International Journal of Neural Networks and Applications, 4(1), 2011, pp. 13-18
A SURVEY ON METHODS OF FULLY AUTOMATED MR BRAIN IMAGE SEGMENTATION R. Shantha Selva Kumari1 and R. Meena Prakash2 2
1 Mepco Schlenk Engineering College, Sivakasi, India, E-mail:
[email protected] P. S. R. Engineering College, Sivakasi, India, E-mail:
[email protected]
Abstract: This paper presents a detailed survey on the currently available frame works of automated brain image segmentation and the methods employed in the frame work for tissue classification, bias field correction and partial volume effect correction. The frame work of automated MR brain image segmentation can be broadly divided into model based segmentation frame work , Fuzzy Connectedness segmentation frame work , Deformable model based frame work and hybrid approaches. The methods employed in the framework which are discussed here include atlas registration used for initializing the segmentation, thresholding, Expectation Maximization algorithm for bias field correction, Markov Random Field model for incorporating contextual information and Artificial Neural Network for clustering. Keywords: Fuzzy Connectedness, Expectation Maximization, bias field, Markov Random Field, Deformable model, Artificial Neural Network
I.
INTRODUCTION
Magnetic resonance imaging (MRI) is a noninvasive imaging technique since no ionizing radiation is employed. It gives three dimensional (3D) information about the human soft tissue anatomy [1]. Magnetic Resonance Images of the brain are highly significant in the treatment of brain diseases such as Multiple Sclerosis, Schizophrenia, epilepsy and others. Segmentation of brain tissues into white matter, gray matter and cerebrospinal fluid plays a crucial rule in diagnosis of the brain disorders. For example, quantification of white matter lesions is needed for the treatment of multiple sclerosis. Volumetric analysis of the different tissue classes is crucial for the treatment of Schizophrenia and epilepsy. Manual segmentation is time consuming and prone to significant intra- and inter- observer variability thereby reducing the precision of the analysis of the segmentation. Therefore, fully automatic, highly accurate, and robust tissue segmentation technique is an invaluable tool for treatment of neurodegenerative diseases. In this paper, we review the state of the art technologies in the automatic segmentation of MR brain images and provide a scope for future research work in this area.
II. MRI SEGMENTATION CONCEPT A. MRI Principles MRI is based on the principles of Nuclear Magnetic Resonance (NMR). Nuclei with an even number of protons and neutrons have no net spin or angular momentum while nuclei with an odd number of protons and neutrons possess a net spin. The nucleus of hydrogen atom is made up of only a single proton and possesses a net spin. The hydrogen atoms are abundant in the fat and water content of human body. An angular moment occurs due to the net spin of the nucleus around its axis. Due to the rotating proton, a current loop is also created which results in magnetic field. Due to this magnetic field and the angular moment, the proton exhibits a magnetic dipole moment parallel to the rotation axis. Under normal condition, the magnetic dipole moments are randomly oriented and hence the net magnetic field will be zero. When placed in a magnetic field, the proton with its magnetic dipole moment is subjected to precession around the field axis. The frequency of this precession is called the Larmor frequency and is directly proportional to the strength of the magnetic field i.e., vo = γBo where Bo is the main magnetic field strength and γ is called gyro magnetic ratio whose value is 42.56 MHz/ Tesla. The application of a magnetic field Bo would create a net equilibrium magnetization Mo per cubic
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centimeter which is aligned to the Bo field. Mo is very much weaker than Bo and hence not directly observable. By perturbing Mo away from Bo field axis with an appropriate RF pulse having a frequency equal to larmor frequency, a longitudinal magnetization component M L and a transverse magnetization component MT is produced. When the RF pulse is turned off, the longitudinal magnetization component ML recovers to M0 with a relaxation time T 1 and the transverse magnetization component MT de phases and decays to zero with a relaxation time T2 . During relaxation, the protons lose energy by emitting their own RF signal with the amplitude proportional to MT. This signal is called Free Induction Decay(FID) response signal. The FID response signal is measured by an RF coil placed around the object being imaged. In Magnetic Resonance Imaging, linear gradient field is employed to get spatial information, Three tissue parameters decide the quantitative description of the MR signal: the proton density which determines Mo and the relaxation times T1 and T2. The RF pulse is repeated at a predetermined rate called repetition time, TR. The time between which the RF pulse is applied and the response signal is measured is the echo delay time TE. With proper selection of TR