A Review of Fully Automated Techniques for Brain Tumor Detection From MR Images <s> II. LITERATURE REVIEW <s> The paper presents a novel method for CT head image automatic segmentation. The images are obtained from patients having a spontaneous intra-cerebral brain hemorrhage (ICH). The results of the segmentation are images partitioned into five regions of interest corresponding to four tissue classes (skull, brain, calcifications and ICH) and background. Once the images are segmented it is possible to calculate various hemorrhage region parameters such as size, position, etc. The segmentation is performed in three major steps. In the first phase feature extraction and normalization is performed using a receptive field (RF). Experiments were performed to determine the optimal RF structure. Pixels are classified in the second phase using the radial basis function (RBF) artificial neural network. Experiments with different RBF network topologies were performed in order to determine the optimal basis functions, network size and a training algorithm. The segmentation results obtained using the RBF network were compared with results obtained by multi-layer perceptron neural network (MLP). In the third phase the image regions obtained by the RBF network were labeled using an expert system. Experiments have shown that the proposed method successfully performs image segmentation. <s> BIB001 </s> A Review of Fully Automated Techniques for Brain Tumor Detection From MR Images <s> II. LITERATURE REVIEW <s> In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm. <s> BIB002 </s> A Review of Fully Automated Techniques for Brain Tumor Detection From MR Images <s> II. LITERATURE REVIEW <s> Automatic 3D segmentation of the brain from MR scans is a challenging problem that has received enormous amount of attention lately. Of the techniques reported in literature, very few are fully automatic. In this paper, we present an efficient and accurate, fully automatic 3D segmentation procedure for brain MR scans. It has several salient features namely, (1) instead of a single multiplicative bias field that affects all tissue intensities, separate parametric smooth models are used for the intensity of each class. This may be a more realistic model and avoids the need for a logarithmic transformation. (2) A brain atlas is used in conjunction with a robust registration procedure to find a non-rigid transformation that maps the standard brain to the specimen to be segmented. This transformation is then used to: segment the brain from non-brain tissue; compute prior probabilities for each class at each voxel location and find an appropriate automatic initialization. (3) Finally, a novel algorithm is presented which is a variant of the EM procedure, that incorporates a fast and accurate way to find optimal segmentations, given the intensity models along with the spatial coherence assumption. Experimental results with both synthetic and real data are included, as well as comparisons of the performance of our algorithm with that of other published methods. <s> BIB003 </s> A Review of Fully Automated Techniques for Brain Tumor Detection From MR Images <s> II. LITERATURE REVIEW <s> This paper presents a method for segmentation of medical images and the application of the so called geometric or Clifford algebras for volume representation, non-rigid registration of volumes and object tracking. Segmentation is done combining texture and boundary information in a region growing strategy obtaining good results. To model 2D surfaces and 3D volumetric data we present a new approach based on marching cubes idea however using spheres. We compare our approach with other method based on the delaunay tetrahedrization. The results show that our proposed approach reduces considerably the number of spheres. Also we show how to do non-rigid registration of two volumetric data represented as sets of spheres using 5-dimensional vectors in conformal geometric algebra. Finally we show the application of geometric algebras to track surgical devices in real time. <s> BIB004 </s> A Review of Fully Automated Techniques for Brain Tumor Detection From MR Images <s> II. LITERATURE REVIEW <s> In this paper we describe a method based on Support Vector machine s for Regression (SV R) to decode cognitive states from functional Magnetic Resonance Imaging (fMRI) data . In the context of the Pit tsburg h Brain Activity Interpretation Competition (PBAIC, 2007) , three participants were scanned during three runs of 20 -minute immersion in a Virtual Reality Environment (VRE) where they played a game that engaged them in various search tasks. A set of ob jective feature ratings was automatically extracted from the VRE during the scanning session , whereas a set of subjective features was then derived from ea ch individual experience . The aim of th e present study was to explore the feasibility of the SV R appr oach in the case of an extremely complex regression problem , in which subjective experience of participants immersed in a VRE had to be predicted from their fMRI data. The proposed methodology was model ed as a multiphase process: a pre -processing phase , ba sed on a filter approach, for fMRI image voxel selection , and a prediction phase , implemented