Segmentation of White Blood Cell, Nucleus and Cytoplasm in Digital Haematology Microscope Images: A Review–Challenges, Current and Future Potential Techniques

Al-Dulaimi, Khamael; Banks, Jasmine; Nugyen, Kien; Al-Sabaawi, Aiman; Tomeo-Reyes, Inmaculada; Chandran, Vinod

doi:10.1109/rbme.2020.3004639

Cited by 38 publications

(40 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second group of features is color characteristics. According to the experience of hematologists, in addition to the shape features of the nucleus, the color features of the nucleus and the cytoplasm can also provide us with useful information about the type of WBC [11]. In this research, four novel color features by means of nucleus region, convex hull region, and ROC region are designed as follows:…”

Section: Nucleus Segmentationmentioning

confidence: 99%

“…In machine learning-based methods, at first, it is a requisite to collect the appropriate dataset taking quality, variety, and size into account. Yet, the lack of such dataset with aforementioned properties is the major challenge [11]. A plethora of articles have used small datasets collected using only one microscope or one camera [7,12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Shape characteristics such as circularity, convexity, solidity are meaningful features for the nucleus. This is due to the fact that lymphocytes and monocytes are mononuclear, and the shape of their nucleus is circular and ellipsoidal, respectively [11]. On the other hand, the nucleus of neutrophil and eosinophil is multi-lobed [11] and non-solid.…”

Section: Introductionmentioning

confidence: 99%

“…This is due to the fact that lymphocytes and monocytes are mononuclear, and the shape of their nucleus is circular and ellipsoidal, respectively [11]. On the other hand, the nucleus of neutrophil and eosinophil is multi-lobed [11] and non-solid. Characteristics such as color and texture, e.g., local binary pattern (LBP) or gray level cooccurrence matrix (GLCM), are also interpretable features for the cytoplasm [11].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Tavakoli

Ghaffari

Kouzehkanan

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Nucleus Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Tavakoli

Ghaffari

Kouzehkanan

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

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

Tavakoli

Ghaffari

Kouzehkanan

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Comprehensive data analysis of white blood cells with classification and segmentation by using deep learning approaches

Özcan,

Uyar,

Karayeğen

2024

Cytometry Pt A

View full text Add to dashboard Cite

Deep learning approaches have frequently been used in the classification and segmentation of human peripheral blood cells. The common feature of previous studies was that they used more than one dataset, but used them separately. No study has been found that combines more than two datasets to use together. In classification, five types of white blood cells were identified by using a mixture of four different datasets. In segmentation, four types of white blood cells were determined, and three different neural networks, including CNN (Convolutional Neural Network), UNet and SegNet, were applied. The classification results of the presented study were compared with those of related studies. The balanced accuracy was 98.03%, and the test accuracy of the train‐independent dataset was determined to be 97.27%. For segmentation, accuracy rates of 98.9% for train‐dependent dataset and 92.82% for train‐independent dataset for the proposed CNN were obtained in both nucleus and cytoplasm detection. In the presented study, the proposed method showed that it could detect white blood cells from a train‐independent dataset with high accuracy. Additionally, it is promising as a diagnostic tool that can be used in the clinical field, with successful results in classification and segmentation.

show abstract

Segmentation of White Blood Cell, Nucleus and Cytoplasm in Digital Haematology Microscope Images: A Review–Challenges, Current and Future Potential Techniques

Cited by 38 publications

References 87 publications

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

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

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

Comprehensive data analysis of white blood cells with classification and segmentation by using deep learning approaches

Contact Info

Product

Resources

About