Malaria Cell Counting Diagnosis within Large Field of View

Zou, Li-hui; Chen, Jie; Zhang, Juan y García Santos, Narciso (2010). Malaria Cell Counting Diagnosis within Large Field of View. En: "International Conference on Digital Image Computing: Techniques and Applications DICTA 2010", 01/12/2010 - 03/12/2010, Sydney, Australia. ISBN 978-1-4244-8816-2.

Descripción

Título: Malaria Cell Counting Diagnosis within Large Field of View
Autor/es:
  • Zou, Li-hui
  • Chen, Jie
  • Zhang, Juan
  • García Santos, Narciso
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: International Conference on Digital Image Computing: Techniques and Applications DICTA 2010
Fechas del Evento: 01/12/2010 - 03/12/2010
Lugar del Evento: Sydney, Australia
Título del Libro: Proceedings of the International Conference on Digital Image Computing: Techniques and Applications DICTA 2010
Fecha: 2010
ISBN: 978-1-4244-8816-2
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[img]
Vista Previa
PDF (Document Portable Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (552kB) | Vista Previa

Resumen

Malaria is one of the most serious parasitic infections of human. The accurate and timely diagnosis of malaria infection is essential to control and cure the disease. Some image processing algorithms to automate the diagnosis of malaria on thin blood smears are developed, but the percentage of parasitaemia is often not as precise as manual count. One reason resulting in this error is ignoring the cells at the borders of images. In order to solve this problem, a kind of diagnosis scheme within large field of view (FOV) is proposed. It includes three steps. The first step is image mosaicing to obtain large FOV based on space-time manifolds. The second step is the segmentation of erythrocytes where an improved Hough Transform is used. The third step is the detection of nucleated components. At last, it is concluded that the counting accuracy of malaria infection within large FOV is finer than several regular FOVs

Más información

ID de Registro: 9236
Identificador DC: http://oa.upm.es/9236/
Identificador OAI: oai:oa.upm.es:9236
URL Oficial: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5692560
Depositado por: Memoria Investigacion
Depositado el: 17 Oct 2011 08:44
Ultima Modificación: 20 Abr 2016 17:44
  • Open Access
  • Open Access
  • Sherpa-Romeo
    Compruebe si la revista anglosajona en la que ha publicado un artículo permite también su publicación en abierto.
  • Dulcinea
    Compruebe si la revista española en la que ha publicado un artículo permite también su publicación en abierto.
  • Recolecta
  • e-ciencia
  • Observatorio I+D+i UPM
  • OpenCourseWare UPM