by nonlinear SVR , for decoding subjective cognitive states from the selected voxel time series. Results highlight the generalization ability of nonlinear SVR , mak ing this approach particularly interesting for real world application of Brain Computer Interface (BCI). <s> BIB005 </s> A Review of Fully Automated Techniques for Brain Tumor Detection From MR Images <s> II. LITERATURE REVIEW <s> We present a new method for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main contribution of the paper is a Bayesian formulation for incorporating soft model assignments into the calculation of affinities, which are conventionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm, and apply the technique to the task of detecting and segmenting brain tumor and edema in multichannel magnetic resonance (MR) volumes. The computationally efficient method runs orders of magnitude faster than current state-of-the-art techniques giving comparable or improved results. Our quantitative results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of glioblastoma multiforme brain tumor. <s> BIB006 </s> A Review of Fully Automated Techniques for Brain Tumor Detection From MR Images <s> II. LITERATURE REVIEW <s> This paper aims to present a review of neural networks used in medical image processing. We classify neural networks by its processing goals and the nature of medical images. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of neural network application for medical image processing and an outlook for the future research are also discussed. By this survey, we try to answer the following two important questions: (1) What are the major applications of neural networks in medical image processing now and in the nearby future? (2) What are the major strengths and weakness of applying neural networks for solving medical image processing tasks? We believe that this would be very helpful researchers who are involved in medical image processing with neural network techniques. <s> BIB007 </s> A Review of Fully Automated Techniques for Brain Tumor Detection From MR Images <s> II. LITERATURE REVIEW <s> In this work a fully automatic algorithm to detect brain tumors by using symmetry analysis is proposed. Here we detect the tumor, segment the tumor and calculate the area of the tumor. The quantitative analysis of MRI brain tumor allows obtaining useful key indicators of disease progression. The complex problem of segmenting tumor in MRI can be successfully addressed by considering modular and multi-step approaches mimicking the human visual inspection process. The tumor detection is often an essential preliminary phase to solve the segmentation problem successfully. The experiments showed good results also in complex situations. Segmentation of images embraces a significant position in the region of image processing. It becomes more and more significant while normally dealing with medical images; magnetic resonance (MR) imaging suggest more perfect information for medical examination than that of other medical images such as ultrasonic , CT images and X-ray. Tumor segmentation and area calculation from MRI data is an essential but fatigue, boring and time unbearable task when it completed manually by medical professional when evaluate with present day’s high speed computing machines which facilitate us to visual study the area and position of unnecessary tissues. <s> BIB008
In this section, we present review of the selected literature on image segmentation techniques and their usage. The key objective is to highlight key strengths and limitations to these techniques. Zhang et al.  , suggest employing the Hidden Markov Random Field (HMRF) model for segmenting Brain MRI by using Expectation-Maximization algorithm. The study shows that HMRF can be merged with other techniques with ease. For instance, to achieve threedimensional and fully automated approach for brain MRI segmentation, the bias field correction algorithm proposed by Guillemaud & Brady (1997) can be easily incorporated into the technique suggested by them. The proposed technique acts as a general method that can be applied to a range of image segmentation problems with improved results. HMRF model is quite flexible for image modeling as it carries the ability to encode both the statistical and spatial properties of an image. HMRF model with Gaussian Emission Distribution is referred as GHMRF that produces images with controllable spatial structure -smaller the standard deviation, the transparent the spatial structure. The expectation maximization algorithm not only offers a proficient method for parameter estimation, but also provides a comprehensive framework for unsupervised classification. In short, the HMRF-EM method can plausibly overcome almost all the drawbacks associated with FM-EM method. The experimental results show that HMRF-EM produced promising results even with high level of noise, low image quality and large number of classes, as compared to RM-EM method. The major limitation in their work is that preliminary estimations based on threshold are purely heuristic. It does not generate perfect results in case of high invariability of brain MRI, particularly in terms of contrast between brain tissues and intensity ranges. And in case of poorly