Computation of Statistical Features of Brain MR Images

2020

The human brain is an amazing organ of the human nervous system and controls all functions of our body. Brain tumors emerge from a mass of abnormal cells in the brain, and catching tumors early often allows for more treatment options. For diagnosing brain tumors, it has been benefited mostly from magnetic resonance images. In this study, we have developed the segmentation systems using the methods as K-Means, Fuzzy CMeans, Self-Organizing Map, Otsu, and the hybrid method of them, and evaluated the methods according to their success rates of segmentation. The developed systems, which take the brain image of MRI as input, perform skull stripping, preprocessing, and segmentation is performed using the clustering algorithms as K-Means, Fuzzy C-Means, Self-Organizing Map and Otsu Methods. Before preprocessing, the skull region is removed from the images in the MRI brain image data set. In preprocessing, the quality of the brain images is enhanced and the noise of the images is removed by...

Medical Image processing has become an accelerating subject of interest these days. Technology is growing day by day to capture the accurate internal body images of human beings to diagnose the abnormalities efficiently. To aggravate the efficiency Artificial Neural Networks (ANNs) can be used as a very good tool. This paper is an approach to classify the processed MRI images of human brain for the presence or absence of a tumor. The Artificial Neural Network (ANN) technology developed here further locates the tumor in case of its presence. The technology is based on the concept of segmenting the MRI image of the human brain and then finding out the gradient of pixels in different segments. On the basis of the pixel densities at different segments the ANN cluster the image into an image with a tumor or without a tumor. Further the position of the tumor is calculated.

Magnetic resonance imaging (MRI) is an advanced medical imaging technique providing rich information about the human soft tissue anatomy. The goal of magnetic resonance (MR) image segmentation is to accurately identify the principal tissue structures in these image volumes. A new unsupervised MR image segmentation method based on self-organizing feature map (SOFM) network is presented. The algorithm includes spatial constraints by using a Markov Random Field (MRF) model. The MRF term introduces the prior distribution with clique potentials and thus improves the segmentation results without having extra data samples in the training set or a complicated network structure. The simulation results demonstrate that the proposed algorithm is promising.

2010

Segmentation in image processing finds immense application in various areas. Image processing techniques can be used in medical applications for various diagnoses. In this article, we attempt to apply segmentation techniques to the brain images. Segmentation of brain magnetic resonance images (MRI) can be used to identify various neural disorders. We can segment abnormal tissues from the MRI, which and can be used for early detection of brain tumors. The segmentation, when applied to MRI, helps in extracting the different brain tissues such as white matter, gray matter and cerebrospinal fluid. Segmentation of these tissues helps in determining the volume of these tissues in the three-dimensional brain MRI. The study of volume changes helps in analyzing many neural disorders such as epilepsy and Alzheimer disease. We have proposed a hybrid method combining the classical Fuzzy C Means algorithm with neural network for segmentation.

Brain tissue Segmentation from the MRI images is having significance in the medical research field. The accurate Segmentation of the normal as well as the abnormal tissues is the complex assignment in this process. In this paper, a technique named Neuro-Fuzzy Based Segmentation (NFBS) is proposed for segmenting the normal features such as White Matter (WM), Gray Matter (GM) and Cerebro-Spinal Fluid (CSF) in the MRI Brain images. (1) Feature extraction (2) Classification (3) Segmentation are the three stages offered in this work. At first, the features such as energy, entropy, homogeneity, contrast and correlation from MRI Brain Images are extracted. Next, by utilizing Neuro-Fuzzy classifier, the Classification process is carried out and for this process, the feature set is specified as the input. From the outcome of Classification, the images are categorized into normal as well as abnormal. The further procedure Segmentation is performed according to this outcome only. The normal MRI images are segmented into normal tissues like White Matter (WM), Gray Matter (GM) and Cerebro-Spinal Fluid (CSF). All the tissues are individually segmented by special methods such as Gradient method, Orthogonal Polynomial Transform method. Utilizing MATLAB platform, the implementation of the proposed technique is made. The experimentation is carried out on the MRI Brain Images by BrainWeb data sets. The performance of the proposed technique is assessed with the help of the metrics namely FPR, FNR, Specificity, Sensitivity and Accuracy. Therefore, using our proposed techniques with enhanced classification, the normal tissues of MRI Brain images are segmented accurately.

