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A Supervised Graph-Cut Deformable Model for Brain MRI Segmentation

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Deformation Models

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 7))

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Abstract

Accurate automatic segmentation of subcortical brain structures in Magnetic Resonance Images (MRI) is of great interest in the analysis of developmental disorders. Segmentation methods based on a single or multiple atlases have been shown to suitably localize brain structures. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentation. A fully-automatic segmentation method for brain MRI is considered, which defines a deformable model combining an atlas-based segmentation strategy with a supervised Graph-cut model. The Graph-cut model is adapted to make it suitable for segmenting small and low-contrast brain structures by defining new data and boundary potentials of the energy function. In particular, information concerning the intensity and geometry is exploited, and supervised energies based on contextual brain structures are added. Furthermore, boundary detection is reinforced using a new multi-scale edgeness measure. The method is applied to the segmentation of the brain caudate nucleus in a set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as in a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures, and present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD. The obtained results show improved performance in terms of segmentation accuracy compared to state-of-the-art approaches.

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Notes

  1. 1.

    www.cabiatl.com/mricro/

  2. 2.

    www.cause07.org/

  3. 3.

    http://elastix.isi.uu.nl

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Authors and Affiliations

  1. Universitat de Barcelona, Gran Via de les Corts Catalanes 585, 08007 , Barcelona, Spain

    Laura Igual, Antonio Hernández-Vela, Sergio Escalera & Petia Radeva

  2. Computer Vision Center (CVC), Campus UAB, Edifici 0, Bellaterra, 08193 , Barcelona, Spain

    Laura Igual, Antonio Hernández-Vela, Sergio Escalera & Petia Radeva

  3. Unitat de Recerca en Neurociència Cognitiva (URNC) Department of Psychiatry, Universitat Autònoma de Barcelona (UAB), IAPS Hospital del Mar. Passeig Marítim, 25-29, 08003 , Barcelona, Spain

    Joan Carles Soliva & Oscar Vilarroya

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  1. Laura Igual
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  2. Joan Carles Soliva
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  3. Antonio Hernández-Vela
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  4. Sergio Escalera
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  6. Petia Radeva
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Correspondence to Laura Igual .

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Editors and Affiliations

  1. , Maths. and Computer Science, University of the Balearic Islands, Crta. de Valldemossa Km. 7.5, Anselm Turmeda Building, Palma de Mallorca, 07122, Spain

    Manuel González Hidalgo

  2. , Maths. and Computer Science, University of the Balearic Islands, Crta. de Valldemossa km. 7.5, Anselm Turmeda Building, Palma de Mallorca, 07122, Spain

    Arnau Mir Torres

  3. , Maths. and Computer Science, University of the Balearic Islands, Crta. de Valldemossa km 7.5, Anselm Turmeda Building, Palma de Mallorca, 07122, Spain

    Javier Varona Gómez

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Igual, L., Soliva, J.C., Hernández-Vela, A., Escalera, S., Vilarroya, O., Radeva, P. (2013). A Supervised Graph-Cut Deformable Model for Brain MRI Segmentation. In: González Hidalgo, M., Mir Torres, A., Varona Gómez, J. (eds) Deformation Models. Lecture Notes in Computational Vision and Biomechanics, vol 7. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5446-1_10

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  • DOI: https://doi.org/10.1007/978-94-007-5446-1_10

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