and TE, the MR image can be made to contrast different tissue types. B. Problems Associated with MR Brain Image Segmentation Partial Volume effects are the most commonly occurring artefacts in MR brain images where multiple tissue types contribute to a single pixel, resulting in a blurring of intensity across boundaries. PVA (Partial Volume Averaging) artefacts occur due the limited resolution of the imaging device and due to this, fine anatomical structures are lost in the image. Partial volume effects in MRI can be overcome by soft segmentation that allows regions or classes to overlap. Membership functions are defined for soft segmentation as 0 ≤ mk(j) ≤ 1 for all j, k (1) Σmk(j)=1 for k = 1,...K and for all j (2) The value of a membership function mk(j) can be interpreted as the contribution of class k to location j. Therefore, if the membership values are greater than zero for two or more classes, they are overlapping. Membership functions can be derived by fuzzy clustering and classifier algorithms. Soft
International Journal of Neural Networks and Applications
segmentations can be converted to hard segmentations by assigning a pixel to its class with highest membership value. Another difficulty associated with the segmentation of brain images is the intensity inhomogeneity artefact. The INU (Intensity Non Uniformity) artefact arises due to in- homogeneity in the magnetic field, and exists as an unwanted low frequency bias term modulating the signal. This causes a shading effect to appear over the image. Methods that simultaneously segment the image and estimate the in-homogeneity give good results than the methods that employ pre filtering operation which removes the in-homogeneity before actual segmentation. All MR images are affected by random noise. The noise comes from the stray currents in the detector coil due to the fluctuating magnetic fields arising from random ionic currents in the body, or the thermal fluctuations in the detector coil itself. This may cause errors in tissue segmentation. C. The MRI Tissue Segmentation Problem An image can be modeled as the union of c homogeneous regions Ak, A = Uk=1,..c (3) where each homogeneous region is specified by Ak(x, y) = pk+nk
(4)
where p k represents signal intensity and n k represents additive, zero mean random noise component. Figure (1) represents the MR brain image segmentation into WM,GM and CSF. The image formation process in MRI can be modelled as s(x) = o(x)b(x) + n(x)
(5)
where s(x) is the measured MR signal, o(x) is the true signal emitted by the tissue, b(x) is the unknown smoothly varying bias field, and n(x) is an additive noise assumed to be independent of b(x). Accurate segmentation of an MR image thus requires an accurate estimation of the unknown bias field b(x) and removing this bias field from the measured MR signal prior to or during segmentation. Using the estimated b(x), the log transformed true signal can be recovered as, log o(x) = log s(x) – log b(x)
(6)
A Survey on Methods of Fully Automated MR Brain Image Segmentation
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The intensity of a voxel belonging to class k is normally distributed around a mean µ k , with variance σ2 grouped in θk ={µk, σ2}. The overall model parameters is indicated by Φy = {θ1, …. θk, C} where C denote the set of bias field parameters. The bias field in MR is modelled as a multiplicative effect. Hence it is worked on log-transformed intensities, which makes the bias additive. Thus, three steps are involved in this frame work, classification of voxels, estimation of the normal distributions and estimation of the bias field. The Expectation Maximization algorithm is used to estimate the maximum likelihood (ML) parameters Ö by iteratively estimating the hidden data z based on the current parameter estimation Φ and recalculating Φ that maximizes the likelihood of the complete data.
Figure 1: WM,GM,CSF Segmentation of T1 Weighted Image (a) Input Image (b)White Matter (c) Gray Matter (d) CSF
III. METHODS In this section, we present a review of different approaches for fully automated MR brain image segmentation under three frame works, namely model based segmentation framework, deformable model based segmentation framework and fuzzy connectedness segmentation framework. The implementation and significance of each method is analyzed. A. Model Based Segmentation Framework In the model based segmentation framework, the MR signal is modelled as a realization of a random process with a parametric probability distributions that is corrected by a smooth polynomial inhomogeneity or bias field. [2][3][4]. The hidden segmentation z is modelled as the realization of a random process with some probability density function f(z/Φ z ) that is parameterized by the parameter set Φ z. z has generated the observed intensities y with probability density function f(y/z, Φy) parameterized by Φy. Estimation of the segmentation z is straightforward once the model parameters Φ ={Φ y , Φ z} are known. Both the segmentation and the model parameters can be estimated simultaneously by interleaving the segmentation with estimation of model parameters.
