Brain tissue segmentation on Diffusion Weighted Magnetic Resonance data

Esteban, Oscar; Gorthi, Subrahmanyam; Daducci, Alessandro; Ledesma Carbayo, María Jesús; Santos Lleo, Andres de; Thiran, Jean-Philippe y Bach-Cuadra, Meritxell (2012). Brain tissue segmentation on Diffusion Weighted Magnetic Resonance data. En: "2012 10th IEEE International Symposium on Biomedical Imaging (ISBI)", 2012, Barcelona (Spain).

Descripción

Título: Brain tissue segmentation on Diffusion Weighted Magnetic Resonance data
Autor/es:
  • Esteban, Oscar
  • Gorthi, Subrahmanyam
  • Daducci, Alessandro
  • Ledesma Carbayo, María Jesús
  • Santos Lleo, Andres de
  • Thiran, Jean-Philippe
  • Bach-Cuadra, Meritxell
Tipo de Documento: Ponencia en Congreso o Jornada (Póster)
Título del Evento: 2012 10th IEEE International Symposium on Biomedical Imaging (ISBI)
Fechas del Evento: 2012
Lugar del Evento: Barcelona (Spain)
Título del Libro: IEEE International Symposium on Biomedical Imaging (ISBI)
Fecha: Mayo 2012
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería Electrónica
Licencias Creative Commons: Reconocimiento

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Resumen

Introduction Diffusion weighted Imaging (DWI) techniques are able to measure, in vivo and non-invasively, the diffusivity of water molecules inside the human brain. DWI has been applied on cerebral ischemia, brain maturation, epilepsy, multiple sclerosis, etc. [1]. Nowadays, there is a very high availability of these images. DWI allows the identification of brain tissues, so its accurate segmentation is a common initial step for the referred applications. Materials and Methods We present a validation study on automated segmentation of DWI based on the Gaussian mixture and hidden Markov random field models. This methodology is widely solved with iterative conditional modes algorithm, but some studies suggest [2] that graph-cuts (GC) algorithms improve the results when initialization is not close to the final solution. We implemented a segmentation tool integrating ITK with a GC algorithm [3], and a validation software using fuzzy overlap measures [4]. Results Segmentation accuracy of each tool is tested against a gold-standard segmentation obtained from a T1 MPRAGE magnetic resonance image of the same subject, registered to the DWI space. The proposed software shows meaningful improvements by using the GC energy minimization approach on DTI and DSI (Diffusion Spectrum Imaging) data. Conclusions The brain tissues segmentation on DWI is a fundamental step on many applications. Accuracy and robustness improvements are achieved with the proposed software, with high impact on the application’s final result.

Más información

ID de Registro: 22221
Identificador DC: http://oa.upm.es/22221/
Identificador OAI: oai:oa.upm.es:22221
Depositado por: Oscar Esteban
Depositado el: 27 Ene 2014 14:38
Ultima Modificación: 21 Abr 2016 14:00
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