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

Tavakoli, Eslam; Ghaffari, Ali; Kouzehkanan, Zahra Mousavi; Hosseini, Reshad

doi:10.21203/rs.3.rs-496778/v1

Cited by 5 publications

(6 citation statements)

References 26 publications

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“…While several virtual staining techniques based on a variety of label-free imaging techniques have been presented [29][30][31][32], they are mostly geared toward the staining of tissues for histopathology and are not designed to digitally stain and analyze blood smears. Further, our segmentation method is robust and achieves comparable or even better performance than methods based on stained or pseudocolorized images, without the need for fix-ing and staining the sample [36][37][38] or the need for multispectral imaging [24]. We have presented a simple and robust classification and counting procedure that utilizes cellular and nuclear segmentation masks along with the grayscale images to first exclude dead WBCs and then classify healthy WBCs into five subtypes.…”

Section: Discussionmentioning

confidence: 99%

“…While several methods for segmentation and classification of WBCs have been proposed, most of them rely on feature extraction or training DNNs using stained images [35][36][37][38][39] or fail to provide an accurate five-part white blood cell differential [15,16,27,28,40]. Here, we present a segmentation method that uses only grayscale images (and is independent of the virtual staining branch of the pipeline) and has very high accuracy, with an average dice score of 0.9899 for cellular segmentation and 0.9718 for nuclear segmentation on an unseen test dataset.…”

Section: Introductionmentioning

confidence: 99%

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Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis

2022

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Objective and Impact Statement. We present a fully automated hematological analysis framework based on single-channel (single-wavelength), label-free deep-ultraviolet (UV) microscopy that serves as a fast, cost-effective alternative to conventional hematology analyzers. Introduction. Hematological analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel, costly chemical reagents, and lengthy protocols. Label-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler workflow. In this work, we leverage the unique capabilities of deep-UV microscopy as a label-free, molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining, segmentation, classification, and counting of white blood cells (WBCs) in single-channel images of peripheral blood smears. Methods. We train independent deep networks to virtually stain and segment grayscale images of smears. The segmented images are then used to train a classifier to yield a quantitative five-part WBC differential. Results. Our virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining, the gold standard in hematology. The trained cellular and nuclear segmentation networks achieve high accuracy, and the classifier can achieve a quantitative five-part differential on unseen test data. Conclusion. This proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple, fast, and low-cost, point-of-care hematology analyzer.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis

2022

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“…Lower pixel standard deviations are found in cell pictures with more evenly colored cytoplasm and nucleus, and vice versa. Therefore, four color features are retrieved for each cell image in this study from a total of 12 channels in four different color spaces, including RGB, HSV, LAB, and YCrCb [38].…”

Section:  mentioning

confidence: 99%

White Blood Cells Image Classification Based on Radiomics and Deep Learning

Liao

2022

IEEE Access

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White blood cell (WBC) identification performance is highly correlated with the quality of the extracted features, with radiomic features having greater resolution and more detailed information and deep features having more robust semantic information. This research integrates these two aspects to creatively suggest a WBC classification model based on radiomics and deep learning. This research suggests a brandnew method for extracting radiomic features from WBC images as well as a dual-branch feature fusion network RCTNet based on CNN and Transformer for extracting deep features. The radiomic feature extraction method not only has a simple segmentation algorithm but also solves the problem of cell adhesion, can obtain higher quality shape, color, and texture features without segmenting intact cells, and is more generalizable. RCTNet can extract more critical and recognizable deep features from WBC nuclei, avoiding the influence of too much redundant and invalid information on the results, and has better performance than several existing CNN models. We compared the classification outcomes based on radiomics with those based on deep learning to confirm the efficacy of the WBC classification model based on radiomics and deep learning suggested in this research. The experimental results demonstrated that combining radiomic features and deep features significantly improved the classification accuracy, with an AUC exceeding 0.9995, accuracy, sensitivity, specificity, precision, and F1-score reaching 0.9880, 0.9823, 0.9883, 0.9968, and 0.9881, respectively. The model has significant research significance in clinical applications and aids physicians in improving diagnosis and screening for diseases of WBCs.INDEX TERMS Radiomics, deep learning, feature fusion, dual-branch, white blood cell classification.

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“…There have been many attempts to develop CAD systems; however, they depend on the availability of a fully automated workflow. Images acquired from a manual setup suffer from illumination and staining variations [12]. In such cases, the automated systems fail to detect leukemia accurately.…”

Section: Introductionmentioning

confidence: 99%

Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

Veeraiah¹,

Alotaibi²,

Subahi³

2023

Computer Systems Science and Engineering

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An abnormality that develops in white blood cells is called leukemia. The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery. Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis. This paper proposes a Histogram Threshold Segmentation Classifier (HTsC) for a decision support system. The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images. Arithmetic operations are used to crop the nucleus based on automated approximation. White Blood Cell (WBC) segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer approach. Through entropy-adaptive mask generation, WBCs accurately detect the circularity region for identification of the nucleus. The proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation operations. Variation in WBC imaging characteristics depends on the cytoplasmic and nuclear regions. The computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear images. The classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression classifier. The designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes, monocytes, neutrophils, eosinophils, and abnormalities in the WBCs. The proposed HTsC identifies the abnormal activity in the WBC, considering the color and shape features. It exhibits a higher classification accuracy value of 99.6% when combined with the other classifiers. The comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%, which is approximately 3%-12% higher than the conventional technique.

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

Cited by 5 publications

References 26 publications

Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis

Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis

White Blood Cells Image Classification Based on Radiomics and Deep Learning

Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

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