defined image, EM procedure is very likely to produce inaccurate segmentation. Apart from this, the whole algorithm is a bit slower than the original FM-EM model, due to the involvement of additional MRF-MAP classification, EM fitting procedure and bias field correction. However, it can obtain slightly good speed by utilizing ICM deterministic method. To further augment the advancements made in image segmentation, Ahmed et al. BIB002 Since, the incorporation of spatial constraints into the classification blurs some details; therefore, highcontrast pixels that usually represent boundaries between the objects should not be included in the neighborhood. However, this method involves phantom measurement based on global corrections for image non-uniformity. Therefore, further work is needed for localized measurement like impact on tumor boundary or volume determinations. Marroquin et al. BIB003 highlight the significance of 3D segmentation of brain MR scans. It uses separate parametric models for the intensity of each class. The brain Atlas is employed with a robust registration procedure to find non-rigid transformation to map the standard brain to the specimen to be segmented. This transformation is further used to segment brain from nonbrain tissues, computing prior probabilities and finding automatic initialization and finally applying MPM-MAP algorithm to find out optimal segmentation. Major findings from the study show that the MPM-MAP algorithm is comparatively robust than EM in terms of errors while estimating the posterior marginal. For optimal segmentation, the MPM-MAP algorithm involves only the solution of linear systems and is therefore computationally efficient. Tolba et al.  in their paper presented a new algorithm proposed for MR brain image segmentation, which is based on EM algorithm and the multi-resolution analysis of images, namely Gaussian multi-resolution EM algorithm. The experiments performed through the simulation show that performance of the proposed technique is much better than the conventional EM algorithm. The authors claim that their technique can be used for many other medical imaging with enhanced accuracy. GMEM overcomes drawback found in EM algorithm; for instance, EM algorithm fails to utilize the strong spatial correlation between neighboring pixels as it is based on GMM which assumes that all the pixels are independent and identically distributed. Moreover, proposed method is less sensitive to the noise level and can be used for noisy images. Additionally, GMEM algorithm can be used for many other medical imaging techniques with accurate results. The authors argue that advanced scientific techniques, such as data fusion, could help in formulating a more reliable segmentation algorithm. However, a limitation to this technique is that the GMEM algorithm, when applies to pixel laying on the boundaries between classes or on edges, generates many misclassified pixels because of parent and grandparent images contain only low level frequencies and hence edges rarely appear in these images. Li et al.  report that edge detection, image segmentation and matching are not easy to achieve in optical lenses that have long focal lengths. Previously, researchers have proposed many techniques for this mechanism, one of which is wavelet-based image fusion. The wavelet function can be improved by applying discrete wavelet frame transform (DWFT) and support vector machine (SVM). In this paper, authors experimented with five sets of 256-level images. Experimental results show that this technique is efficient and more accurate as it does not get affected by consistency verification and activity level measurements. However, the paper is limited to only one task related to fusion, and dynamic ranges are not considered during calculation. Sing et al.  propose fuzzy adaptive RBI based neural network for MR brain image segmentation. Hidden layer neuron of FARBF-NN neurons has been fuzzified to reduce noise effect. Bayro-Corrochano et al. BIB004 assert that medical image segmentation approach involves combination of texture and boundary information. The authors maintain that geometric algebra can be used to obtain volumetric data representation using spheres, nonrigid registration of spheres and real time object tracking. Major contribution of the proposed approach is that the use of marching cube algorithm reduces the number of primitives to model volumetric data and uses lesser number of primitives for the registration process, and thus makes registration process faster. However, the study has employed images obtained from CT scan, which has its own limitations like blurred boundaries and similar grey level between healthy and non healthy tissues. Yu et al.  state that image segmentation is used for extracting meaningful objects from an image. They propose segmenting an image into three parts, including dark, gray and white. Z-function and s-function are used for fuzzy division of the 2D histogram. Afterwards, QGA is used for finding combination of 12 membership parameters, which have maximum value. This technique is used to enhance image segmentation and significance of their work is that three-level image segmentation is used by following the maximum fuzzy partition of 2D