Medical Image Segmentation Techniques: An Overview

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International Journal of informatics and medical data processing (JIMDP) vol.1, no.1, pp16-37, 2016. Homepage: http://www.sci-coll.com/index.php/IJIMDP

Scientific Collection Publishing

Medical Image Segmentation Techniques: An Overview E.A.Zanaty and Said Ghoniemy College of Computers and Information Technology,Taif University, Saudi Arabia. E-mail: [email protected]; [email protected] Abstract: Medical image segmentation provides rich information in clinical applications for supporting the advancement in the biomedical knowledge and to guide surgery. This paper reviews the most relevant medical image segmentation techniques and discusses the future of image segmentation methods in biomedical research. The use of image segmentation in different imaging modalities is also described along with the difficulties encountered in each modality. These techniques are grouped into eleven categories. The advantages and disadvantages of the different approaches are summarized. This work guides the researchers to the limitations of each method in order to develop necessary computational algorithms that enhance the analysis of biomedical data in various clinical applications such as diagnosis. Keywords: Medical image segmentation; Thresholding; Watershed transformation; Region growing (RG); Clustering; Active contour models (snakes); Level set techniques; Graph cut; Genetic evolutionary algorithms.

1. Introduction Image segmentation plays an important role in a variety of other applications, such as robot vision, object recognition, and medical imaging. The goal of the segmentation process is to define areas within the image that have some properties that make them homogeneous. The definition of those properties should satisfy the general condition that the union of neighboring regions should not be homogeneous if we consider the same set of properties [1-2]. The basic idea of segmentation can be described as follows [3]. Given a set of data X={x1,x2,…,xn} and a uniformity predicate P, we wish to obtain a partition of the data into disjoint nonempty groups {C1,C2,…,Ck} subject to the following conditions:

(1)  ki1 Ci  X (2) Ci  C j   ,

i≠j

(3) P(Ci)=TRUE, i=1,2,…,k (4) P(Ci  Cj) = FALSE, i≠j The first condition ensures that every data value must be assigned to a group, while the second condition ensures that a data value can be assigned to only one group. The third and fourth conditions imply that every data value in one group must satisfy the uniformity predicate while data values from two different groups must fail the uniformity criterion. MRI and other medical images contain complicated anatomical structures that require precise and most accurate segmentation for clinical diagnosis. There are many papers in the literature reviewing the various segmentation algorithms [3-4]. Fu and Mui [4] categorized the image segmentation according to three criteria, (1) characteristic feature thresholding or clustering, (2) 16

E.A.Zanaty and Said Ghoniemy edge detection, and (3) region extraction. Pham et al. [2] presented a critical appraisal of the semiautomated and automated methods for the segmentation of anatomical medical images up to that date. Liew and Yan [5] presented an overview of the most popular medical image segmentation techniques and discussed their capabilities, advantages and limitations. In this paper, we examine the segmentation methods that have a generalized scope and are the basis of most of the medical image segmentation techniques today. In contrast with other surveys that only describe and compare different approaches qualitatively, this study provides both qualitative and quantitative comparisons. In addition, as a starting point, the lessons learned from this medical segmentation comparison can also be generalized to other image segmentation domains. Our research spans a wider range of segmentation methods covering the key classical and the rather more recent methods that have enjoyed noticeable success in that field. The rest of this paper is organized as follows: In section 2, the medical image segmentation is described. The techniques of medical image segmentation are grouped into eleven categories in section 3. The conclusions and future trends are presented in section 5.

2. Medical image segmentation The use of digital images in medicine has become very popular these days. All diagnostic imaging devices are computerized and can manipulate digital data. This includes: x-ray, computed tomography (CT), positron emission tomography (PET), single-photon emission computed tomography (SPECT), ultrasound and magnetic resonance imaging [6]. All those devices can manipulate images, perform measurements and export it in a digital way. A natural need for image segmentation comes from various analyses and measurements that can be done, for example, to detect pathologies. The different acquisition modalities, the different image manipulations and variability of organs all contribute to a large variety of medical images. It can be safely said that there is no single image segmentation method that suit all possible images. Furthermore, the expert user can decide to draw part of an object, a cluster of objects or an object with margin, and may request that the segmentation border passes where there are no edges. This can pose great problems for any segmentation method. A more extensive use of image segmentation comes from medical therapy devices. Procedures such as minimal invasive surgeries (e.g: guided surgeries) and non-invasive surgeries (e.g: x-Ray radiation, focused Ultrasound, etc.) become more and more popular and require detailed computerized feedback to compensate for the lack of direct visual contact by the surgeon. In many such systems the user has to segment some object(s) in order to prepare the treatment plan as well as to avoid hurting other sensitive organs. A good interactive segmentation tool can save a lot of time, replacing manual drawing by the surgeon. Medical images tend to suffer much more from noise than realistic images due to the nature of the acquisition devices. This of course poses great challenges to any image segmentation technique. In order to reduce noise, many devices increase the (already existing) partial voluming, that is, they average acquisition on a thick slice. This (among other reasons) leads to blurring the edges between the objects, which make decisions very hard for automatic tools.

