Despotovic, Ivana

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Ghent University Academic Bibliography

3D MRI Head Segmentation in Newborn InfantsIvana Despotovic´Supervisor(s): Ewout Vansteenkiste, Wilfried PhilipsI. INTRODUCTIONNowadays, magnetic resonance imaging(MRI) has become one of the most importantmedical imaging techniques for studying thehuman brain. Very often, tissue segmentationis a prerequisite for a further clinical analysis.MRI segmentation of different head struc-tures in newborn infants plays a crucial rolein three-dimensional (3D) volume visualiza-tion and allows us to measure the growth, toexplore developmental changes and to detectearly disorders of different anatomical struc-tures for both scientific and clinical investi-gations. For example, to combine electroen-cephalography (EEG) and MRI for the epilep-tic source localization, an anatomic head modelis required with segments of scalp, skull, cere-brospinal fluid (CSF) and brain tissue, whichhave significantly different electrical conduc-tivities. However, accurate tissue segmentationis a difficult task and presents a range of chal-lenges (i.e. presence of noise, partial volumeeffects, bias field and motion artifacts). Thisis even more complex for developing neonates,mainly due to the fast growth process and dif-ferent biological tissue properties. To date,many MRI segmentation techniques have beenreported for adult brain, but very few for theneonatal brain [1].II. METHODIn this work we present a hybrid segmen-tation algorithm that combines fuzzy-c means—————————————————–I. Despotovic´ is with the department of Telecom-munications and Information Processing of GhentUniversity (UGent - TELIN - IPI), Gent, Belgium.E-mail: Ivana.Despotovic@telin.ugent.be.Figure 1. Outline of the proposed MRI segmentationalgorithm.(FCM) clustering, thresholding, mathematicalmorphology and active contours. Our pro-posed algorithm performs 3D head segmenta-tion based on information from T1-weighted(T1w) and T2-weighted (T2w) MRI and istested on neonates with the gestational agesfrom 38 to 42 weeks. Due to the low inter-sliceresolution, each slice is processed separatelyand the head is segmented into five classes:scalp, fat, skull, CSF and brain tissue.The proposed algorithm is implemented inthe five following steps (see Fig. 1):Step 1: Affine registration of T1w and T2wimages using mutual information [2].Step 2: Automatic generation of the headmask and brain mask using histogram thresh-olding, morphology and active contours [3].Step 3: Brain segmentation into two clusters,CSF and brain tissue, using a FCM segmenta-tion algorithm with adaptive enhancement [4],[5]. In order to increase the performance of thealgorithm and to generate a fast and reliable es-timator of the FCM’s parameters, an intensity-Figure 2. 2D segmentation of the head of a 39weeks old infant. Upper row shows originalT1w MRI images and the lower row presentsthe segmented images. At the bottom is givenexplanation of the labels.based thresholding is used for the initialization.Step 4: Segmentation of the fat and skull us-ing the same FCM algorithm applied to the im-ages without brain tissue.Step 5: Scalp (skin) reconstruction by se-lecting all pixels between the edge of the headmask and fat using a morphological dilationwith a circular structuring element of a radiusone.Afterwards, when different compartments ofthe brain are obtained, the 3D reconstruction ofthe head is made. Due to the low inter-sliceresolution, the head has a step-shaped layout.To improve the results and 3D visualization weapply curve interpolation on segmented slices.III. RESULTST1 MRI images were acquired on a Siemens1,5T MRI scanner (256×256×20 voxel matrixwith a resolution of 0.7mm×0.7mm×4mm).The recorded data sets are processed using ourproposed method. Final 2D and 3D results areshown in Fig. 2 and Fig. 3. When presentingthese results to the expert physician, he scoredthem as highly plausible and acceptable.One of the applications of this work is de-velopment of a realistic head model for EEGsource localization.Figure 3. 3D segmentation: (a) segmented head witha low inter-slice resolution; (b) segmented headafter final refinement; (c) realistic head modelwith labels.IV. CONCLUSIONIn summary, we have introduced a head seg-mentation method for neonates without usingprior knowledge, like atlases and probabilisticalgorithms. This method yields a good segmen-tation performance even in noisy images, and ithas been validated on real MRI brain images.The validation results demonstrate an encour-aging future of practical applications of the pro-posed method.REFERENCES[1] M. Prastawa, J.H. Gilmore, W.Lin, and G.Gerig,Automatic segmentation of mr images of the de-veloping newborn brain, Med Image Anal, vol. 9,no. 5, pp. 457-466, Oct. 2005.[2] MedINRIA - ImageFusion tool, http://www-sop.inria.fr/asclepios/software/MedINRIA.[3] T.F. Chan and L.A. Vese, Active contours withoutedges, IEEE JIP, vol. 10, no. 2, pp. 266-277, NewYork, 2001.[4] J.C. Bezdek, Pattern Recognition with Fuzzy Ob-jective Function Algorithms, Plenum Press, NewYork, 1981.[5] J.H. Xue, A. Pizˇurica, W. Philips, E. Kerre, R.Van de Walle and I. Lemahieu An integratedmethod of adaptive enhancement for unsuper-vised segmentation of mri brain images, Pat-tern Recognition Letters, vol. 24, pp. 2549-2560,2003.

3D MRI head segmentation in newborn infants

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3D MRI head segmentation in newborn infants

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