Identification and red blood cell automated counting from blood smear images using computer-aided system.
Red blood cell count plays a vital role in identifying the overall health of the patient. Hospitals use the hemocytometer to count the blood cells. Conventional method of placing the smear under microscope and counting the cells manually lead to erroneous results, and medical laboratory technicians...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 3; pp. 483 - 490 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2018
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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=128292225&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128292225 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2018 vid: 56 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 128292225 128292225 NLM28815426 128292225 10.1007/s11517-017-1708-9 NLM28815426 128292225 ppf: 483 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identification and red blood cell automated counting from blood smear images using computer-aided system. aug: au: Acharya, Vasundhara Kumar, Preetham affil: Department of ICT, MIT, Manipal University, Manipal, India sug: subj: Erythrocytes Image Processing, Computer Assisted Erythrocyte Count Methods Automation Algorithms Human ab: Red blood cell count plays a vital role in identifying the overall health of the patient. Hospitals use the hemocytometer to count the blood cells. Conventional method of placing the smear under microscope and counting the cells manually lead to erroneous results, and medical laboratory technicians are put under stress. A computer-aided system will help to attain precise results in less amount of time. This research work proposes an image-processing technique for counting the number of red blood cells. It aims to examine and process the blood smear image, in order to support the counting of red blood cells and identify the number of normal and abnormal cells in the image automatically. K-medoids algorithm which is robust to external noise is used to extract the WBCs from the image. Granulometric analysis is used to separate the red blood cells from the white blood cells. The red blood cells obtained are counted using the labeling algorithm and circular Hough transform. The radius range for the circle-drawing algorithm is estimated by computing the distance of the pixels from the boundary which automates the entire algorithm. A comparison is done between the counts obtained using the labeling algorithm and circular Hough transform. Results of the work showed that circular Hough transform was more accurate in counting the red blood cells than the labeling algorithm as it was successful in identifying even the overlapping cells. The work also intends to compare the results of cell count done using the proposed methodology and manual approach. The work is designed to address all the drawbacks of the previous research work. The research work can be extended to extract various texture and shape features of abnormal cells identified so that diseases like anemia of inflammation and chronic disease can be detected at the earliest. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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