Skull is the anatomic landmark for patient set up of head radiation therapy. Skull is generally segmented from CT images because CT provides better definition of skull than MR imaging. In the mean time, radiation therapy is planned on MR images for soft tissue information. This study utilized a knowledge-guided active model (KAM) method to segmented skull on MR images in order to enable radiation therapy planning with MR images as the primary planning dataset. KAM utilized age-specific skull mesh models that segmented from CT images using a conditional region growing algorithm. Skull models were transformed to given MR images using an affine registration algorithm based on normalized mutual information. The transformed mesh models actively located skull boundaries by minimizing their total energy. The preliminary validation was performed on MR and CT images from five patients. The KAM segmented skulls were compared with those segmented from CT images. The average image similarity (kappa index) was 0.57. The initial validation showed that it was promising to segment skulls directly on MR images using KAM

Hua, Chia-Ho

Ji, Qing

Krasin, Matthew J.

Kun, Larry E.

Merchant, Thomas E.

Parra, Carlos

Reddick, Wilburn E.

Shan, Zuyao Y.

Ying, Xiaofei

Crossref

A knowledge-guided active model method of skull segmentation on T1-weighted MR images

University of Queensland eSpace

* zuyao.shan@stjude.org; phone 1 901 495-2673; fax 1 901 495-4398; http://www.stjude.org/shan. 
A Knowledge-guided Active Model Method of Skull Segmentation on 
T1-weighted MR images 
 
Zuyao Y. Shan*a, Chia-Ho Huab, Qing Jia, Carlos Parraa, Xiaofei Yingb, Matthew J. Krasinb, 
Thomas E. Merchant b, Larry E. Kun b, Wilburn E. Reddicka 
aDivision of Translational Imaging Research, bDivision of Radiation Oncology, Department of 
Radiological Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, USA 
 
 
ABSTRACT 
 
Skull is the anatomic landmark for patient set up of head radiation therapy. Skull is generally segmented from 
CT images because CT provides better definition of skull than MR imaging. In the mean time, radiation therapy is 
planned on MR images for soft tissue information. This study utilized a knowledge-guided active model (KAM) method 
to segmented skull on MR images in order to enable radiation therapy planning with MR images as the primary 
planning dataset. KAM utilized age-specific skull mesh models that segmented from CT images using a conditional 
region growing algorithm. Skull models were transformed to given MR images using an affine registration algorithm 
based on normalized mutual information. The transformed mesh models actively located skull boundaries by 
minimizing their total energy. The preliminary validation was performed on MR and CT images from five patients. The 
KAM segmented skulls were compared with those segmented from CT images. The average image similarity (kappa 
index) was 0.57. The initial validation showed that it was promising to segment skulls directly on MR images using 
KAM. 
Keywords: Registration, Segmentation, Mathematical Morphology 
 
1. INTRODUCTION 
 
Currently, radiation planning procedures use images from both CT and magnetic resonance (MR) datasets.  
The primary reason of including CT images is because CT provides better definition of skull than MR imaging. The 
skull is the key anatomic landmark for patient set up in head radiation planning procedures. However, CT does not 
provide good contrast in soft tissue. Furthermore, MR images exhibit better disease regions (soft tissue tumors for most 
patients who require head radiation therapy) than those seen on CT images. Therefore, the state of the art for radiation 
planning is to register images from CT and MR together. The purpose of this study was to segment skull on MR images 
in order to enable radiation therapy planning using MR images as the primary planning dataset. The advantages of MR-
only radiation therapy simulation include (1) reducing radiation exposure for pediatric patients, (2) removing 
uncertainty introduced by CT and MR registration for soft tissue tumors, and (3) performing MR imaging in treatment 
position at the time of MR simulation. One major problem encountered toward implementing MR simulation is the lack 
of strong MR intensity of skull and clear differentiation of bone and air on the MR images. It is challenging to generate 
a good quality MR digital reconstructed radiographs showing skeletal position for treatment portal verification. 
Therefore, we proposed a knowledge-guided active model (KAM) method to extract skulls from MR images for this 
purpose. 
 
