WBC-based segmentation and classification on microscopic images: a minor improvement
Introduction: White blood cells (WBCs) are immunity cells which fight against viruses and bacteria in the human body. Microscope images of captured WBCs for processing and analysis are important to interpret the body condition. At present, there is no robust automated method to segment and classify...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
F1000 Research Ltd
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/93574/ http://psasir.upm.edu.my/id/eprint/93574/1/93574.pdf |
| _version_ | 1848861873058873344 |
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| author | Lam, Xin-Hui Ng, Kok-Why Yoong, Yih-Jian Ng, Seng-Beng |
| author_facet | Lam, Xin-Hui Ng, Kok-Why Yoong, Yih-Jian Ng, Seng-Beng |
| author_sort | Lam, Xin-Hui |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Introduction: White blood cells (WBCs) are immunity cells which fight against viruses and bacteria in the human body. Microscope images of captured WBCs for processing and analysis are important to interpret the body condition. At present, there is no robust automated method to segment and classify WBCs images with high accuracy. This paper aims to improve on WBCs image segmentation and classification method.
Methods: A triple thresholding method was proposed to segment the WBCs; meanwhile, a convolutional neural network (CNN)-based binary classification model that adopts transfer learning technique was proposed to detect and classify WBCs as a healthy or a malignant. The input dataset of this research work is the Acute Lymphoblastic Leukemia Image Database (ALL-IDB). The process first converts the captured microscope images into HSV format for obtaining the H component. Otsu thresholding is applied to segment the WBC area. A 13 × 13 kernel with two iterations was used to apply morphological opening on image to ameliorate output results. Collected cell masks were used to detect the contour of each cell on the original image. To classify WBCs into a healthy or a malignant category, characteristics and conditions of WBCs are to be examined. A transfer learning technique and pre-trained InceptionV3 model were employed to extract the features from the images for classification.
Results: The proposed WBCs segmentation method yields 90.45% accuracy, 83.81% of the structural similarity index, 76.25% of the dice similarity coefficient, and is computationally efficient. The accuracy of fine-tuned classifier model for training, validation and test sets are 93.27%, 92.31% and 96.15% respectively. The obtained results are high in accuracy and precision are over 96% and with lower loss value.
Discussion: Triple thresholding outperforms K-means clustering in segmenting smaller dataset. Pre-trained InceptionV3 model and transfer learning improve the flexibility and ability of classifier. |
| first_indexed | 2025-11-15T13:08:02Z |
| format | Article |
| id | upm-93574 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T13:08:02Z |
| publishDate | 2021 |
| publisher | F1000 Research Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-935742025-06-23T06:39:27Z http://psasir.upm.edu.my/id/eprint/93574/ WBC-based segmentation and classification on microscopic images: a minor improvement Lam, Xin-Hui Ng, Kok-Why Yoong, Yih-Jian Ng, Seng-Beng Introduction: White blood cells (WBCs) are immunity cells which fight against viruses and bacteria in the human body. Microscope images of captured WBCs for processing and analysis are important to interpret the body condition. At present, there is no robust automated method to segment and classify WBCs images with high accuracy. This paper aims to improve on WBCs image segmentation and classification method. Methods: A triple thresholding method was proposed to segment the WBCs; meanwhile, a convolutional neural network (CNN)-based binary classification model that adopts transfer learning technique was proposed to detect and classify WBCs as a healthy or a malignant. The input dataset of this research work is the Acute Lymphoblastic Leukemia Image Database (ALL-IDB). The process first converts the captured microscope images into HSV format for obtaining the H component. Otsu thresholding is applied to segment the WBC area. A 13 × 13 kernel with two iterations was used to apply morphological opening on image to ameliorate output results. Collected cell masks were used to detect the contour of each cell on the original image. To classify WBCs into a healthy or a malignant category, characteristics and conditions of WBCs are to be examined. A transfer learning technique and pre-trained InceptionV3 model were employed to extract the features from the images for classification. Results: The proposed WBCs segmentation method yields 90.45% accuracy, 83.81% of the structural similarity index, 76.25% of the dice similarity coefficient, and is computationally efficient. The accuracy of fine-tuned classifier model for training, validation and test sets are 93.27%, 92.31% and 96.15% respectively. The obtained results are high in accuracy and precision are over 96% and with lower loss value. Discussion: Triple thresholding outperforms K-means clustering in segmenting smaller dataset. Pre-trained InceptionV3 model and transfer learning improve the flexibility and ability of classifier. F1000 Research Ltd 2021-11 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/93574/1/93574.pdf Lam, Xin-Hui and Ng, Kok-Why and Yoong, Yih-Jian and Ng, Seng-Beng (2021) WBC-based segmentation and classification on microscopic images: a minor improvement. F1000Research, 10. art. no. 1168. pp. 1-18. ISSN 2046-1402; eISSN: 1759-796X https://f1000research.com/articles/10-1168/v1 10.12688/f1000research.73315.1 |
| spellingShingle | Lam, Xin-Hui Ng, Kok-Why Yoong, Yih-Jian Ng, Seng-Beng WBC-based segmentation and classification on microscopic images: a minor improvement |
| title | WBC-based segmentation and classification on microscopic images: a minor improvement |
| title_full | WBC-based segmentation and classification on microscopic images: a minor improvement |
| title_fullStr | WBC-based segmentation and classification on microscopic images: a minor improvement |
| title_full_unstemmed | WBC-based segmentation and classification on microscopic images: a minor improvement |
| title_short | WBC-based segmentation and classification on microscopic images: a minor improvement |
| title_sort | wbc-based segmentation and classification on microscopic images: a minor improvement |
| url | http://psasir.upm.edu.my/id/eprint/93574/ http://psasir.upm.edu.my/id/eprint/93574/ http://psasir.upm.edu.my/id/eprint/93574/ http://psasir.upm.edu.my/id/eprint/93574/1/93574.pdf |