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Automatic segmentation of newborns’ skull and fontanel from CT data using model-based variational level set

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Abstract

The newborn’s cranium is composed of flat cranial bone and fontanels forming together the envelope of the cerebral cavity. The fontanels are relatively flexible since they consist of fibrous membrane that ossifies during maturation becoming flat cranial bone as well. Fontanels give less contrast in computerized tomography (CT) images; they can be identified as gaps between the cranial bones. In this paper, we propose an automatic model-based method using variational level set to segment the skull and fontanels from CT images. In this approach, firstly a skull model consisting of cranial bones and fontanels is created and then used as constraint for level set evolution. Then, by removing the cranial bones from the segmented skulls, the fontanels are obtained. To verify the validity of the achieved results, automatically segmented skull and fontanels have been compared with the ones manually segmented by an expert using Dice similarity and Hausdorff dissimilarity measures, which show the good agreement between them. Furthermore, the surface areas of cranium and fontanel have been determined for these segmentations. The results for both, manual and automatic segmentation, are in good agreement.

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Abbreviations

A i (·):

The 12-parameter affine transformation

C :

Evolving contour

c 1 :

Approximation of the mean value of image intensity inside C

c 2 :

Approximation of the mean value of image intensity outside C

D(·):

The Euclidean distance

\({D_{H_K} (\cdot)}\) :

The Hausdorff distance

D i (·):

Nonlinear deformation

h(·):

Direct Hausdorff distance

H(·):

Heaviside function

I :

Input image

I R :

Reference image

I′:

The affine-normalized image

I′′:

The affine- and nonlinear normalized image

Ĩ:

Transformed image by applying computed \({\bar{T}}\) to \({I_i^{{\prime}{\prime}}}\) for each subject

S′:

The affine-normalized extracted skull

S′′:

The affine and nonlinear normalized skull

S͂:

Transformed skull by applying computed \({\bar{T}}\) to \({S_i^{{\prime}{\prime}}}\) for each subject

T :

The inverse nonlinear transformation

\({\bar{T}}\) :

The mean of T i transformations

W:

Registration parameters

α :

Non-negative constant

δ(.):

Delta function

ε :

Constant for Heaviside function approximation

λ 1 :

Positive weighting constants

λ 2 :

Positive weighting constants

μ :

Positive weighting constant

ν :

Positive weighting constant

φ :

Level set function

φ m :

Level set function of the shape prior

Ω:

Image space

\({\mathfrak{R}^{n}}\) :

n-Dimensional space

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

    Nassim Jafarian, Kamran Kazemi & Mohamad Sadegh Helfroush

  2. GRAMFC, Inserm U1105, UFR of Medicine, University of Picardie Jules Verne, 3 rue des Louvels, 80036, Amiens, France

    Kamran Kazemi, Hamid Abrishami Moghaddam, Reinhard Grebe & Fabrice Wallois

  3. Faculty of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Hamid Abrishami Moghaddam

  4. GRAMFC, Inserm U1105, EFSN Pediatrique, CHU Amiens, Place V. Pauchet, 80054, Amiens, France

    Marc Fournier & Fabrice Wallois

  5. MRI Unit, Department of Radiology, North Hospital, 80056, Amiens, France

    Catherine Gondry-Jouet

Authors
  1. Nassim Jafarian
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  2. Kamran Kazemi
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  3. Hamid Abrishami Moghaddam
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  4. Reinhard Grebe
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  6. Mohamad Sadegh Helfroush
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  7. Catherine Gondry-Jouet
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  8. Fabrice Wallois
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Correspondence to Kamran Kazemi.

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Jafarian, N., Kazemi, K., Abrishami Moghaddam, H. et al. Automatic segmentation of newborns’ skull and fontanel from CT data using model-based variational level set. SIViP 8, 377–387 (2014). https://doi.org/10.1007/s11760-012-0300-x

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  • DOI: https://doi.org/10.1007/s11760-012-0300-x

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