dc.creatorDorini, LB
dc.creatorMinetto, R
dc.creatorLeite, NJ
dc.date2013
dc.dateJAN
dc.date2014-07-30T18:31:51Z
dc.date2015-11-26T17:49:28Z
dc.date2014-07-30T18:31:51Z
dc.date2015-11-26T17:49:28Z
dc.date.accessioned2018-03-29T00:32:33Z
dc.date.available2018-03-29T00:32:33Z
dc.identifierIeee Journal Of Biomedical And Health Informatics. Ieee-inst Electrical Electronics Engineers Inc, v. 17, n. 1, n. 250, n. 256, 2013.
dc.identifier2168-2194
dc.identifierWOS:000321142500029
dc.identifier10.1109/TITB.2012.2207398
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/71422
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/71422
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1289393
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionThis paper approaches novel methods to segment the nucleus and cytoplasm of white blood cells (WBC). This information is the basis to perform higher level tasks such as automatic differential counting, which plays an important role in the diagnosis of different diseases. We explore the image simplification and contour regularization resulting from the application of the selfdual multiscale morphological toggle (SMMT), an operator with scale-space properties. To segment the nucleus, the image preprocessing with SMMT has shown to be essential to ensure the accuracy of two well-known image segmentations techniques, namely, watershed transform and Level-Set methods. To identify the cytoplasm region, we propose two different schemes, based on granulometric analysis and on morphological transformations. The proposed methods have been successfully applied to a large number of images, showing promising segmentation and classification results for varying cell appearance and image quality, encouraging future works.
dc.description17
dc.description1
dc.description250
dc.description256
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionFundacao Araucaria [17588]
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionFundacao Araucaria [17588]
dc.languageen
dc.publisherIeee-inst Electrical Electronics Engineers Inc
dc.publisherPiscataway
dc.publisherEUA
dc.relationIeee Journal Of Biomedical And Health Informatics
dc.relationIEEE J. Biomed. Health Inform.
dc.rightsfechado
dc.rightshttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dc.sourceWeb of Science
dc.subjectMathematical morphology
dc.subjectmedical image analysis
dc.subjectwhite blood cell image segmentation
dc.subjectClassification
dc.titleSemiautomatic White Blood Cell Segmentation Based on Multiscale Analysis
dc.typeArtículos de revistas


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