Histograms. QGA is selected for optimal combination of parameters with the fuzzy partition entropy. The proposed method of fuzzy partition entropy of 2D histogram generates better performance than one- large number of possible combinations of 12 parameters in a multi-dimensional fuzzy partition are used, and it is practically not feasible to compute each possible value; therefore, QGA can be used to find the optimal combination. DiBono et al. BIB005 emphasize that a comprehensive methodology is required to explore the feasibility of the SVR Kernel-based approach for extremely complex regression problem. The authors have addressed this problem by adopting a method modeled as a multiphase process, i.e., preprocessing phase and a prediction phase. The authors created virtual environment to gain subjective feature and objective measures, and then FMRI data was collected for prediction of ratings. For each subject, feature was predicted separately. After applying SVM Regression, it was tuned with the help of applying statistical measures to achieve enhanced performance and generalizability. Generalization makes an algorithm easy to use in real-world applications. However, other statistical techniques such as sorting, distributions (chi-square, binomial) can be used to achieve more accuracy. Moreover, virtual environment has its own limitations and special considerations that sometimes lead to inaccuracy. Luts et al.  propose a technique to create nosologic brain images based on MRI and MRSI data. This technique uses color coding scheme for each voxel to differentiate distinctive tissues in a single image. For this purpose, a brain atlas and an abnormal tissue prior is acquired from MRSI data for segmentation. The detected abnormal tissue is then classified further by employing a supervised pattern recognition method followed by calculating the class probabilities for diverse tissue types. The proposed technique offers a novel way to visualize tumor heterogeneity in a specific image. The study results point out that combining MRI with MRSI feature improves classifiers' performance. A prior for the abnormal tissue along with a healthy brain atlas further improves the nosologic images. Despite its usefulness, the proposed methodology, however, only provides the one-dimensional image features. Shi1et al. BIB007 employed neural networks for medical image processing, including the key features of medical image preprocessing, segmentation, and object detection and recognition. The study employed Hopfield and feedforward neural networks. The feed-forward and Hopfield neural networks are simple to use and easy to implement. The added advantage of Hopfield neural networks is that it does not require pre-experimental knowledge. The time required to resolve image processing predicament is substantially reduced by using trained neural network. Kovacevic et al. BIB001 propos a segmentation method for brain images that performs a basic segmentation process comprising three steps. In the first step, prominent features of images are extracted and normalization is carried out. In the next step, pixels are classified using artificial neural networks. Finally, the results obtained in the second step are labeled. Once the aforesaid three steps are carried out, the validation is performed. The key feature of the study is that RBF network has better generalization capabilities. Moreover, the training algorithm is relatively simple as compared to the iterative back-propagation algorithm used in the multi-layer perceptron (MLP). Roy and Bandyopadhyay BIB008 propose automatic brain tumor detection approach using symmetry analysis. They first detect the tumor, segment it and then find out the area of tumor. One of the important aspects is that after performing the quantitative analysis, we can identify the status of an increase in the disease. They have suggested multi-step and modular approached to solve the complex MRI segmentation problem. Tumor detection is the first step of tumor segmentation. They have obtained good results in complex situations. The authors claim that MRI segmentation is one of the essential tasks in medical area but boring and time consuming if it is performed manually, so visually study of MRI is more interesting and fast. Padole and Chaudhari  proposed an efficient method for brain tumor detection. One of the most important steps in tumor detection is segmentation. Combination of two standard algorithm, mean shift and normalized cut is performed to detect the brain tumor surface area in MRI. Pre-processing step is first performed by using the mean shift algorithm in order to form segmented regions. In the next step region nodes clustering are processed by Ncut method. In the last step, the brain tumor is detected through component analysis. Kumar and Mehta  highlight that segmentation results will not be accurate if the tumor edges are not sharp, and this case occurs during the initial stage of tumor. Texture-based method is proposed in this paper. Along with brain tumor detection, segmentation is also done automatically using this method. The proposed texture analysis and seeded region method was implemented in MATLAB environment using 25 MRI images. Meenakshi and Anandhakumar  emphasize that MRI are