Segmentation of female pelvic organs in axial magnetic resonance images using coupled geometric deformable models

• Tipo: Artigo em Revista Científica Internacional
• Data: 2013

Autores

Zhen Ma (Autor) Outra; Renato M. Natal Jorge (Autor) FEUP; Teresa Mascarenhas (Autor) FMUP; João Manuel R. S. Tavares (Autor) FEUP

Revista

Título: Computers in Biology and Medicine

Vol. 43 Nº 4

Páginas: 248-258

ISSN: 0010-4825

Editora: Elsevier

Indexação

ISI Web of Knowledge - 18 Citações

ISI Web of Science

Scopus - 20 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências biológicas

CORDIS: Ciências Tecnológicas

Outras Informações

ID Authenticus: P-005-1FD

DOI: 10.1016/j.compbiomed.2012.12.012

Abstract (EN): The segmentation of pelvic structures in magnetic resonance (MR) images of the female pelvic cavity is a challenging task. This paper proposes the use of three novel geometric deformable models to segment the bladder, vagina and rectum in axial MR images. The different imaging appearances and prior shape knowledge are combined into a level set framework as segmentation cues. The movements of the contours are coupled with each other based on interactive information, and the organ boundaries can be segmented simultaneously. With the region-based external forces defined, the proposed algorithms are robust against noise and partial volume effect.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11