DESARROLLO DE HERRAMIENTAS DE SOFT COMPUTING PARA LA AYUDA AL DIAGNOSTICO CLINICO Y A LA GESTION DE EMERGENCIAS

PID2020-113870GB-I00

Nombre agencia financiadora Agencia Estatal de Investigación
Acrónimo agencia financiadora AEI
Programa Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i
Subprograma Subprograma Estatal de Generación de Conocimiento
Convocatoria Proyectos I+D
Año convocatoria 2020
Unidad de gestión Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020
Centro beneficiario UNIVERSIDAD DE LAS ISLAS BALEARES
Identificador persistente http://dx.doi.org/10.13039/501100011033

Publicaciones

Resultados totales (Incluyendo duplicados): 1
Encontrada(s) 1 página(s)

A framework for active contour initialization with application to liver segmentation in MRI

Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
  • Mir-Fuentes, Arnau
  • Mir Torres, Arnau
  • Antunes Dos Santos, Felipe
  • Fernández Fernández, Francisco Javier
  • López Molina, Carlos
Object segmentation is a prominent low-level task in image processing and computer vision. A technique of special relevance within segmentation algorithms is active contour modeling. An active contour is a closed contour on an image which can be evolved to progressively fit the silhouette of certain area or object. Active contours shall be initialized as a closed contour at some position of the image, further evolving to precisely fit to the silhouette of the object of interest. While the evolution of the contour has been deeply studied in literature [5, 11], the study of strategies to define the initial location of the contour is rather absent from it. Typically, such contour is created as a small closed curve around an inner position in the object. However, literature contains no general-purpose algorithms to determine those inner positions, or to quantify their fitness. In fact, such points are frequently set manually by human experts, hence turning the segmentation process into a semi-supervised one. In this work, we present a method to find inner points in relevant object using spatial-tonal fuzzy clustering. Our proposal intends to detect dominant clusters of bright pixels, which are further used to identify candidate points or regions around which active contours can be initialized., The authors gratefully acknowledge the financial support of the grants PID2019-108392GB-I00 funded by MCIN/AEI/10.13039/501100011033, as well as that by the Government of Navarra (PC082-083-084 EHGNA). A. Mir acknowledges the financial support of the grant PID2020-113870GB-I00 funded by MCIN/AEI/10.13039/ 501100011033/.