Skull Segmentation of MR images based on texture features for attenuation correction in PET/MR

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1 Skull Segmentation of MR images based on texture features for attenuation correction in PET/MR CHAIBI HASSEN, NOURINE RACHID ITIO Laboratory, Oran University ABSTRACT The work we present in this paper is part of overall development combined PET/MRI system as an alternative to PET/CT system. We propose a new segmentation method of the skull on brain MR image, for MR-based attenuation correction. Our aim is to obtain three distinct classes: bone, air and other tissue. We chose the first-order features to compute the primitive texture. Texture features are calculated around each pixel in eight selected neighbourhoods, and for two different window sizes. We use random forest algorithm for classification of the MRI pixels. The method has been tested with real MRI data. Our results present a good segmentation of the skull in the MRI images; moreover it is useful for the correction for the attenuation in PET/MRI. Keywords: skull segmentation, Attenuation correction, PET/MRI, random forest. I. INTRODUCTION Attenuation correction is an important step in quantitative analysis of brain PET images.in the case of combined PET/CT, attenuation correction is straightforward: the attenuation coefficients (AC) are calculated from transmission CT images, where different tissues are fairly well discriminated (good separation between the bones and other tissue). Unfortunately, this does not work as well for the PET/MRI as bone and air have similar aspects on MRI. In addition, MRI alone does not provide sufficient information on the attenuation coefficients of tissues [1]. Therefore, it is necessary to use other methods for estimating AC from the MRI. The relative amount of bone in the head is higher than in other parts of the body, and this bone contributes significantly to the attenuation of PET photons. MR-based attenuation correction (MRAC) methods for the head/brain must thus consider bone in attenuation maps to allow for accurate PET quantification specifically in brain PET imaging [2]. For all standard MR sequences, the MR intensity voxel does not contain sufficient information to uniquely determine its tissue class. For example bone and air have the largest difference in PET attenuation coefficients, but show the same intensity values in T1- weighted MR images. Segmentation of skull from MR images therefore presents a challenging problem. II. RELATED WORKS Xiaofeng Yang [3] develop a skull segmentation method for MRAC. He transform T1-weithed MR image to the radon domain in order to detect the feature of the skull, and use a bilateral filter to get a mask for skull segmentation. A. Santos [4] use probabilistic neural network for a skull segmentation of UTE MR Images, although UTE is MRI sequences with the capability to generate signal from cortical bone. In other way several methods have been proposed to automate the skull-stripping task [13][14][15]. A current taxonomy for such methods is defined by three categories: region-based methods, edge-based methods and meta-algorithms [5]. The most of these techniques require manual intervention and are not adapted for MRAC because they exclude the bone tissues for the brain extraction. III. METHOD: The work we present in this paper is part of overall development combined PET/MRI system as an alternative to PET/CT system. We have developed an method for analysis of MRI images based on statistical textures approaches, and using a random forest algorithm for classification of the pixels in three classes (bone, air and others tissues). In MRI image air and bone does not produce any signal. We need more information for separate these two classes. Furthermore we required description more robust than the simple pixel value, and we will see in this respect the analysis of texture makes it possible to define a series of descriptors, with the very interesting performances. In order to extract the most significant aspects of MRI image, the textural properties are derived by using the first-order statistics and are computed from two different window sizes of neighbourhoods. As neighbourhoods of the pixel in the MRI, we selected 8 neighbourhoods where the pixel is a corner, and two neighbourhoods centred on it, as shown in Fig. 1. The normalized feature vector contains altogether 30 features.