Although there are numerous MR segmentation methods in the literature, few studies devoted to segmentation 
of skull because of obscure differentiation of bone and air on MR images. Several previous studies did provide 
segmentation of skull as a by-product. For an example, a Markov random field (MRF) approach was developed to 
segment MR head images into gray matter, white matter, cerebrospinal fluid, scalp-bone, and background1.  However, 
because the method was not developed for skull segmentation, it does not always generate continuous bounding 
contours of skulls. With the development of magnetoencephalography(MEG) and electroencephalography(EEG) 
techniques, skull segmentation on MR images attracted some research interests recently. The impetus came from the 
fact that MEG and EEG inverse problems require realistic models of head for use in accurate computation of the 
mapping from neural current sources to scalp potentials and extracranial magnetic fields1. Dogdas et al. utilized Brain 
Surface Extractor (BET) algorithms followed by a combination of thresholding and morphological operations to 
Medical Imaging 2007: Image Processing, edited by Josien P. W. Pluim, Joseph M. Reinhardt, 
Proc. of SPIE Vol. 6512, 65122R, (2007) · 1605-7422/07/$18 · doi: 10.1117/12.709801
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segment the skull on MR images2. This study also did the quantitative validation using coregistered MR-CT data to 
evaluate their results. The average dice coefficient between automated segmented skulls and skulls from CT is 0.75. 
Other methods of segmentation of skull for this purpose were also reported previously. Since these studies have been 
reviewed in the previous report2, we did not described them one by one here. Because forward modeling calculation in 
MEG and EEG are primarily affected by the upper scalp and skull, furthermore, skull morphology in the lower portion 
of the head is extreme complex, these methods are unreliable on skull in the lower portion of head. However, these parts 
of skull are generally included as landmarks in radiation therapy planning procedures. 
 
In this paper, we described a knowledge-guided active mesh model method to segment skull on T1-weighted 
MR images. The skull was first extracted from CT images based on grey level intensity thresholding. A triangular mesh 
model was then constructed based on surface curvature from this skull and transformed onto MR images of given 
patient using an affine registration. The mesh model was further actively slithering to locate the boundary of skull using 
active mesh method. Finally, the KAM defined skull was compared with the skull from registered CT image to evaluate 
accuracy.     
 
2. METHODOLOGY 
2.1. Data  
The MR and CT images of six patients that used for radiation therapy panning previously were retrieved for 
this study. The retrospectively using of the data was approved by the Institutional Review Board at St Jude Children’s 
Research Hospital. The patients were from 9-10 years old when these images were collected. These clinic images have 
various voxel sizes, dimensions, and sizes of region of interest (ROI). We performed a preprocessing step to resample 
all CT and MR images with voxel size of 1× 1 × 1mm and similar ROIs.  
2.2. Skull mesh model  
The CT images from one of these six patients were randomly selected to construct the skull model. The skull 
was segmented from the CT images using a region growing algorithm with a threshold of bone intensity value (grey 
level of 1200 in this study). The triangle mesh model for the skull was constructed as follows. First, boundary surfaces 
of the skull including outer and inner boundaries were extracted using a 26 neighborhood judgment. Second, boundary 
surfaces were simplified based on the surface. Surfaces with higher curvature were simplified with more reference 
points and lower curvature surfaces with less reference points. Finally, a 3D triangular mesh model for the skull was 
constructed by triangulation of the reference points for the skull surface (Figure 1). 
2.3. Knowledge guided active model (KAM) 
The KAM used an object function similar to Gibbs free energy in classic physics: 
G H S= −                                            (1) 
Entropy (S) is the same as the normalized mutual information (NMI) described in previous studies3. The entropy (S) 
was maximized using an affine registration. To maximize the entropy, the model CT images were registered to 
individual MR images. Then the skull model was transformed using the transformation matrix found in the 
registration. The enthalpy (H) was minimized using active mesh method. The enthalpy includes the internal energy 
that controls smoothness and elasticity and the external energy that locates the image edges: 
ex inH E E= +                                         (2) 
The external energy for active model is defined by the distance of each triangle vertex to the edge of the image: 
2exp ( )ex
i
E d iα ⎡ ⎤= − −⎣ ⎦∑                                   (3) 
The internal energy is defined as the sum of the curvature energy (Ecur) and the continuity energy (Econ): 
( )21 iij ij
j
cur
iji
j
s n n
E
S
β
⎡ ⎤⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎣ ⎦
− •
=
∑∑ ∑
GG 
,      ( )2cont ij i
i j
E d dγ= −∑∑                    (4) 
 
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Figure 1: The skull model. The triangle mesh model was illustrated on the left and the surface rendering of the skull model was 
illustrated on the right. Both inner and outer surfaces of the skull were simplified by the triangle meshes based on curvature. The 
color from blue to red on the triangle mesh model represented the level from superior to inferior of the skull. 
2.4. Validation 
Five patients, excluding the one used to generate the skull model, were included in the validation study. We 
used the skull volumes segmented from corresponded CT images to evaluate the accuracy of the segmentation results. 
The CT images were registered to the MR images of the same patient. The skull was segmented from the CT images 
using a conditionally region growing algorithm and served as the gold standard. The KAM segmented skulls were 
compared with skulls segmented from CT images. The similarities between these two sets of images were evaluated by 
calculation of kappa index: 
2
( , ) a ba b
a b
S S
S S
S S
κ
∩
=
+
                                (5) 
where Sa refers to the skulls defined by the mesh models and Sb refers to the skull segmented from CT images. 
 