useful for analyzing brain images because of its high-accuracy rate. Detection of the brain tumor has become a challenging task. Most of the existing techniques used machine learning techniques to detect brain tumor, but still they are suffered by the wrong diagnosis. The proposed technique combines the clustering and classification algorithm to minimize the error rate. Segmentation task is performed using orthonormal operators and classification using BPN. Images having the tumor are processed using K-means clustering and significant accuracy rate of 75% is obtained. In  , brain segmentation is automated using Dual Localization method. In the first step of their process scull mask is generated for the MRI images. White matter and tumor region is used to improvise K-means algorithm. In the last step of their method, they assessed the breath and length. Corso et al. BIB006 state that bottom up affinity-based segmentation and top down generative model techniques were not enough to get good results, and propose a novel methodology of automatic segmentation of heterogeneous images. Main difference in this paper is the use of Bayesian formulation to make complex calculations on soft models. It uses multichannel MR volumes to detect and segment brain tumor. Calculation in this model is more efficient than the conventional presented models, and results are presenting improved output in the form of quantitative analysis. A 2D portion of MR image can be used to detect multiform brain tumor, and an outline can be drawn to label the edema or active part of tumor. An automatic segmentation technique is helping the medical clinic researchers with automatic labeling freeing them from manual labeling. Fuzzy clustering is also a very famous technique for detecting brain tumor. It has demonstrated that fuzzy clustering approach provides better results by using raw multi sequence data. The Segmentation by Weighted Aggregation algorithm is also used to give a graph hierarchy of segments at different scales. Paul and Bandyopadhyay  emphasize that automation of the process to avoid any manual process is a challenging in tumor detection using MRI images. They present an automated two-step segmentation procedure which will stripped the skull by generating a skull mask and then after that by using an advanced K-means algorithm to provide two-level granularity for assessing the length and breadth of brain tumor. In a given algorithm, MRI image is read and image is enhanced using a 3 by 3 unsharpened filters. A clearer picture can be obtained by removing all the blurred area of the previous image. The two-dimensional array can be using to hold the output and values are rounded off in case if they are in the form of fraction. Mask can be generated for skull stripping and using a method automatically a histogram shape based image threshold is performed. It has a bi-model histogram. If threshold is successful, then we get a binary image with skull. Finally, K-Means algorithm based segmentation is performed. K-points of the objects are clustered and assign each point a group and recalculate the position of K points until they no longer move. After performing the above task in the form of segmentation, one can calculate the histogram of the segmented imaged and different peaks of this histogram will show the different image grey values against the grey and matter for tumor detection. Finally, after the segmentation, a line is applied to the image to know the maximum breadth and the length of the tumor. This methodology gives better results as compared to the qualitative and quantitative techniques. MRI of three different angles also improves the results and gives satisfactory results using segmentation. Roy and Bandyopadhyay BIB008 introduce the symmetric analysis to detect the brain tumor by making calculations on the area of tumor. Magnetic resonance imaging is used to perform quantitative analysis. MR images give better results as compare to other techniques used in the field of medical sciences like CT images and X-rays and ultrasound. Automatic segmentation of images helps facilitate medical specialists to make manual labeling since a healthy brain has a strong symmetry which does not remain stronger in case of a tumor. An algorithm has been introduced to perform a calculation on MRI of brain image, which requires an image as an input which is read in the form of a color or grayscale image. If the image is colored, then it is converted into grayscale with all the details of RGB components. Then it is resized and filters the multidimensional array using the multidimensional filters and rounds off all the fraction values. Then it combines the grayscale image with the filtered image and generates an enlarged image. Afterwards, it uses binary image having values in the form of 0s and 1s to calculate the global threshold. In the next step, one can perform watershed segmentation. Finally, morphological operations can be computed and stored as a final image using two variables for rows and columns. The proposed segmentation method is quite helpful in detecting tumor using MRI. There is one limitation associated with the current methodology that it cannot properly capture the model in case of unforeseen pathologic tissue type.