International Journal of Computer Science and Information Technology, 2011

In this paper, an optimized fuzzy logic method for Magnetic Resonance Imaging (MRI) brain images segmentation is presented. The method is a technique based on a modified fuzzy c-means (FCM) clustering algorithm. The FCM algorithm that incorporates spatial information into the membership function is used for clustering, while a conventional FCM algorithm does not fully utilize the spatial information in the image. The advantages of the algorithm are that it is less sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. Originality of this research is the methods applied on a normal MRI brain image and MRI brain images with tumor, and analyze the area of tumor from segmented images. The results show that the method effectively segmented MRI brain images with spatial information, and the segmented MRI normal brain image and MRI brain images with tumor can be analyzed for diagnosis purpose. In order to identify the area of abnormal mass of MRI brain images with tumor, it is resulted that the area is identified from 8.38 to 25.57 cm 2 .

2011

Uncontrollable and unlimited cell growth leads to tumor genesis in the brain. If brain tumors are not diagnosed early and cured properly, they could cause permanent brain damage or even death to patients. As in all methods of treatments, any information about tumor position and size is important for successful treatment; hence, finding an accurate and a fully automated method to give information to physicians is necessary. A fully automatic and accurate method for tumor region detection and segmentation in brain magnetic resonance (MR) images is suggested. The presented approach is an improved fuzzy connectedness (FC) algorithm based on a scale in which the seed point is selected automatically. This algorithm is independent of the tumor type in terms of its pixels intensity. Tumor segmentation evaluation results based on similarity criteria (similarity index (SI), overlap fraction (OF), and extra fraction (EF) are 92.89%, 91.75%, and 3.95%, respectively) indicate a higher performance of the proposed approach compared to the conventional methods, especially in MR images, in tumor regions with low contrast. Thus, the suggested method is useful for increasing the ability of automatic estimation of tumor size and position in brain tissues, which provides more accurate investigation of the required surgery, chemotherapy, and radiotherapy procedures.

International Journal of Information and Education Technology, 2011

Expectation maximization algorithm has been extensively used in a variety of medical image processing applications, especially for detecting human brain disease. In this paper, an efficient and improved semi-automated Fuzzy EM based techniques for 3-D MR segmentation of human brain images is presented. FEM along with histogram based Kmeans in initialization step is used for the labeling of individual pixels/voxels of a 3D anatomical MR image (MRI) into the main tissue classes in the brain, Gray matter (GM), White matter (WM), CSF (Celebro-spinal fluid). FEM's membership function were estimated through a histogram-based method. The results show our proposed FEM-KMeans has better performance and convergence speed compare to histogram based EM.

International Journal of Computational Intelligence in Bioinformatics and Systems Biology, 2010

In this paper we introduce a new clustering method and apply it to brain magnetic resonance imaging (MRI) lateral ventricular compartments segmentation. The method uses Gaussian smoothing to enable fuzzy c-mean (FCM) to create both a more homogeneous clustering result and reduce effect caused by noise. With the objective of finding the optimal clustering results, we present a weighted clustering scheme which is applied to a Gaussian smoothed image using bootstrapping approach of feature weighting. The scheme is called weighted FCM with Gaussian smoothing (WGFCM). In addition to the observations on the clustering results of the MR images, we use validity functions and clustering centroids to evaluate the clustering results. Compared with the standard FCM with or without Gaussian smoothing, we found that the proposed scheme provides a better clustering performance for brain MRI lateral ventricular compartments segmentation.