3. Image Segmentation Categories Extensive work has been done in the field of image segmentation, following multiple ways to categorize a segmentation technique ([3], [7]). Image segmentation techniques may be divided to automatic and semi-automatic techniques or bottom-up and top-down segmentation techniques. Automatic segmentation techniques takes no user input, example for fully-automatic methods is split and merge. Semi-automatic segmentation techniques require some form of user interaction, whether it is to provide additional information about the data, or to evaluate the results. Semiautomatic methods vary from "initialize and let go" (level sets, [8]), through specifying "constraints" (graph cuts, [9]; snakes, [10]), to "full-guidance" through-out the process (live wire [11]). The following subsections categorize the image segmentation methods into eleven groups: (1) thresholding techniques; (2) watershed transformation; (3) region growing (RG) techniques; 17

E.A.Zanaty and Said Ghoniemy (4) clustering techniques, (5) active contour models (snakes), (6) level set techniques, (7) graph cut techniques, (8) genetic, (9) artificial intelligence, (10) hybrid and (11) others algorithms.

3.1 Thresholding technique Thresholding is often used as an initial step in a sequence of image processing operations [12]. Its main limitations are that in its simplest form only two classes are generated and it cannot be applied to multi-channel images (see Figure 1). In addition, thresholding typically does not take into account the spatial characteristics of the image. This causes it to be sensitive to noise and intensity inhomogeneities, which can occur in medical images. Both these artifacts essentially corrupt the histogram of the image, making separation more difficult.

Figure 1: Segmentation by thresholding technique (threshold=0.4) [Taken from Sahoo et al. [12]]. There are several methods for selecting a threshold: Otsu's algorithm [13] was a chosen threshold approach by analysing the distribution of gray values of an image. The minimum between the two peaks in a bimodal histogram is chosen as a threshold. A threshold is computed to lie between the means of fore and backgrounds, but selecting a local minimum threshold is affected by a partial volume effect which makes it undetectable. In general, a single threshold cannot give good segmentation results over an image. Li et al. [14] proposed that the use of two dimensional (2D) histograms of an image is more useful for finding thresholds for segmentation rather than just using grey level information in one dimension. Huang and Chau [15] proposed a thresholding method based on Gaussian mixture model. The optimal number of mixtures (Gaussian function) is searched for the candidate by the expectation maximization algorithm. The optimal threshold has been determined as the average of these means. Maitra and Chatterjee [16] proposed a novel optimal multilevel thresholding algorithm for brain MRI segmentation, see Figure 2. This optimization algorithm, employed for image histogram-based thresholding, was based on a relatively recently proposed evolutionary approach, namely, bacterial foraging.

Figure 2: Original image (left), 3 region (middle), and 5 region (right) segmentation results, [Taken from Maitra and Chatterjee [16]]. Rogowska [17] used thresholding technique to divide an image into groups of pixels with values less than the threshold, and groups of pixels with values greater than or equal to the threshold 18

E.A.Zanaty and Said Ghoniemy value. However, this technique is not widely used without preprocessing algorithm because of their sensitivity to noise. Zhang et al. [18] proposed an automatic, fast, robust and accurate method for the segmentation of bone using 3D adaptive thresholding. An initial segmentation was first performed to partition the image into bone and non-bone classes, followed by an iterative process of 3D correlation to update voxel classification. Vijay et al. [19] presented a waveletbased multiscale products thresholding for MRI. In the first stage, the noisy image was passed through filtering and some amount of noise was reduced but the image become blurred, hence adaptive wavelet thresholding was applied with multiscale product rule in the second stage. For more thresholding techniques, see [20-21]. Thresholding methods can perform well on simple images with bimodal intensity distribution but fails on medical images which do not have bimodal distribution, of intensity. In this case, thresholding result cannot partition the images into various anatomical structures correctly. Also this kind of segmentation neglects all spatial information of the image and is quite sensitive to noise.

3.2 Watershed transformation Watershed transformation has increasingly been recognized as a powerful segmentation process due to its many advantages [22-23], including simplicity, speed, and complete division of the image. Even with target regions having low contrast and weak boundaries, watershed transformation can always provide closed contours. A detailed review of algorithms that make use of the watershed transformation for image segmentation can be found in [23-24]. Some important draw-backs also exist, and they have been widely treated in the related literature [25]. Among the most important are: oversegmentation, sensitivity to noise, poor detection of significant areas with low contrast boundaries, and poor detection of thin structures (see Figure 3). Some researchers employed diffusion algorithms before running the watershed transform because watershed is very sensitive to noise [26-28]. Other approaches used probability theory to compute the landscape of the image or use different strategy to compute the saliency of merge to merge the watershed images [26]. Most of these approaches focused on the oversegmentation problem and it is widely agreed that the time and memory cost of watershed is hard to decrease because of its birth from region based.