2 does not give the desired results. Segmentation based on textural feature methods gives more reliable results. Therefore, rather than using the values of the neighbourhood, we chose to use the textural feature. We select the first-order features to compute the primitive texture. First order texture measures are statistics calculated from the original image values, like variance, and do not consider pixel neighbour relationships. In our method we use three texture measures: Figure 1. Distribution of used windows Neighbourhood The important aspect regarding bone tissue is that bone and air both yield no signal with conventional MR sequences, and skull morphology in the lower portion of the head is extremely complex, then we think that any method to extract the skull in the MRI images will have to take into account the anatomical information of the head (Fig. 2). We must use an atlas to describe the anatomical information, in our case the atlas is the dataset of training, and this dataset is a CT volume and a corresponding MRI volume. Furthermore the dataset of training will have to reflect the anatomical variability. For training the classifier we use tow volumes MRI and CT for the same patient. Figure 2. MRI and CT image for the same patient, bone and air are indistinguishable in MRI image A. Texture Feature Texture is an important property for the characterization and recognition of images. This fact is observed by the great amount of research involving; however, it is difficult to provide a formal definition for texture. Literature gives a variety of definitions. In a general way texture can be understood as a set of intensity variations that follow certain repetitive patterns [6]. Sharma in [7] mention that the texture-based analysis is extensively used in analysis of medical images, because segmentation based on gray level Mean: It is a measure of brightness. For, is pixel at location(r, s)., (1) Standard deviation: It is a measure of contrast, (2) Entropy: is a randomness statistical measure of log (3) Where p(b)=n(b)/n2 for {0 b L-1},where L is the number of different values which pixels can adopt, N(b) = number of pixels of amplitude (b) in the pixel window of size (n n). Texture features are calculated around each pixel in the specified neighbourhood. B. Random Forests The random forest is an increasingly used statistical method introduced by Breiman in It gives outstanding results in prediction for lots of diverse applications. It used for regression problems and for supervised classifications. They also succeed to handle very high dimensional data [9]. A random forest is an ensemble classifier consisting of many decision trees, where the final predicted class for a test object is the mode of the predictions of all individual trees. For a dataset consisting of N objects, each with M features, a value m << M is selected, and each tree grown as follows. To construct the training set for a tree, N objects are sampled at random with replacement. At each node in the tree, m features are randomly selected from the available M, and the node is partitioned using the best possible binary split. Each tree is fully grown without pruning [10]. Our approach for classification can be explained through the following steps:

3 1. The training step : The training dataset comprising an MR image and a corresponding CT image for the same subject is given. As the pre-processing, we registered the CT image to the corresponding MR image with SPM [8]. We have used simple gray level based thresholding technique to segment the CT image in three classes (bone, air and others tissues). Texture features are calculated around each pixel in the specified neighbourhood of the entire MR image in the training dataset. Classifier learning algorithm is trained to create model that could be used to classify MRI pixels in three classes. Construction of this model is achieved by training the classifier using a labelled subset of the data. The inputs data is the features texture vector, and the corresponding CT class is the targets data for training the classifier. 2. The classification step: Feature vector around each pixel in the specified neighbourhood of the MR image to be segmented and classified is computed. Random forest classifies the feature vector of each pixel of image. Overview of these steps is given in Fig3 and Fig4. Figure 3. The training step in our method Figure 4. The classification step IV. EXPERIMENTS AND RESULTS 1) Data set We applied our method to patient brain MRI data from the Vanderbilt Retrospective Registration Evaluation Dataset (RREP) [11]. In this database, the T1-weighted MR data were obtained using a Magnetization Prepared Rapid Gradient Echo (MP- RAGE) sequence. This is a rapid gradient-echo technique in which a preparation pulse is applied before the acquisition sequence to enhance contrast. The dimensions of the MR data were 256 x 256 x 128 with resolution on the average of 0.98mm x 0.99mm x 1.484mm. The corresponding CT scans had 3mm slice thickness with slice dimensions 512 x 512, with resolution on the average of 0.419mm x 0.419mm. The number of slices for each volume varied between 42 and 49. First, we registered the CT to the corresponding MR images using SPM [8]. After aligning the volumes, by using thresholding we labelled the bone, air and others tissues in the CT data sets. The random forest is used as classifier; we used the algorithm with 50 decision trees. The performance of most classifiers can be improved by increasing the size of the training dataset. To represent more variability of MRI data we have used tow MRI/CT pairs as training dataset, and we have used the others volumes for the test. 2) Performance assessment metrics As evaluation metrics we use the Dice similarity index (DSI) and the Jaccard similarity index (JSI). The DSI have been used as performance assessment metrics in many skulls stripping algorithms.the goal is to quantify the intersection of the method s output with the ground true masks [5]. The Dice similarity index is computed as follows

