A Posterization Strategy for the Registration

of [123I]FP-CIT SPECT Brain Images

Diego Salas-Gonzalez

, Elmar W. Lang

, Juan M. G´orriz

and Javier Ram´ırez

CIML Group, Institute of Biophysics, University of Regensburg, Regensburg, Germany

Dpt. Signal Theory, Networking and Communications, University of Granada, Granada, Spain

Keywords:

[123I]FP-CIT SPECT Brain Images, Spatial Registration.

Abstract:

A fully automatic procedure to build a [123I]FP-CIT SPECT template in the MNI-space using only informa-

tion from the source images is presented. This approach does not require the acquisition of patient-speciﬁc

brain magnetic resonance image. This fully automatic procedure uses, ﬁrstly, the Otsu’s method to outline the

source images; secondly, a threshold strategy to posterize the source images and the template and, lastly, an

afﬁne registration algorithm by the optimization of a square root of sum of squares cost function.

1 INTRODUCTION

The low resolution and the intersubject variability be-

tween [123I]FP-CIT SPECT images renders very dif-

ﬁcult to perform the registration of these images. For

this reason, some published works performed the spa-

tial normalization assisted by the existence of a high

resolution MRI for each subject under study (van de

Giessen et al., 2013; Aarts et al., 2012). Sometimes,

there is not a T1-MRI available to assist the spatial

registration. For that cases, we present a fully auto-

matic method to perform the registration of [123I]FP-

CIT SPECT accurately. This method surmount the

difﬁculties of working with [123I]FP-CIT SPECT.

Namely, these images exhibit a lack of anatomical in-

formation outside the striatum. Speciﬁcally, in our

database, the brain image is cut and some slices in top

of the brain are missing.

Figure 1(a) depicts a montage showing all the

slices of a sample source image (transaxial view).

Figure 1(b) displays the transaxial, coronal and sagit-

tal view showing the maximum intensity value calcu-

lated for each in this 3 orthogonal projections of the

brain 3D volume for a sample source image.

2 [123I]FP-CIT SPECT BRAIN

IMAGES

40 FP-CIT SPECT brain images with bilateral, sym-

metrical uptake appeared in caudate and putamen nu-

clei. These patients were chosen to perform an FP-

CIT tomographic study because of a movement disor-

der, but they are all labeled as non Parkinsonian’s.

The images were obtained between 3 and 4 hours

after the intravenous injection of 185 MBq (5 mCi)

of Ioﬂupane-I-123, with prior thyroid blocking with

Lugol’s solution. The tomographic study (SPECT)

with Ioﬂupane/FP-CIT-I-123 was performed using a

General Electric gamma camera, Millennium model,

equipped with a dual head and general purpose colli-

mator. A 360

◦

circular orbit was made around the cra-

nium, at 3

◦

intervals, acquiring 60 images per detector

with a duration of 35 seconds per interval, each con-

sisting of a 128× 128 matrix. Transaxial image slices

were reconstructed using the ﬁltered back-projection

algorithm without attenuation correction, and apply-

ing a Hanning ﬁlter (cutoff frequency equal to 0.7).Fi-

nally, the dimension of the images in this dataset is

128× 128× Z, where Z ranges from 34 to 54 for dif-

ferent images.

3 POSTERIZED MNI TEMPLATE

IMAGE

Some functional imaging studies match their data to

a brain template from the Montreal Neurological In-

stitute (MNI) (Holmes et al., 1998; Aubert-Broche

et al., 2006). They are also used in the Statistical Para-

metric Mapping software (Friston et al., 2007). This

MNI template is based on an average of many scans

Salas-Gonzalez D., W. Lang E., Gorriz J. and Ramírez J..

A Posterization Strategy for the Registration of [123I]FP-CIT SPECT Brain Images.

DOI: 10.5220/0005296000880092

In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISAPP-2015), pages 88-92

ISBN: 978-989-758-091-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

(a)

(b)

Figure 1: (a) Montage with the transaxial slices of a selected source brain image. (b) Transaxial, coronal and sagittal view

showing the maximum intensity value projected in these 3 orthogonal planes for a sample source image.

of healthy young adults. We create a posterized ver-

sion of the brain in the standard space MNI (Montreal

Neurological Institute) of brain images using three in-

tensity values: 0 for voxels outside the head, 1 for the

head and a value in the striatum v

> 1. The position

of the striatum in the MNI space is taken by the infor-

mation in the labeled structural brain template image

provided by the MNI.

