Color Magnetic Resonance Brain Image Segmentation by ParaOptiMUSIG Activation Function: An Application

The segmentation of magnetic resonance (MR) brain images is an important problem in medical imaging. Accurate segmentation of MR brain images allows a detail study of 3D brain tissue anatomy. It is also of great interest in the study of many brain disorders, where accurate volumetric measurement of the disorders is often required. In view of the importance of the task, much effort has been spent on finding accurate and efficient algorithms for the MRI segmentation problem. This chapter attempts to give the readers an overview of the MR brain segmentation problem, the various image artifacts that are often encountered, and describe some of the current approaches in this area, as well as our own work. To facilitate discussion, we broadly divide current MR brain image segmentation algorithms into three categories: classification-based, region-based, and contour-based methods, and discuss the merits and limitations of these approaches. Following a review of existing methods, we describe our approach for MR brain image segmentation in detail. Our approach is based on a clustering-for-classification framework, using a novel variant of the fuzzy c-means algorithm. We show that by incorporating two key ideas into the clustering algorithm, we are able to take into account the local spatial context, to compensate for the intensity nonuniformity artifact and the partial volume averaging artifact, and to reduce the influence of image noise, during the segmentation process. Extensive experiment results on both simulated and real MR brain images are given to illustrate the effectiveness and robustness of our approach. We conclude this chapter by pointing to some possible future directions in this area.

An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images

A simple yet accurate method for segmenting magnetic resonance (MR) brain images has been implemented. The semi-automatic technique carries out region growing in all three dimensions guided by initial seed points. Seed voxels may be specified interactively with a mouse or automatically through the selection of intensity thresholds. The 3-D seeded region growing (3-D SRG) algorithm offers significant advantages for MR brain segmentation, particularly in terms of speed and flexibility.

Automated segmentation of magnetic resonance (MR) brain imagery into anatomical regions is a complex task that needs contextual guidance to overcome problems associated with noise, missing data, and the overlap of features associated with different anatomical regions. In this work, the contextual information is provided as an anatomical brain atlas. The matching of atlas to image data is represented by a set of deformable contours that seek compromise fits between expected model information and image data. >

Brain MRI high resolution image creation and segmentation with the new GAN method

The hidden Markov random field (HMRF) model is an efficient method for image segmentation. However, it may not accurately segment a poorly defined image like Magnetic Resonance (MR) brain image. The HMRF model uses an initial estimation for tissue segmentation of MR brain image. The initial estimation of an input image has a large role to play to obtain a robust HMRF model. In general, a hard clustering method is used for initial estimation. However, it is not an efficient technique for the segmentation of a complex and irregular shape image like MR brain image. The method of clustering acts as a preprocessing step in improving the performance of HMRF. It aims at producing a set of concise data in the segmentation of brain tissues. In this paper, we propose a new approach to HMRF model, which incorporates the fuzzy C-means (FCM) clustering for the initial estimation of the brain image, which is not found in the literature. This results in efficient segmentation of brain tissues. The results are compared with the spatial FCM clustering algorithm and HMRF model with K-means clustering, using different segmentation evaluation indices. It is seen that the results are better than the other methods.

Accurate classification and quantification of brain tissues is important for monitoring disease progression, measurement of atrophy, and correlating magnetic resonance (MR) measures with clinical disability. Classification of MR brain images in the presence of lesions, such as multiple sclerosis (MS), is particularly challenging. Images obtained with lower resolution often suffer from partial volume averaging leading to false classifications. While partial volume averaging can be reduced by acquiring volumetric images at high resolution, image segmentation and quantification can be technically challenging. In this study, we integrated the brain anatomical knowledge with non-parametric and parametric statistical classifiers for automatically classifying tissues and lesions on high resolution multichannel three-dimensional images acquired on 60 MS brains. The results of automatic lesion segmentation were reviewed by the expert. The agreement between results obtained by the automated analysis and the expert was excellent as assessed by the quantitative metrics, low absolute volume difference percent (36.18 ± 34.90), low average symmetric surface distance (1.64 mm ± 1.30 mm), high true positive rate (84.75 ± 12.69), and low false positive rate (34.10 ± 16.00). The segmented results were also in close agreement with the corrected results as assessed by Bland–Altman and regression analyses. Finally, our lesion segmentation was validated using the MS lesion segmentation grand challenge dataset (MICCAI 2008).