The independent model explained above classifies the voxels based on their intensity only. This yields acceptable segmentation results as long as the different classes are well separated in intensity feature space. But in brain tissues, voxels surrounding the brain show an MR intensity that is very similar to brain tissue. This results in erroneous classifications of small regions surrounding the brain as gray matter or white matter. Hence spatial and anatomical constraints are incorporated by introducing the Markov Random Field. The difference lies that in the independent model each voxel had the same a priori probability to belong to class k, whereas now this probability depends on the classification of the neighbouring voxels. The prior classification is derived from a digital brain atlas that contains spatially varying prior probability maps for the location of white matter, gray matter and CSF. The use of atlas avoids interactive user intervention to initialize the algorithm and make the method fully automated. B. Deformable Model based Segmentation Framework Deformable models are model-based techniques for delineating region boundaries by using closed parametric curves or surfaces that deform under the influence of internal and external forces[5].To delineate an object boundary in an image, a closed curve or surface must first be placed near the desired boundary and then allowed to undergo an iterative relaxation process. Internal forces are computed from within the curve or surface to keep it smooth throughout the deformation. External
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forces are usually derived from the image to drive the curve or surface toward the desired feature of interest. The main advantages of deformable models are their ability to directly generate closed parametric curves or surfaces from images and their incorporation of a smoothness constraint that provides robustness to noise and spurious edges. A disadvantage is that they require manual interaction to place an initial model and choose appropriate parameters. Deformable models may be parametric deformable models or Level set deformable models and are classified as geometric active contours, gradient based level set active contours, Geodesic Active contours depending upon the method for contour extraction. A detailed survey is found in [10]. In [7], a three stage method is employed to segment images. The first stage, Segment Head, uses intensity histogram analysis to remove background noise and provide a head mask defining the head. The second stage Generate Initial Mask produces a mask that approximately identifies the intracranial boundary. A head image is filtered using a nonlinear anisotropic diffusion filter, to identify regions corresponding to brain. The nonlinear anisotropic diffusion effectively counters RF inhomogeneity by smoothing the brain regions and by reducing the intensity of the narrow non brain regions such as the scalp. With the initial brain mask as seed, the third step, Generate Final Brain Mask, locates the intracranial boundary using an active contour algorithm. The active contour model algorithm consistently tracks the edge of the brain, even in the presence of partial volume effects. This method effectively provides fully automatic intracranial boundary detection algorithm which can be used for studies of Multiple Sclerosis Lesions. The points in the contour iteratively approach the intracranial boundary through the solution of an energy minimization problem. M3DLS algorithm[11], a multiphase 3D Level Set Algorithm utilizes a multi phase extension of the region based deformable model based on the Mumford-Shah functional by iteratively deforming two closed curves separating four regions. An automated MR brain tissue segmentation that integrates both geometric and statistical image features into edge-based deformable model formulation is employed [8]. The advantage of this method is the stabilization of the active contour. In this approach, first the Gaussian Mixture EM parameters are estimated such that each mixture
International Journal of Neural Networks and Applications
distribution represents one single class. Based on these estimated distributions, the normalized posterior probability of each voxel is calculated. The hybrid geometric-statistical feature is derived by combining both the voxel statistics and the image gradient information. To initialize the active contour, a voxel threshold is introduced. The hybrid feature and the contour initialization are determined individually for each tissue to be segmented and each contour is then propagated independently. C. Fuzzy Connectedness Segmentation Framework The fuzzy connectedness (FC) works on voxel basis and therefore can better segment objects with irregular or complex shapes and hence better suited for brain MRI. The FC method takes simultaneously into consideration the degree of space adjacency, degree of intensity adjacency, and degree of intensity gradient adjacency between two voxels. In the fully automated method of brain image segmentation proposed in [12], four steps are included. In Atlas registration, [18] the pre segmented atlas is registered onto the MRI through a rigid registration method. In FC Segmentation, with the registered atlas as initial segmentation , the brain tissues are segmented. The prior information of tissues are specified which include (1) the intensity probability distribution (2) the intensity difference probability distribution (IDPD) (3) initial seeds. In PABIC correction, the INU artefacts are estimated and corrected. The PABIC (Parametric Bias Field Correction) method employs Gaussian components decomposition algorithm for decomposing the intensity histogram of tissue into number of Gaussian components and choosing the one with the largest amplitude to represent the tissue. Then Re-FC Segmentation is done by taking the PABIC corrected MRI as the subject and the FC-segmented MRI as the initial segmentation. Fuzzy clustering algorithm requires a priori knowledge about the number of clusters, nature of data, clustering criteria and so on. Histogram of MR images may be used to determine the number of clusters but they fail for diseased cases. In [14] Artificial Neural Network (ANN) is employed to find out the exact number of clusters and FCM algorithm is used in the next stage for segmentation. Neural Networks have the ability of doing parallel operation and they simulate biological learning.
