Bone Marrow Cells Detection: A Technique for the Microscopic Image Analysis.
In the detection of myeloproliferative, the number of cells in each type of bone marrow cells (BMC) is an important parameter for the evaluation. In this study, we propose a new counting method, which consists of three modules including localization, segmentation and classification. The localization...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 4 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=135714984&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135714984 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Apr2019 vid: 43 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135714984 135714984 135714984 10.1007/s10916-019-1185-9 135714984 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Bone Marrow Cells Detection: A Technique for the Microscopic Image Analysis. aug: au: Liu, Hong Cao, Haichao Song, Enmin affil: School of Computer Science & Technology, Huazhong University of Science and Technology, Wuhan Shi, China sug: subj: Bone Marrow Anatomy and Histology Cells Classification Image Processing, Computer Assisted Microscopy Utilization Immunohistochemistry Cytological Techniques Cell Movement Neutrophils Anatomy and Histology Lymphocytes Anatomy and Histology Granulocytes Anatomy and Histology Erythrocytes Anatomy and Histology Cytoplasm Cell Nucleus Bone Marrow Pathology Cell Count Monocytes Anatomy and Histology China Funding Source ab: In the detection of myeloproliferative, the number of cells in each type of bone marrow cells (BMC) is an important parameter for the evaluation. In this study, we propose a new counting method, which consists of three modules including localization, segmentation and classification. The localization of BMC is achieved from a color transformation enhanced BMC sample image and stepwise averaging method. In the nucleus segmentation, both stepwise averaging method and Otsu's method are applied to obtain a weighted threshold for segmenting the patch into nucleus and non-nucleus. In the cytoplasm segmentation, a color weakening transformation, an improved region growing method and the K-Means algorithm are employed. The connected cells with BMC will be separated by the marker-controlled watershed algorithm. The features will be extracted for the classification after the segmentation. In this study, the BMC are classified using the support vector machine into five classes; namely, neutrophilic split granulocyte, neutrophilic stab granulocyte, metarubricyte, mature lymphocytes and the outlier (all other cells not listed). Experimental results show that the proposed method achieves superior segmentation and classification performance with an average segmentation accuracy of 91.76% and an average recall rate of 87.49%. The comparison shows that the proposed segmentation and classification methods outperform the existing methods. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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