Figure 3: Original image (left), oversegmentation of watershed transformation (right) results, [Taken from Zanaty [27]]. Peng et al. [29] presented a novel implementation of watershed transform using multi-degree immersion simulation, which is originally proposed by Vincent and Soille [22]. Hsieh et al. [30] presented an approach for small moving object detection by using watershed-based transformation. In order to improve the detection results, a noise removal technique is first applied to remove the noise from the image and improve the image quality. Frucci and Baja [31] suggested a segmentation technique based on the use of the watershed transformation to partition the image into homogeneous regions, and on the successive assignment of the regions to either the foreground or the background. Hamarneh and Li [32] proposed a method for enhancing watershed segmentation by utilizing prior shape and appearance knowledge. They handled the oversegmentation problem by clustering and merging appropriate watershed segments. Zhang and Cheng [33] presented a segmentation approach that combines watershed algorithm with graph theory. Smaoui and Masmoudi [34] improved the watershed technique by introducing a histogram 19

E.A.Zanaty and Said Ghoniemy driven methodology. However, Zanaty [27] introduced a method that is based on combination of watershed segmentation, histogram, and seed region growing to resolve the weakness of each method while Zanaty and Afifi [28] presented watershed approach based on seed region growing and image entropy. Although these watershed methods achieved satisfactory results, there are still some problems such as oversegmentation and sensitivity to noise.

3.3 Region growing (RG) technique Region growing (RG) technique has been applied to MRI segmentation Heinonen et al. [35] to develop a semi-automatic MRI segmentation algorithm that employs simple RG technique for lesion segmentation. Methods can be categorized into criterion selection based on gray-level properties and segmentations using different homogeneity criterion. Criterion selection based on gray-level properties of the current points is dependent on seed point location and search order ([36]). They are concerned with selecting a pixel for image segmentation and its improvements. The first method is called "seeded region growing" and it is introduced by Adams and Bischof [37] and the improved version of this algorithm was proposed in [38] which often determines the final segmentation results by subsequent region grow. As described in [39], the seeded region growing (SRG) has two inherent pixel order dependencies that cause different resulting segments. The first-order dependency occurs whenever several pixels have the same difference measure to their neighbouring regions. The second-order dependency occurs when one pixel has the same difference measure to several regions. Segmentations using different homogeneity criterion is used to stop growing outside a region in region growing method ([40]). These methods are often slow because of the large number of segmentations that require distinguishing the true result from segmentations with slightly different homogeneity criteria. Zanaty and Asaad [41] presented a region growing (RG) approach based on a homogeneity threshold to improve region growing. Although the approach works well with high noise images and achieves similar results as Del-Fresno et al. [42] in case of low noise levels, it is suffers misclassification tissues of images that include weak boundaries.

A)

B)

Figure 4: A) A mammogram image, B) segmentation result after the first iteration of the topdown region dividing based approach [Taken from Wu et al. [36]]. Figure 4(A) is a mammogram image. Figure 4(B) is the segmented result after the first iteration of the top-down region dividing based approach. The image is segmented into three regions marked in dark-gray, gray, and light-gray indicating background, soft tissue and breast areas, respectively. Figure 5(a) shows a typical chest CT. The left and right lungs with low intensities are well separated by the mediastinum in the middle. Using the Wu et al. [36] method with one divided iteration yields Figure 5(b), the three major regions are marked in dark-gray, gray, and light-gray indicating the lung, vessel and body areas, respectively. The profiles of the two lungs are delineated roughly as shown in Figure 5(c) by a simple thresholding to keep the light-gray area. 20

E.A.Zanaty and Said Ghoniemy The wide range of region growing based image segmentation techniques failed to find appropriate thresholds or seeds, in most cases largely because of unknown and irregular noise, inhomogenity, poor contrast and weak boundaries which are inherent to medical images.

Figure 5: a) Chest CT image, b) Using the Wu et al. [36] method with one dividing iteration, c) Two lungs are delineated roughly by a simple thresholding [Taken from Wu et al.[36]].