3. RESULTS 
 
The skull segmentation required 4 hours on a SGI Origin 300 computer without optimization of computation 
algorithms. Figure 2 illustrated a surface rendering of segmented skull and skull segmented based on CT from the same 
subject. The KAM segmented skull looks identical to skull generated from CT although parts of skull bone near eyes 
were missing and the surface of KAM segmented was rough than the ones segmented from CT images. Figures 3 
illustrated a segmented skull overlaid back to MR and CT images. We did observe some mismatches between skull 
segmented by KAM and from CT images. However, overall visual inspection showed that the segmented skull agreed 
with skull on CT images well. Routinely used anatomical landmarks for patient setup verification such as Sella Turcica 
and occipital bone were easily visible. 
 
Average kappa index between the KAM segmented skull and skull segmented from CT for five patients was 
0.57. 
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Figure 2: Surface rendering of segmented skull. The figure on the left showed a surface rendering of skull in cyan from the CT 
images. The figure on the right showed a surface rendering of skull segmented using KAM based on MR images from the same 
patients. The general shape and size of these two skulls are very similar, however, surface of skull segmented by KAM on MR 
images (yellow) is more rough than that from CT images.  
 
4. DISCUSSION 
 
We have presented a novel method, KAM, to segment the skull on MR images. To the best of our knowledge, 
this is the first study to segment the whole skull on MR images. Skull is generally invisible on MR images. It becomes 
more challenging in region close to the eyes because there is no visible boundary between bone and air. KAM utilized a 
prior defined model to define boundary skull and air interfaces. In the mean time, KAM compensate for inter-patient 
variability by minimizing of the free Gibbs energy. 
 
The image similarity between the KAM segmented skulls and those segmented from CT were relatively low. 
We believed that this is caused by two major reasons. First, the skull has a high surface to volume ratio, in other words, 
skull is a very thin structure. Any mismatches will lead to significant decrease of kappa index. Second, there is no 
perfect registration between CT and MR image. KAM identified skull based on MR images. Mismatches between CT 
and MR images will also lead to the decrease of the skull similarity. We selected skull segmented from CT images as 
the gold standard because there is no other better choice. Compared with previous study2, the accuracy of our method is 
lower than what they obtained. However, only the upper part of the skull was evaluated in the previous study. 
Furthermore, images from normal adults were used in the previous study. We also attempted to implement the method 
in the previous study for a comparison. However, the downloaded free software of Brain Surface Extractor (BET) 
algorithms failed on most of our MR data. The reason for this could be due the fact that additional inter-subject 
variability is high for our data because we worked on images from children with brain tumors. Additional inter-subject 
variability comes from that the patients were at different developmental stages and differences in surgical removal of 
brain tissues.    
 
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There are several shortcomings in this study. First, the validation was performed on relatively small number of 
patients. We are performing more extensive validation based on the success of this preliminary study. Second, we have 
selected skull segmented from CT images as the gold standard. As discuss above, mismatches between CT and MR  
 
Figure 3: Illustration of skull segmented by KAM. The upper row slices are selected slices from CT images. The lower row slices are 
corresponded ones of MR images from the same patients. Yellow regions are segmented skull overlaid back onto the CT and MR 
images.   
 
images will introduce additional discrepancies. Other validation approaches are under investigation. Finally, the 
accuracy of KAM segmented skull is not good enough to perform radiation therapy planning simulation yet. Some other 
options and improvement of the method is under investigation now.  
 
In conclusion, we have developed a knowledge-guided active model (KAM) method to automatically segment 
skull from MR images with promising initial validation results 
 
.  
 
ACKNOWLEDGEMENTS 
 
This work is supported by the Cancer Center Support Grant (CA21765) from the National Cancer Institute and by 
the American Lebanese Syrian Associated Charities (ALSAC). 
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 REFERENCES 
 
 
1.  K. Held, E. R. Kops, B. J. Krause, W. M. Wells, R. Kikinis, and H. W. Muller-Gartner, "Markov random field 
segmentation of brain MR images", IEEE Trans. Med. Imaging 16, pp. 878-886, 1997. 
2.  B. Dogdas, D. W. Shattuck, and R. M. Leahy, "Segmentation of skull and scalp in 3-D human MRI using 
mathematical morphology", Human Brain Mapping 26, pp. 273-285, 2005. 
3.  F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, "Multimodality image registration by 
maximization of mutual information", IEEE Trans. Med. Imaging 16, pp. 187-198, 1997. 
 
 
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A knowledge-guided active model method of skull segmentation on T1-weighted MR images

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