4 POSTERIZATION OF SOURCE

IMAGES

Firstly, we select those voxels corresponding to the

head in source images. We apply a low-pass band

ﬁlter to the image by smoothing it using a Gaussian

ﬁlter with the size of the convolution kernel [7 7 7]

and standard deviation equal to 5. This smoothness

of the image will allows us to distinguish more accu-

rately between the intensity values inside and outside

the brain. Once the source images are smoothed and

the high frequency noise reduced, we use the Otsu’s

method to automatically perform clustering-based im-

age thresholding, reducing the graylevel image to a

binary image (Otsu, 1979). This method assumes

that the source image contains two classes of vox-

els (head and background), then, calculates the op-

timum threshold separating those two classes so that

their combined intra-class variance is minimal. We

initially assign intensity value equal to 1 to the voxels

in the head and 0 to background voxels.

APosterizationStrategyfortheRegistrationof[123I]FP-CITSPECTBrainImages

Figure 2: Transaxial slice of the posterized brain image.

Left: Template in the MNI space. Right: Source image.

Then, we select the Ns voxels in the striatum with

highest intensity values. We set an intensity value

to these selected voxels. Therefore, after posteri-

zation, the source image has three different intensity

values: 0 outside the head (the background), 1 in the

head (outside the striatum) and v

> 1 in the stria-

tum. Figure 2 presents a transaxial slice of the pos-

terized brain template image and one sample source

image. The striatum is the most important volume in

the source image for this SPECT modality, but it is

very small. The parameter v

controls the weight of

the striatum in the calculation of the cost function to

perform the spatial transformation of the source im-

ages to the MNI space.

5 AFFINE REGISTRATION

After posterization of the MNI template and the

source images, we perform the afﬁne registration of

the posterized source images to the posterized brain

template in the MNI space. The 12 afﬁne parame-

ters are calculated using the Gauss-Newton optimiza-

tion method. The cost function to minimize is the

mean squared difference between the intensity val-

ues in source and template images (Salas-Gonzalez

et al., 2008). Once the 12 afﬁne parameters are calcu-

lated, we apply the afﬁne transformation to the origi-

nal source image.

6 RESULTS

We apply the proposed methodology to 40 [123I]FP-

CIT SPECT brain images. Initially, we select Ns =

400 voxels of the striatum in each source image.

We posterize them using three different levels for

background (voxel intensity = 0), head (voxel in-

tensity = 1) and striatum (voxel intensity, v

2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15). This value (v

) con-

trols the weight of the striatum in the calculation of

2 3 4 5 6 7 8 9 10 11 13 15

0.1

0.2

0.3

0.4

0.5

Weight Striatum

Jaccard Index

Figure 3: Mean Jaccard Index and error for varying v

(Ns =

400 voxels).

the cost function used to perform the afﬁne registra-

tion. Then, we perform the afﬁne registration of the

source images to the MNI template and measure the

overlap between the striatum in the source images and

the template using the Jaccard Index (JI).

We do not expect to get a Jaccard Index near 1 be-

cause the striatum of the posterized source and tem-

plate images are three-dimensional regions with dif-

ferent shapes and volumes, and therefore, using only

afﬁne transfomations, they are not expected to fully

overlap.

Figure 3 shows the mean Jaccard Index and the er-

ror measured as the 75th and 25th percentile for each

value of the weight v

. The measured value of JI in-

creases concomitantly with v

up to v

= 6, where the

best accuracy and lower error are obtained. For higher

values of v

, the measured JI decreases and the error

bar increases.

Once the posterized source images have been reg-

istered to the posterized MNI template, the estimated

12 afﬁne parameters are applied to the original im-

ages. Then, the mean image is calculated to build the

template.

Figure 4 shows all transaxial slices of the FP-CIT

SPECT template built using the proposed methodol-

ogy and the MRI T1 template in the MNI space. It

can be visually checked that the FP-CIT template has

been successfully transformed to the MNI space.

Figure 5 shows a transaxial slice of the FP-CIT

SPECT Template superimposed to the T1 MRI tem-

plate in the MNI space. This ﬁgure shows that the

high intensity values corresponding to the striatum is

accurately placed in its true anatomical position.

VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications

(a)

(b)

Figure 4: (a) T1 template in the MNI space. (b) [123I]FP-CIT SPECT template.

APosterizationStrategyfortheRegistrationof[123I]FP-CITSPECTBrainImages

Figure 5: Brain fusion image in the MNI space: MRI and

[123I]FP-CIT SPECT.

7 CONCLUSION

In this work, a procedure to build a [123I]FP-CIT

SPECT template is presented. The brain images

are posterized to three different intensity level (back-

ground, brain and striatum). Then, these image are

registered to a previously posterized template im-

age using a 12 parameters afﬁne model. The pro-

posed methodology is shown to accurately works for

[123I]FP-CIT SPECT images, even when high reso-

lution magnetic resonance image for each subject is

not available.

ACKNOWLEDGEMENTS

This work has been supported by a Marie Curie Intra-

European Fellowship from the 7th Framework Pro-

gramme FP7-PEOPLE-2013-IEF (Project: 624453

ALPHA-BRAIN-IMAGING).

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