A New Method for Automated Identification and Morphometry of Myelinated Fibers Through Light Microscopy Image Analysis.
Nerve morphometry is known to produce relevant information for the evaluation of several phenomena, such as nerve repair, regeneration, implant, transplant, aging, and different human neuropathies. Manual morphometry is laborious, tedious, time consuming, and subject to many sources of error. Theref...
| Publicado en: | Journal of Digital Imaging Vol. 29; no. 1; pp. 63 - 73 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2016
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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=112403682&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 112403682 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2016 vid: 29 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 112403682 112403682 112403682 10.1007/s10278-015-9804-6 112403682 ppf: 63 ppct: 10 formats: fmt: @attributes: type: P tig: atl: A New Method for Automated Identification and Morphometry of Myelinated Fibers Through Light Microscopy Image Analysis. aug: au: Novas, Romulo Fazan, Valeria Felipe, Joaquim affil: Department of Computing and Mathematics, Faculty of Philosophy, Science and Languages of Ribeirão Preto, University of São Paulo at Ribeirão Preto, Ribeirão Preto Brazil sug: subj: Microscopy Methods Peripheral Nerves Anatomy and Histology Image Processing, Computer Assisted Algorithms Nerve Tissue Anatomy and Histology Prospective Studies Random Sample Pearson's Correlation Coefficient P-Value Wilcoxon Rank Sum Test T-Tests Human Comparative Studies ab: Nerve morphometry is known to produce relevant information for the evaluation of several phenomena, such as nerve repair, regeneration, implant, transplant, aging, and different human neuropathies. Manual morphometry is laborious, tedious, time consuming, and subject to many sources of error. Therefore, in this paper, we propose a new method for the automated morphometry of myelinated fibers in cross-section light microscopy images. Images from the recurrent laryngeal nerve of adult rats and the vestibulocochlear nerve of adult guinea pigs were used herein. The proposed pipeline for fiber segmentation is based on the techniques of competitive clustering and concavity analysis. The evaluation of the proposed method for segmentation of images was done by comparing the automatic segmentation with the manual segmentation. To further evaluate the proposed method considering morphometric features extracted from the segmented images, the distributions of these features were tested for statistical significant difference. The method achieved a high overall sensitivity and very low false-positive rates per image. We detect no statistical difference between the distribution of the features extracted from the manual and the pipeline segmentations. The method presented a good overall performance, showing widespread potential in experimental and clinical settings allowing large-scale image analysis and, thus, leading to more reliable results. 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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