3.4 Clustering technique A cluster is therefore a collection of objects which are “similar” between them and are “dissimilar” to the objects belonging to other clusters. Clustering methods include soft clustering (e.g., Fuzzy c-means or FCM) and hard clustering (e.g., fuzzy k-means or FKM). The k-means clustering algorithm clusters data by iteratively computing the mean intensity for each class and segmenting the image by classifying each pixel in the class with the closest mean. Fuzzy c-means algorithm generalizes the k–means algorithm allowing for soft segmentations based on fuzzy set theory. Some methods are available to speed up hard k-means clustering [43]. Zanaty et al. [44] exploited this characteristic to develop a fast k-means clustering algorithm to valid the k clusters. Often, the value of k is assumed to be known based on prior knowledge of the anatomy being considered, see Figure 6. Many researchers have incorporated spatial information into the original FCM algorithm to enhance image segmentation and can be broadly grouped into two categories: similarity measure algorithms and modified FCM objective function [45-46]. A new similarity measure based on spatial neighborhood information was introduced for enhancing the FCM performance ([48-50]) and post-processing (noise cleaning on the classified data). Xue et al. [51] proposed an algorithm where they firstly denoise images and then classify the pixels using the standard FCM method. These methods can reduce the noise to a certain extent, but still have some drawbacks such as increasing computational time [47], complexity [50] and introducing unwanted smoothing ([49]). Modified FCM objective function adds penalty term into the objective function to constrain the membership values. Based on the traditional FCM objective function, most improved approaches embodied regularization terms to show the increased robustness of the classification of the noisy images. Ahmed et al. [47] introduced a neighborhood averaging additive term into the objective function of FCM and named the algorithm bias corrected FCM (BCFCM). Liew and Yan [46] introduced a spatial constraint to a fuzzy cluster method where the inhomogeneity field was modeled by a B-spline surface. The spatial voxel connectivity was implemented by a dissimilarity index, which enforced the connectivity constraint only in the homogeneous areas. This way preserves significantly the tissue boundaries. Szilágyi et al. [52] modified the FGFCM (MFGFCM) to improve the precision of segmentation. They proposed EnFCM algorithm to accelerate the image segmentation process. EnFCM is based on a simple fact about images, which is usually overlooked in many FCM-type algorithms. Cai et al. [49] introduced a new local similarity measure by combining spatial and gray level distances. They used their method as an alternative pre-filtering to EnFCM. They named this approach fast generalized FCM (FGFCM). 21

E.A.Zanaty and Said Ghoniemy Kang et al. [53] improved FCM with adaptive weighted averaging filter (FCM AWA) to suppress the noise and to enhance FCM accuracy. Wang et al. [54] incorporated both the local spatial context and the non-local information into the standard FCM cluster algorithm. These approaches could overcome the noise impact, but the intensity homogeneity cannot be handled at the same time. To address this problem, possibilistic clustering which is pioneered by the possibilistic c-means (PFCM) algorithm is developed in [55]. However, the robustness of PFCM comes at the expense of the stability of the algorithm. The PCM-based algorithms suffer from the coincident cluster problem, which makes them too sensitive to initialization [55]. Many efforts have been presented to improve the stability of possibilistic clustering [56]. Although some compatibility or similarity measure can be applied to choose the clusters to be merged, no validity measure is used to guarantee that the clustering result after a merge is better than the one before the merge. They still depend on a fixed spatial parameter which needs to be adjusted. Furthermore, the cost of estimating the neighbors for each point in an image is still high. Therefore, these drawbacks will reduce the clustering performance in real applications. The main disadvantages of the above algorithms are accuracy, misclassification in noise affected images and coincident clusters. Therefore, the standard FCM algorithm has proven to be problematic because medical images always include considerable uncertainty and unknown noise caused by operator performance, equipment, and the environment.

Figure 6: Segmentation of brain MRI, left: original image, right: segmentation using the 3-means algorithm [Taken from Zanaty et al. [44]].

3.5 Active Contour technique ("Snakes") The original snake was proposed as an interactive method, which requires expert guidance on the snake initialization and the selection of correct deformation parameters. It is important to understand several underlying concepts that identify limitations of the original snake method. First, the magnitude of the external force dies out rapidly when moving away from the image edges or boundaries. This implies that the capture range of the original snake is small. Secondly, image noise can cause the contour to be easily attracted to a local energy minimum, which does not correspond to the ground truth. Therefore, to reach the desired boundary, the initial contour should lie close to the desired boundary to avoid these hazards. Furthermore, the original snake method is a parametric method and the contour cannot change topology during its deformation process without an additional mechanism. With these limitations, a number of deformable contour methods have been proposed to improve the original snake, such as snake variations [57]. In [58], different edge-based external forces are proposed to overcome the sensitivity of the initial condition in [80] by enhancing the effect of image edges. The methods in ([58-60] provided different mechanisms to enable the contour topology to change during the deformation process. Besides the topological constraint, the author of [59] proposed algorithms to apply new physical 22

