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Active Contours Using Additive Local and Global Intensity Fitting Models for Intensity Inhomogeneous Image Segmentation.
Soomro, Shafiullah; Akram, Farhan; Kim, Jeong Heon; Soomro, Toufique Ahmed; Choi, Kwang Nam.
Afiliação
  • Soomro S; Department of Computer Science and Engineering, Chung-Ang University, Seoul 156-756, Republic of Korea.
  • Akram F; Department of Computer Engineering and Mathematics, Rovira i Virgili University, 43007 Tarragona, Spain.
  • Kim JH; Korea Institute of Science and Technology Information, Daejeon 305-806, Republic of Korea.
  • Soomro TA; School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW 2795, Australia.
  • Choi KN; Department of Computer Science and Engineering, Chung-Ang University, Seoul 156-756, Republic of Korea.
Comput Math Methods Med ; 2016: 9675249, 2016.
Article em En | MEDLINE | ID: mdl-27800011
This paper introduces an improved region based active contour method with a level set formulation. The proposed energy functional integrates both local and global intensity fitting terms in an additive formulation. Local intensity fitting term influences local force to pull the contour and confine it to object boundaries. In turn, the global intensity fitting term drives the movement of contour at a distance from the object boundaries. The global intensity term is based on the global division algorithm, which can better capture intensity information of an image than Chan-Vese (CV) model. Both local and global terms are mutually assimilated to construct an energy function based on a level set formulation to segment images with intensity inhomogeneity. Experimental results show that the proposed method performs better both qualitatively and quantitatively compared to other state-of-the-art-methods.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Reconhecimento Automatizado de Padrão / Diagnóstico por Imagem / Interpretação de Imagem Assistida por Computador Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Reconhecimento Automatizado de Padrão / Diagnóstico por Imagem / Interpretação de Imagem Assistida por Computador Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2016 Tipo de documento: Article