Discrimination and quantification of live/dead rat brain cells using a non-linear segmentation model.
The automatic cell analysis method is capable of segmenting the cells and can detect the number of live/dead cells present in the body. This study proposed a novel non-linear segmentation model (NSM) for the segmentation and quantification of live/dead cells present in the body. This work also revea...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 5; pp. 1127 - 1147 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2020
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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=142943686&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142943686 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2020 vid: 58 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142943686 142943686 NLM32189205 10.1007/s11517-020-02135-7 NLM32189205 142943686 ppf: 1127 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Discrimination and quantification of live/dead rat brain cells using a non-linear segmentation model. aug: au: Sharma, Mukta Bhattacharya, Mahua affil: ABV-IIITM, Gwalior, India sug: ab: The automatic cell analysis method is capable of segmenting the cells and can detect the number of live/dead cells present in the body. This study proposed a novel non-linear segmentation model (NSM) for the segmentation and quantification of live/dead cells present in the body. This work also reveals the aspects of electromagnetic radiation on the cell body. The bright images of the hippocampal CA3 region of the rat brain under the resolution of 60 × objective are used to analyze the effects called NISSL-stained dataset. The proposed non-linear segmentation model segments the foreground cells from the cell images based on the linear regression analysis. These foreground cells further get discriminated as live/dead cells and quantified using shape descriptors and geometric method, respectively. The proposed segmentation model is showing promising results (accuracy, 82.82%) in comparison with the existing renowned approaches. The counting analysis of live and dead cells using the proposed method is far better than the manual counts. Therefore, the proposed segmentation model and quantifying procedure is an amalgamated method for cell quantification that yields better segmentation results and provides pithy insights into the analysis of neuronal anomalies at a microscopic level. Graphical Abstract Resultant View of the overall proposed approach. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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