E.A.Zanaty and Said Ghoniemy constraints on the snake in order to control the contour geometry and deformation. The methods of [60-61] deform the contour with the constraint from a priori knowledge of the object shape, which helps the deformable contour avoid being trapped by spurious edges. In [62-63], the authors try to utilize region-based image features or combine them with the edge-based features as the external forces in order to overcome the image noise. However, Ronfard [62] method still requires the initial contour to be close to the desired boundary and it cannot handle the contour topological changes. Starting from multiple seeds, Zhu and Yuille [63] performs image segmentation on the whole image by doing boundary deformation and region merging iteratively; however, it cannot handle contour splitting topology change. In order to relieve the sensitivity to initialization and accurately locate the global minimum, dynamic programming approach was applied in [64-65] to replace the variational method to minimize the contour energy. These approaches also have the advantage of avoiding the estimation of higher order derivatives and improve the numerical stability. The active contours start with an initialized contour and actively deform themselves to the desired border while reducing the defined energy in each iteration until convergence. Guyader and Vese [66] had proposed a geodesic active-contour based model that globally integrates a topological constraint, see Figure 7. In Lei et al. [67], the comparative study to review eight different deformable contour methods (DCMs) of snakes and level set methods applied to the medical image segmentation was presented. Zhu et al. [64] presented a segmentation method based on gradient vector flow snake model. This approach has the advantage of avoiding the estimation of higher order derivatives. Deformable contour methods-based solutions are highly analytical and involve extensive numerical computations. All these factors make the solutions less intuitive for the practitioners and hard to compare the methods in terms of their applicability and computational requirements. Also, critical issues for any practical application of DCMs include complex procedures, multiple parameter selection, and sensitive initial contour location.

Figure 7: Obtained evolution when topological constraints are enforced. Iterations 0, 100, 250, 450, 720, 800, [Taken from Guyader and Vese [66]].

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3.6 Level set technique Level set was first introduced by Osher and Sethian [68], in fluid dynamics. Applying it to image segmentation was simultaneously suggested by [69] to tracks an evolving contour. As the original level set method [69] had no energy minimization, some researchers applied the level set formulation with a contour energy minimization in order to obtain a good convergence. In ([7073]) region-based image features are used alone or united with the edge-based features to construct the energy to minimize. The method in [72] handles image segmentation using only region-based image features with the assumption that only object and background exist in the image. In [73], the authors focus on supervised texture segmentation and require a priori knowledge of the object texture pattern. In [70], the contour energy minimization is formulated as a constrained optimization problem with a constraint indicating the degree of contour interior homogeneity. The methods in [74-75] integrate the a priori knowledge of the object shape into the level set formulation in order to constrain the contour deformation within an admissible range.

The initial contour

100 iterations

300 iterations

Figure 8: Result of variational level set method for an ultrasound image [Taken from Xu et al.[75]]. Li et al. [75] presented a new variational level set formulation that completely eliminates the need of the reinitialization as shown in Figure 8. The level set method can be easily implemented by using simple finite difference scheme and is computationally more efficient than the traditional level set methods. As shown in Figure 9, Bernard et al. [76] proposed a new formulation of active contours based on level sets, where the implicit function is modeled as a continuous parametric function expressed on a B-spline basis. This representation provides an overall control of the level set, and allows one to avoid the re-initialization step of the level set via the normalization of the Bspline coefficients.

Figure 9: Brain MRI (left), reference contours of WM (middle), segmentation by Bernard et al. [76] (right). Chan and Vese [72] proposed an active contour model based on Mumford–Shah segmentation technique and level set method. This technique is not based on an edge-function to stop the 24

E.A.Zanaty and Said Ghoniemy evolving curve on the desired boundary and it does not need to smooth the initial image, even if it is very noisy. In some cases, the locations of boundaries are well detected and preserved, but when this technique is applied to medical image to segment WM, it fails to detect it and only detect both WM and GM, see Figure 10.

Figure 10: Brain MRI (left), reference contours of WM (middle), segmentation by Chan and Vese, [103] (right) [Taken from Chan and Vese, [72]]. Truc et al. [77] presented a novel active contour model for medical image segmentation that was based on a convex combination of two energy functionals to both minimize the inhomogeneity within an object and maximize the distance between the object and the background. Barman et al. [78] proposed a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re-initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Li et al. [79] proposed a level set technique to integrate image gradient, region competition and prior information for CT liver tumor segmentation. Zhan et al. [80] proposed an improved variational level set approach to correct the bias and to segment the MRI with inhomogeneous intensity. They used a Gaussian distribution with bias field as a local region descriptor in two-phase level set formulation for segmentation and bias field correction of the images with inhomogeneous intensities. A disadvantage of active contour techniques is that they require manual interaction to place an initial model and choose appropriate parameters. Level sets propose a powerful geometric framework but the convergence of the process is not guaranteed and seems to depend on the starting point.

3.7 Graph cut technique Graph cut optimization has become well accepted in the area of early vision since it was proposed as an efficient way to minimize a larger class of energy functions [81]. A cut in a graph (see Figure 11c) is the subset of the edges that if removed, split the graph into two, see Figure 11d. The cost of the cut is measured by the sum of weights of the cut edges. The minimal cut is the cut with minimal sum-of weights out of all possible cuts. In particular, segmentation is scored according to the following criteria:  Each pixel inside the object is given a value according to whether its intensity matches the object’s appearance model; low values represent better matches.  Each pixel in the background is given a value according to whether its intensity matches the appearance model of the background; low values represent better matches.  A pair of adjacent pixels, where one is inside the object and the other is outside, is given a value according to whether the two pixels have similar intensities; low values correspond to contrasting intensities (i.e. to an edge).

