ArticleJournal of medical systems2018
Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images.
Article in Journal of medical systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- An Artificial Intelligence Framework for Detection of Leukemia with Comparison Between Segmented and Non-Segmented Images.Sensors (Basel, Switzerland) · 2026Article
- Advancing WBC Classification: A Hybrid ConvNextV2-Swin Transformer Framework with R3GAN Data Balancing and CLAHE Preprocessing.Journal of imaging informatics in medicine · 2026Article
- The Application Value of Flow Cytometry-Based Peripheral Blood Leukocyte Classification in Screening for Acute Leukemia and Posttreatment Monitoring.Journal of clinical laboratory analysis · 2026Article
- Effectiveness and user satisfaction of an online cell morphology self-study and self-test platform on blood smear diagnostic skills.BMC medical education · 2025Article
- AneRBC dataset: a benchmark dataset for computer-aided anemia diagnosis using RBC images.Database : the journal of biological databases and curation · 2024Article
- CAM: a novel aid system to analyse the coloration quality of thick blood smears using image processing and machine learning techniques.Malaria journal · 2024Article
- A fine-grained image classification algorithm based on self-supervised learning and multi-feature fusion of blood cells.Scientific reports · 2024Article
- Blood cell image segmentation and classification: a systematic review.PeerJ. Computer science · 2024Article
- An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological Disorder.Life (Basel, Switzerland) · 2023Article
- Machine Learning-Based Detection of Dengue from Blood Smear Images Utilizing Platelet and Lymphocyte Characteristics.Diagnostics (Basel, Switzerland) · 2023Article
- WBC image classification and generative models based on convolutional neural network.BMC medical imaging · 2022Article
- Examination of blood samples using deep learning and mobile microscopy.BMC bioinformatics · 2022Article
- A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm.Scientific reports · 2022Observational
- Systematic segmentation method based on PCA of image hue features for white blood cell counting.PloS one · 2021Article
- Leukocyte segmentation in peripheral blood images using a novel edge strength cue-based location detection method.Medical & biological engineering & computing · 2020Article
- Automated Decision Support System for Detection of Leukemia from Peripheral Blood Smear Images.Journal of digital imaging · 2020Article
- Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Images.Computational and mathematical methods in medicine · 2020Article
- Study on Contribution of Biological Interpretable and Computer-Aided Features Towards the Classification of Childhood Medulloblastoma Cells.Journal of medical systems · 2018Article
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4 authors.
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Abstract
Peripheral Blood Smear analysis plays a vital role in diagnosis of many diseases such as leukemia, anemia, malaria, lymphoma and infections. Unusual variations in color, shape and size of blood cells indicate abnormal condition. We used a total of 117 images from Leishman stained peripheral blood smears acquired at a magnification of 100X. In this paper we present a robust image processing algorithm for detection of nuclei and classification of white blood cells based on features of the nuclei. We used novel image enhancement method to manage illumination variations and TissueQuant method to manage color variations for the detection of nuclei. Dice similarity coefficient of 0.95 was obtained for nucleus detection. We also compared the proposed method with a state-of-the-art method and the proposed method was found to be better. Shape and texture features of the detected nuclei were used for classifying white blood cells. We considered classification of WBCs using two approaches such as 5-class and cell-by-cell approaches using neural network and hybrid-classifier respectively. We compared the results of both the approaches for classification of white blood cells. Cell-by-cell approach offered 1.4% higher sensitivity in comparison with the 5-class approach. We obtained an accuracy of 100% for lymphocyte and basophil detection. Hence, we conclude that lymphocytes and basophils can be accurately detected even when the analysis is limited to the features of nuclei whereas, accurate detection of other types of WBCs will require analysis of the cytoplasm too.
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