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A Survey on Methods of Fully Automated MR Brain Image Segmentation
D. Other Methods Water shed segmentation method is a popular method which may be used for MRI segmentation. But the drawback here is over segmentation. Hence in [17] merging process for the over segmentation is done using Fuzzy C Means Clustering algorithm following the water shed segmentation. K Nearest Neighbour (KNN) classifier is employed to partition the regions that needed re-segmentation. Hybrid approaches of level set method with fuzzy segmentation and level set method with atlas based segmentation show better segmentation results [15, 16]. IV. DISCUSSIONS AND FUTURE CHALLENGES Automated segmentation of MR brain images is a supportive tool for the doctors in the diagnosis of brain diseases such as schizophrenia, Alzheimer’s disease, epilepsy etc., The problem of segmenting MR Brain image into White Matter, Gray Matter and CSF has been addressed using various approaches. Manual intervention is required in many of these methods. The foremost requirement is fully automated method without any external intervention. The main challenges in MR brain image segmentation are Partial Volume Effect, Intensity In homogeneity artefact and random noise. Most segmentation methods need initialization for which atlas registration with known set of labels can be prescribed. From the survey, it is observed that each method is having its own advantages and drawbacks as listed out in the table. The future scope lies in combining the various methods leading to hybrid approach which adapts the advantages of each method involved and overcoming the drawbacks. Future research should also focus on the computation time, reduction in complexity and accuracy of the segmentation results. V. CONCLUSION In this paper, the various existing methods for fully automated MR brain image segmentation have been meticulously analyzed and a detailed survey report has been given. Based on the study, it is observed that the most applicable methods for MR brain image segmentation can be listed under three categories –model based statistical approach, deformable model based method and fuzzy connectedness segmentation. A serious discussion has also been made of the hybrid approach
Table I Overview of MR Brain Image Segmentation Frameworks Segmentation Advantages Frame work
Limitations
Supportive methods employed
Model Based The method (2,3,4) overcomes the drawback of spectral overlap of MR intensities of different tisse classes which is predominant in intensitybased methods.
The method requires initialization of the classification process.
Markov Random Field is employed to incorporate contextual information. Atlas initialization is done.
Deformable Model Based (7,10)
The method yields a nice representation of the tissues and boundaries. It also simplifies optimization.
Segmentation result relies on the initial contour placement.
Hybrid geometricstatistical feature is derived for each tissue to be segmented.
Fuzzy Conn- The method ectedness provides (12,14) reduced Computational complexity and better suited for complex brain structures.
The method tends to be affected by Intensity NonUniformity artifact.
Atlas Registration is used for initialization and PABIC method is adapted to correct the INU artifacts.
Hybrid Approach (15,16,17)
Computational Complexity is involved.
Based on the requirements, the above said supportive methods may be employed.
Combines the advantage of each method involved
employing combination of several methods. This paper is based on the number of research articles published in the previous decade. The references cited in bibliography provide a good comprehension of current trends and future scope in fully automated MR Brain Image Segmentation. The further research focuses in construction of hybrid methods which acquire advantages of each method and at the same time reducing the computational complexity, improving the accuracy and avoiding the human intervention.
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International Journal of Neural Networks and Applications
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