Non-parametric and integrated framework for segmenting and counting neuroblastic cells within neuroblastoma tumor images.
Neuroblastoma is a malignant tumor and a cancer in childhood that derives from the neural crest. The number of neuroblastic cells within the tumor provides significant prognostic information for pathologists. An enormous number of neuroblastic cells makes the process of counting tedious and error-pr...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 51; no. 6; pp. 645 - 656 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jun2013
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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=104072792&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104072792 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2013 vid: 51 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104072792 NLM23359256 2012112715 10.1007/s11517-013-1034-9 NLM23359256 104072792 ppf: 645 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Non-parametric and integrated framework for segmenting and counting neuroblastic cells within neuroblastoma tumor images. aug: au: Tafavogh, Siamak Navarro, Karla Felix Catchpoole, Daniel R Kennedy, Paul J affil: Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, PO Box 123, Broadway, Sydney, NSW, 2007, Australia, siamak.tafavogh@student.uts.edu.au. sug: subj: Image Interpretation, Computer Assisted Methods Neuroblastoma Pathology Algorithms Cell Count Methods Human Information Science Methods ab: Neuroblastoma is a malignant tumor and a cancer in childhood that derives from the neural crest. The number of neuroblastic cells within the tumor provides significant prognostic information for pathologists. An enormous number of neuroblastic cells makes the process of counting tedious and error-prone. We propose a user interaction-independent framework that segments cellular regions, splits the overlapping cells and counts the total number of single neuroblastic cells. Our novel segmentation algorithm regards an image as a feature space constructed by joint spatial-intensity features of color pixels. It clusters the pixels within the feature space using mean-shift and then partitions the image into multiple tiles. We propose a novel color analysis approach to select the tiles with similar intensity to the cellular regions. The selected tiles contain a mixture of single and overlapping cells. We therefore also propose a cell counting method to analyse morphology of the cells and discriminate between overlapping and single cells. Ultimately, we apply watershed to split overlapping cells. The results have been evaluated by a pathologist. Our segmentation algorithm was compared against adaptive thresholding. Our cell counting algorithm was compared with two state of the art algorithms. The overall cell counting accuracy of the system is 87.65 %. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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