Brain tissue segmentation on Diffusion Weighted Magnetic Resonance data

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

Description

Title: Brain tissue segmentation on Diffusion Weighted Magnetic Resonance data
Author/s:
  • Esteban, Oscar
  • Gorthi, Subrahmanyam
  • Daducci, Alessandro
  • Ledesma Carbayo, María Jesús
  • Santos Lleo, Andres de
  • Thiran, Jean-Philippe
  • Bach-Cuadra, Meritxell
Item Type: Presentation at Congress or Conference (Poster)
Event Title: 2012 10th IEEE International Symposium on Biomedical Imaging (ISBI)
Event Dates: 2012
Event Location: Barcelona (Spain)
Title of Book: IEEE International Symposium on Biomedical Imaging (ISBI)
Date: May 2012
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition

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Abstract

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.

More information

Item ID: 22221
DC Identifier: http://oa.upm.es/22221/
OAI Identifier: oai:oa.upm.es:22221
Deposited by: Oscar Esteban
Deposited on: 27 Jan 2014 14:38
Last Modified: 21 Apr 2016 14:00
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