 99%

Bi-Level algorithm for the segmentation and counting of Leukocytes and Erythrocytes

Umamaheswari¹

2020

IJST

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Background/Objectives: To present an accurate quantitative approach based two-phase algorithm to count both the leukocytes and erythrocytes for identifying the severity of leukaemia in the human body. Methods/Statistical analysis: The algorithm is having two-phases with the first phase meant for recognizing and counting the leukocytes using the thresholding based segmentation technique that focuses on the intensity values of pixels of the greyscale blood smear images; whereas the second phase recognizes the erythrocytes by their circular shape using Circular Hough Transform (CHT) method. The system experiments with 26 stained blood smear images from the ALL-IDB1 benchmark dataset. Findings: The first phase of the algorithm achieves 99.41 per cent overall accuracy in leukocytes detection and in the second phase 99.76 per cent overall accuracy is attained in erythrocytes detection. Novelty/Applications: This proposal applies Circular Hough Transform in detecting the erythrocytes by adjusting the radius of the circle according to the magnification rate of the sample image.

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White blood cell counting analysis of blood smear images using various segmentation strategies

Cited by 16 publications

References 7 publications

A Method for Expanding the Training Set of White Blood Cell Images

A Method for Expanding the Training Set of White Blood Cell Images

Hybrid adversarial‐discriminative network for leukocyte classification in leukemia

Bi-Level algorithm for the segmentation and counting of Leukocytes and Erythrocytes

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