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Figure 11: (a) User marks as foreground and backgrounds, O and B respectively, (b) t-links to terminal node T and for n-links are shown (t-links to foreground terminal S are equivalent). (c) Minimal cut. (d) Imposed segmentation (Taken from Boykov and Jolly [81]). In medical imaging applications, images consist mostly of a large background area with small but significant regions. Graph cut technique uses all pixels in the image even if the user does not need to extract them. The authors in [82-83] had used graph cut image segmentation techniques for medical images, see Figures 12-13.

Figure 12: Bone removal in a CT image. Bone segments are marked by horizontal lines [Taken from Boykov and Jolly[83]].

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Figure 13: Segmentation of cardiac MRI by graph cut using Boykov and Funka-Lea [82], original image (Left), initialization (Middle), segmentation by graph cuts (Right) [Taken from Boykov and Funka-Lea [82]]. Various problems as image segmentation image restoration [84], image synthesis [85], stereo and motion [86], can be solved by graph cuts. The optimality of graph cut minimization methods depends on the number of labels and the exact form of the smoothness term. Among all these, typical graph partitioning methods graph-cuts are comparatively new and the most powerful one for image segmentation. Chen et al. [87] proposed a non-rigid registration method combined with a semi-automatic liver segmentation method for MR-guided liver cancer surgery. These techniques have two problems: for the background seed pixels, we need to put seeds pixel in many tissues described as background. In addition each pixel in the image is used and described to belong to object or belong to background.

3.8 Genetic Algorithms GAs are efficient, adaptive, and robust search and optimization techniques guided by the principles of evolution and natural genetics, and have implicit parallelism [88]. A variety of different GA algorithms were also available in literature [89-90]. Lo Bosco [89] developed a GA to segment still images. The general segmentation problem has been treated as a set partitioning problem and the GA has been used to find an approximated solution. The algorithm has been applied to natural and medical images see Figure 14.

Figure 14: Segmentation by GA [Taken from Lo Bosco [89]]. Ghosh and Mitchell [90] developed GA to evolve a segmenting contour by incorporating both texture and shape information to extract objects without prominent edges, such as the prostate on pelvic CT images. Ali [91] presented an edge-based segmentation scheme using a GA for medical images segmentation. Ghosh [92] used GA to solve the difficulty of explicit energy function term in level set technique because it eliminates the energy function (and instead uses a fitness function) 27

E.A.Zanaty and Said Ghoniemy thereby providing a framework for incorporating high-level features and combining multiple features for segmentation. The main advantages of using GA for segmentation lie in their ability to determine the optimal number of regions of a segmentation result or to choose some features such as the size of the analysis window or some heuristic thresholds. The main challenges and issues in integrating GAs for solving the optimization problems in medical image segmentation are manifold. First, the encoding strategy must be suitably defined so that it conforms to the building block hypothesis. Any ad hoc encoding strategy may not follow this hypothesis, and GAs may often yield poor result in such situations. Since fitness computation is the most time-consuming part, its efficient design is crucial for a successful application of GAs. The choice of the different genetic operators as well as the termination criteria are also important issues in GAs. Often, these are tuned manually, and require a large amount of expertise as well as experience. Alternatively, the parameters can be kept variable and/or adaptive so as to be able to self-modify in response to the population statistics. Ways of avoiding premature convergence are also critical in any application of GAs. Another important consideration in medical image analysis is the reduction of the computation time of GAs, which is, usually, time-consuming in nature.

3.9 Artificial intelligence techniques Artificial neural networks (ANNs) are capable of performing many classification, learning and function approximation tasks, yet in practice sometimes they deliver only marginal performance [93-94]. The inherent nonlinearity of artificial neural network results in the existence of many suboptimal networks and the great majority of training algorithms converge to these sub-optimal configurations. To address these problems we must use an optimal algorithm to optimize the ANN. ANNs play an essential role in the medical imaging field, including medical image analysis and computer-aided diagnosis, because objects such as lesions and organs in a medical image may not be represented into an accurate equation easily. Torbati et al. [94] proposed a neural network based method for medical image segmentation. Firstly, a modified self-organizing map (SOM) network, named moving average SOM (MA-SOM), was utilized to segment medical images. In addition, the proposed method segmented x-ray computerized tomography (CT) and magnetic resonance (MR) head images much better than the incremental supervised neural network (ISNN) and SOM-based methods. A variety of different neural network-based algorithms were also available in [95] for texture-based segmentation and classification having good accuracy. The ANNs were extended in medical diagnosis as in [96]. Automatic segmentation methods can be useful for clinical applications if they have: 1) Ability to segment like an expert; 2) Excellent performance for diverse datasets; and 3) Reasonable processing speed. Artificial Neural Networks (ANNs) have been developed for a wide range of applications such as function approximation, feature extraction, optimization, and classification. One of the main uses of ANN in medical image analysis is to classify lesions into some classes such as normal or abnormal, malignant or benign and lesions or non-lesions [94]. Ant- colony algorithms which are population based search methods are effective in optimization with a large number of design variables and low cost function evaluation. Particle swarm optimization (PSO) algorithm was inspired by the social behavior of animals, such as bird flocking or fish schooling [98]. In PSO, each solution is a ‘bird’ in the flock and is referred to as a ‘particle’. Unlike genetic algorithms, in the process of evolution, the PSO does not create new child from parents, instead the particle in the population evolve to its social behavior and finds a path towards the destination [99]. The performance of three evolutionary algorithms such as genetic algorithm, particle swarm optimization (PSO), and ant-colony optimization (ACO) which is used to optimize the ANN are presented in [100]. They showed that optimization of neural networks improves speed of recall and may also improve the efficiency of training. The main advantages of artificial intelligence techniques are: 1. ability to learn adaptively, using training data to solve complex problems. 28

