Segmentation and 3D reconstruction approaches for the design of laparoscopic augmented reality environments

Sánchez González, Patricia ORCID: https://orcid.org/0000-0001-9871-0884, Gayá Moreno, Francisco Javier, Cano González, Alicia and Gómez Aguilera, Enrique Javier ORCID: https://orcid.org/0000-0001-6998-1407 (2008). Segmentation and 3D reconstruction approaches for the design of laparoscopic augmented reality environments. "Lecture Notes in Computer Science", v. 5104 ; pp. 127-134. ISSN 0302-9743. https://doi.org/10.1007/978-3-540-70521-5_14.

Descripción

Título: Segmentation and 3D reconstruction approaches for the design of laparoscopic augmented reality environments
Autor/es:
Tipo de Documento: Artículo
Título de Revista/Publicación: Lecture Notes in Computer Science
Fecha: Julio 2008
ISSN: 0302-9743
Volumen: 5104
Materias:
ODS:
Palabras Clave Informales: Laparoscopic surgery, video analysis, illumination model, segmentation, 3D reconstruction, augmented reality.
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Tecnología Fotónica y Bioingeniería
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

A trend in abdominal surgery is the transition from minimally invasive surgery to surgeries where augmented reality is used. Endoscopic video images are proposed to be employed for extracting useful information to help surgeons performing the operating techniques. This work introduces an illumination model into the design of automatic segmentation algorithms and 3D reconstruction methods. Results obtained from the implementation of our methods to real images are supposed to be an initial step useful for designing new methodologies that will help surgeons operating MIS techniques.

Más información

ID de Registro: 2736
Identificador DC: https://oa.upm.es/2736/
Identificador OAI: oai:oa.upm.es:2736
Identificador DOI: 10.1007/978-3-540-70521-5_14
URL Oficial: http://www.springerlink.com/content/105633
Depositado por: Memoria Investigacion
Depositado el: 29 Mar 2010 09:50
Ultima Modificación: 21 May 2024 14:23