4 ! $ % &$ ' (4) $ % ( $ ' Where ) * is the test mask (the result), and ) + is the ground true mask. Jaccard is another commonly cited metric defined as, $ % &$ ' $ % -$ ' (5) However In order to evaluate the performance of the segmentation method, several authors define others metrics to evaluate the segmentation results [3] [12]. 3) Quantitative Result In order to extract the most significant aspects of MRI image, the textural properties are computed from two different window sizes of neighbourhoods. We tested different sizes of window and the results are specified in the Fig. 5. The choice of size at 12 pixels is retained for the first window (this value maximise the result of system), and the other window is fixed at 5pixels. 0, , , ,65000 Figure 5. The mean Dice calculate with different windows sizes The CT volumes from the data set give the ground truth of the skull images; it can be used to evaluate our segmentation results. Eight volumes from the database were used to perform statistical test; and the calculated Dice and Jaccard similarity index for the data set is given in Table1. TABLE 1: AVERAGE DICE AND JACCARD SIMILARITY INDEX OF THE PROPOSED METHOD FOR 8 MRI VOLUMES FROM RREP DATA SET DSI JSI Patient1 Patient2 Patient3 Patient4 Patient5 Patient6 Patient7 Patient Average ) Qualitative Result We show 2 volumes result from test data set. The qualitative results are given in Fig. 6, Fig. 7 and Fig. 8. The generated classified image exhibits a high visual similarity to the segmented CT image. Just using the local patch may fail if the patch describing the neighborhood of the pixel of interest is not characteristic enough for a certain CT value. This might lead to prediction of tissue classes that are highly unlikely to occur at the pixel of interest position, neglecting global information. For example, some methods might predict bone tissue in the middle of the brain. Our method presented acceptable results. These results proved that our model contain sufficient information to uniquely determine class tissue of MRI pixels. V. CONCLUSION In this paper we have proposed a new method for MRI segmentation. It uses the first order texture measures. We defined a set of neighbourhood, which offer a better separation of the skull in MRI images. We used random forest as classifier; it is a powerful classifier of big data, and the proposed method is qualitatively and quantitatively evaluated with the real data set. Initial results show the power of our model to classifier the Skull in the MRI image. The Interest in the results images is in their application to the MRAC. We need to test the results with PET data and this is work in progress. ACKNOWLEDGMENTS This work is supported by LITIO Laboratory, Oran University. BP 1524, El-M'Naouer, Oran, Algeria References [1] Hofmann and al. MRI-Based Attenuation Correction for PET / MRI: A Novel Approach Combining Pattern Recognition and Atlas Registration.The Journal Of Nuclear Medicine. Volume 49 - No. 11. November (2008). [2] MR-Based PET Attenuation Correction for PET/MR Imaging. Semin Nucl Med 43: (2013) [3] Xiaofeng Yang and al. A Skull Segmentation Method for Brain MR Images Based on multiscale Bilateral Filtering Scheme. Medical Imaging. (2010). [4] A. Santos and al. Skull segmentation of UTE MR images by probabilistic neural network for attenuation correction in PET/MR. Nuclear Instruments and Methods in Physics Research (2013) [5] Andre G.R. B. and al. Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRI. Computers in Biology and Medicine (2012) [6] Otavio A. and al. Comparative Study of Global Color and Texture Descriptors for Web Image Retrieval. Journal of Visual Communication and Image Representation. September 16, (2011) [7] Sharma N and al. Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network. J Med Phys. 33(3): (Jul 2008) [8] SPM software available online: [9] R. Genuer and al. Random Forests based feature selection for decoding fmri data. Proceedings of the 19th COMPSTAT pp (2010)

5 [10] Katherine R. Gray et al.random Forest-Based Manifold Learning for Classification of Imaging Data in Dementia. Machine Learning in Medical Imaging Volume 7009, pp , (2011). [11] RREP data set available online: [12] Rosniza R., Nursuriati J., Rozi M.- Skull Stripping of MRI Brain Images using Mathematical Morphology. IECBES 2010 Kuala Lumpur, Malaysia. (2010). [13] S. Ghadimi.Segmentation of Scalp and Skull in Neonatal MR Images Using Probabilistic Atlas and Level Set Method. 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada, August 20-24, 2008 [14] M Daliri and al. Skull Segmentation in 3D Neonatal MRI using Hybrid Hopfield Neural Network.32nd Annual International Conference of the IEEE EMBS.Buenos Aires, Argentina (2010) [15] B. Dogdas. Segmentation of skull in 3D human MR images using mathematical morphology. Hum Brain Mapp Dec;26(4): (2005) Figure 6. Comparison between Our Method segmentation and CT segmentation for data set 1: MRI image (Left) Result Image(Middle), and Original CT (rigth). Figure 7. Comparison between Our Method segmentation and CT segmentation for data set 2: MRI image (Left) Result Image(Middle), and Original CT (rigth). Figure 8. Comparison between Our Method segmentation and CT segmentation data set 3: MRI image (Left) Result Image(Middle), and Original CT (rigth).