ArticleJournal of clinical laboratory analysis2018
Quantitative distinction of the morphological characteristic of erythrocyte precursor cells with texture analysis using gray level co-occurrence matrix.
Article in Journal of clinical laboratory analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial intelligence of digital morphology analyzers improves the efficiency of manual leukocyte differentiation of peripheral blood.BMC medical informatics and decision making · 2023Article
- Evaluation of two semi-supervised learning methods and their combination for automatic classification of bone marrow cells.Scientific reports · 2022Article
- Value of conventional magnetic resonance imaging texture analysis in the differential diagnosis of benign and borderline/malignant phyllodes tumors of the breast.Cancer imaging : the official publication of the International Cancer Imaging Society · 2021Article
- Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network.Journal of biomedical optics · 2020Article
- Quantitative distinction of the morphological characteristic of erythrocyte precursor cells with texture analysis using gray level co-occurrence matrix.Journal of clinical laboratory analysis · 2018Article
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13 authors.
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Abstract
backgroundMorphological characteristics of blood cells are still qualitatively defined. So a texture analysis (Tx) method using gray level co-occurrence matrices (GLCMs; CM-Tx method) was applied to images of erythrocyte precursor cells (EPCs) for quantitatively distinguishing four types of EPC stages: proerythroblast, basophilic erythroblast, polychromatic erythroblast, and orthochromatic erythroblast.
methodsFifty-five images of four types of EPCs were downloaded from an atlas uploaded by the Blood Cell Morphology Standardization Subcommittee (BCMSS) of the Japanese Society of Laboratory Hematology (JSLH). Using in-house programs, two types of GLCMs-(R: d=1, θ=0°) and (U: d=1, θ=270°)-and nine types of texture distinction index (TDI) were calculated with images removed outer part of cell.
resultsThree binary decision trees were sequentially divided among four types of EPC with the sum average of GLCM (U), the contrast of GLCM (R), and the sum average of GLCM (U). The average concordance rate (sensitivity) of CM-Tx method with the judgments of eleven experts in the BCMSS of the JSLH was 95.8% (87.5-100.0), and the average specificity was 97.6% (92.5-100.0).
conclusionsThe CM-Tx method is an effective tool for quantitative distinction of EPC with their morphological features.
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