E.A.Zanaty and Said Ghoniemy capability of self-organization; it can create its own organization depending upon the information it receives during learning time 3. capability to perform in real time because of parallel configuration The main disadvantages of artificial intelligence techniques are: 1. most of these texture classifier algorithms require training; their performance is sensitive to training parameters and is adversely affected in the presence of noise. 2. at times supervised image segmentation and classification methods become very expensive, difficult and even impossible to correctly select and label the training data with its true category. 3. training is the main requirement of many ANN based algorithms where the classifiers need to be trained before they can be applied to segmentation and classification problem. 4. for different data sets, analysis of different images of different type and format, the whole effort of selecting training data set and training is required to be redone. 2.

3.10

Hybrid techniques

Hybrid techniques offer an improved solution to the segmentation problem by combining techniques of the previous categories. Hybrid methods [101] are reported to be robust in the segmentation of images, covering a wider range of types of images, including synthetic image, T1weighted MR phantom, and real MR slices in color or grey-level images. Yu and Wang [102] used the edge information to determine the seeds for region growing but applied a different algorithm not the standard algorithm. A so-called difference in strength (DIS) map is first created. The pixel with the smallest DIS value among the unlabeled pixels is chosen as the seed of a region. Tang et al. [103] presented an MRI brain image segmentation approach based on multiresolution edge detection, region selection, and intensity threshold techniques. Del-Fresno et al. [42] technique combined the genetic algorithms and Harris detection [104] to obtain more accurate tissue segmentation. Although the quality of image segmentation is improved, they consuming much time for finding the optima. Wu et al. [36] described a top down region-based image segmentation technique for medical images that contain three major regions: background and two tissues. This method can only segment 2D images and cannot segment 3D images or images which contain more than two tissues. Wang et al. [105] performed image segmentation on the whole image by doing boundary detection and region merging iteratively. However, Canny edge detection [106] is adopted to evaluate the performance of edges locating and decide which is the most suitable. Biswas and Sil [107] proposed an algorithm based on the concept of type-2 fuzzy sets to handle uncertainties that automatically selects the threshold values needed to segment the gradient image using classical Canny’s edge detection algorithm. The hybrization of region growing and watershed applied to brain imaging applications was described in [28]. They presented a watershed approach based on seed region growing and image entropy which could improve the medical image segmentation. The proposed algorithm enables the prior information of seed region growing and image entropy in its calculation. Zanaty [108] presented an approach based on combining the fuzzy k-mean clustering (FKM), seed region growing, and average overlap metric (AOM) with decision fusion to produce the greatest improvement in classification accuracy. Although several papers have been proposed for improving the medical images segmentation via watershed or hybrid watershed methods, the problems of oversegmentation and sensitivity to noise are still the challenge. However these techniques during the flood procedure are not unchangeable actually, by contrast with the wide value range of the intensity of medical image, this flooding is excessively particular and helps to cause the oversegmentation problem. For GA hybridization, Melkemi et al. [109] used genetic algorithms to combine different segmentation results obtained by different agents. Lai and Chang [110] presented hierarchical evolutionary algorithms (HEA) for medical image segmentation that can classify the image into appropriate classes and avoid the difficulty of searching for the proper number of classes. Tian et 29

E.A.Zanaty and Said Ghoniemy al. [111] proposed a hybrid genetic and variational expectation-maximization algorithm for brain MRI segmentation. In this approach, the variational expectation-maximization algorithm is performed to estimate the Gaussian mixture model, and the GA is employed to initialize the hyperparameters of the conjugate prior distributions of Gaussian mixture model parameters involved in the variational expectation-maximization algorithm. McIntosh and Hamarneh [112] explored the application of GAs to deformable models through the proposition of a novel method for medical image segmentation that combines GA with nonconvex, localized, medial-based shape statistics. Pavan et al. [113] studied medical image segmentation and attempted to extract the shape of the tissues in medical images automatically using automatic clustering using differential evolution. A method based on hybrid GA and active contour was presented to solve some of active contour problems for accurate medical ultrasound image segmentation. Talebi et al. [114] combined GA and fuzzy clustering in which the genetic algorithm is adopted to optimize the initial cluster center and then the fuzzy clustering is used for image segmentation. Zanaty and Ghiduk [115] combined GA and region growing to produce accurate medical image segmentation, and to overcome the oversegmentation problem. Chen et al. [116] proposed a hybrid segmentation method based on k clustering and graph-cuts for cardiac dual-source CT images. Rudra et al. [117] proposed a new set of edge weights for the traditional graph cut algorithm to correctly segment the WM from T1-weighted MRI sequence. Although the hybridization between GA and fuzzy clustering was presented to avoid the drawbacks of the fuzzy clustering and active contour respectively, these methods suffer oversegmentation problem. The main disadvantage of these methods is the difficulty of searching for the proper number of classes. However, the problem remains challenging, with no general and unique solution, due to a large and constantly growing number of different objects of interest, large variations of their properties in images, different medical imaging modalities, and associated changes of signal homogeneity, variability, and noise for each object.

3.11

Other approaches

Other approaches are also available for image segmentation such as atlas based segmentation [118]. Atlas based segmentation are frequently used powerful approaches in the field of medical image segmentation. Various techniques for atlas construction are developed for different human organs, like the heart [119], and especially the brain [120]. Some techniques for atlas construction were described by [121] and exported in more details where the strategies for the atlas selection were discussed. They investigated four different selections of an atlas. First, one individual from the set is selected, second, the average shape atlas is constructed, third, the most similar instance from the set is selected as atlas and fourth, several individual images are used as atlases, and multi classifier approach is introduced before final segmentation. However, these are not the only possible options from which one can select an atlas, so we will list few more approaches for atlas construction.

4. Conclusions In this paper, we have reviewed the medical image segmentation and algorithms. An obvious conclusion is that for solving the same problem, a range of techniques is available that give different results depending on the application. In addition, we conclude the following:  Techniques that do not guarantee the connectivity among the tissues, like thresholding and clustering, cannot be used for medical-CAD modeling.  Techniques that use all the pixels in the image, like graph cut and clustering, and do not focus on the tissue to be extracted, will increase the time of segmentation.  Techniques in which we need to know the number of different tissues in the image will not be useful for medical image segmentation.  Techniques that need more information from users, active contours; clustering and graph cut, will depend mainly on the knowledge of the images.

30

E.A.Zanaty and Said Ghoniemy 

Techniques that work with weak boundary segmentation such as region growing, initial seeds are critical for segmentation.  Techniques based on GAs are promising, due to their robustness, and do not need prior knowledge for image segmentation. Through this survey, we have noted that the medical image segmentation has developed for many years in medical research; it is not regarded as an automated, reliable, and high speed technique because of magnetic field inhomogeneities such as: 1. Noise: random noise associated with the MR imaging system, which is known to have a Rician distribution. The noise comes from the stray current 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. When the level of noise is significant in an MR image, tissues that are similar in contrast could not be delineated effectively, causing error in tissue segmentation. 2. Intensity inhomogeneity (also called bias field or shading artefact) is manifested as smooth spatially varying signal intensity across the image caused by several factors including inhomogeneous radio frequency (RF) fields (caused by distortion of the RF field by the object being scanned or non-uniformity of the transmission field), resulting in the shading effect. 3. The boundaries among tissues become weak (when pixels around the boundaries have very similar intensities). The boundaries become strong if there is big difference between the pixels inside and outside the tissues. 4. Partial volume effect: more than one type of class or tissue occupy one pixel or voxel of an image, which are called partial volume effect. These pixels or voxels are usually called mixels. Future research aims to validate the MRI-based method in larger, more diverse groups of cancer patients, as well as examine its possible use for monitoring tumors over the course of cancer treatment. The MRI-based method also holds promise for scanning patients after their treatment is complete, when the ability to monitor them without radiation would be especially valuable. Although design and development of new algorithms for medical image segmentation is an active research field, there are challenges to overcome in order to achieve reliable and accurate solutions for auto-segmentation. Developing methods for automated segmentation will enhance comparability between and within medical studies and increase accuracy by allowing acquisition of thinner MRI-slices. The process of accurate segmentation of medical images is very important and crucial for a correct diagnosis by clinical tools. The future research will be focused on imaging modalities and methods for noise reduction, inhomogeneity correction and segmentation. Researchers are encouraged to investigate a suitable automatic landmark detection technology for cephalometric x-ray images and provide a standard evaluation framework with a clinical dataset. An automated medical image segmentation of major structures, tissues, and volume of interest would be improved from x-ray, MRI, PET and CT images.

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