<p>The performance of healthcare systems, particularly regarding disease diagnosis and treatment planning, depends on the segmentation of medical images. Fuzzy c-means (FCM) is one of the most widely used clustering techniques for image segmentation due to its simplicity and effectiveness. FCM, on the other hand, has the disadvantages of being noise-sensitive, quickly settling on local optimal solutions, and being sensitive to initial values. This paper suggests a fuzzy c-means clustering improved with a nature-inspired raindrop optimizer for lesion extraction in brain magnetic resonance (MR) images to get around this constraint. In the preprocessing stage, the possible noises in a digital image, such as speckles, gaussian, etc., are eliminated by a hybrid filter—A combination of Gaussian, mean, and median filters. This paper presents a comparative analysis of FCM clustering and FCM-raindrop optimization (FCM-RO) approach. The algorithm performance is evaluated for images subjected to various possible noises that may affect an image during transmission and storage. The proposed FCM-RO approach is comparable to other methods now in use. The suggested system detects lesions with a partition coefficient of 0.9505 and a partition entropy of 0.0890. Brain MR images are analyzed using MATLAB software to find and extract malignancies. Image data retrieved from the public data source Kaggle are used to assess the system’s performance.</p>

Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm

Image segmentation is a very crucial step in medical image analysis which is the first and the most important task in many clinical interventions. The authors propose in this paper to apply the variable neighborhood search (VNS) metaheuristic on the problem of brain magnetic resonance images (MRI) segmentation. In fact, by reviewing the literature, they notice that when the number of classes increases the computational time of the exhaustive methods grows exponentially with the number of required classes. That's why they exploit the VNS algorithm to optimize two maximizing thresholding functions which are the between-class variance (the Otsu's function) and the entropy thresholding (the Kapur's function). Thus, two versions of the VNS metaheuristic are respectively obtained: the VNS-Otsu and the VNS-Kapur. These two novel proposed thresholding methods are tested on a set of benchmark brain MRI to show their robustness and proficiency.

Fuzzy c-means (FCM) is a popular clustering method for image segmentation. However, FCM has difficulties in handling artifacts in brain magnetic resonance imaging (MRI), especially when it comes to bias field and noise. We propose a novel multiple-surface-approximation-based FCM with interval membership method for simultaneous bias correction and segmentation of Brain MRI. First, multiple surface representation of bias field is embedded into FCM to estimate and correct bias field. Then memberships of the improved FCM are extended to intervals. After the extension, clustering centers of different MR brain tissues could be solved more properly by the proposed method. Moreover, the proposed method is less sensitive to noise by introducing effects of neighboring pixels. Experiments conducted on artificial images and synthetic and real clinical Brain MRI show that the proposed method is effective and obtains better results of both bias field correction and segmentation than comparing methods.

In present times, automatic computer interpretation of medical images is of great importance for medical diagnosis. Having previously been an obstacle for medical science, early stage diagnoses together with accurately calculating the size and level of disease are very important elements of today's treatment process. The treatment and diagnosis of brain tumors depend on successful processing and evaluation of magnetic resonance (MR) brain images. Image segmentation is the most intractable process, because of the complicated nature of the MR brain images. In processing and analyzing MR images, there is unsuccessful automatic systems or successful semiautomatic system, which is still required large scale experting involvement and assessment. Within this paper the aim is to propose a system that will automatically extract the location, volume and borders of a brain tumor from patient MRI brain images. To achieve this goal, an automated algorithm has been developed, based upon and derived from a successful semi-automatic hybrid algorithm, for automation of the tumor diagnosis process. Patients' MR brain images from varying stages of the diagnosis and treatment were used for testing and validating the automatic algorithm under development. Based upon the achieved results of this algorithm and method, an automatic, sensitive MR brain computer interpretation can be proposed.

Unsupervised Fuzzy C-Means (FCM) clustering technique has been widely used in image segmentation. However, conventional FCM algorithm, being a histogram-based method when used in classification, has an intrinsic limitation: no spatial information is taken into account. This causes the FCM algorithm to work only on well-defined images with low level of noise. In this paper, a novel improvement to fuzzy clustering is described. The prior spatial constraint, which is defined as refusable level in this paper, is introduced into FCM algorithm through Markov random field theory and its equivalent Gibbs random field theory, in which the spatial information is encoded through mutual influences of neighboring sites. The algorithm is applied to the segmentation of synthetic image and brain magnetic resonance (MR) images (simulated and real) and the classification results show the new algorithm to be insensitive to noise.

Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, it is assumed that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. In this paper results obtained from the proposed algorithm is compared with those obtained by using Level set function in both synthetic and clinical data is analyzed. Thus concluding that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and improves the accuracy of brain MR image segmentation.

Segmentation of magnetic resonance images is medically complex and important for study and diagnosis of medical brain images, because of its sensitivity in terms of noise for brain medical images. These are the main issues in classification of brain images. Because of uncertainty & vagueness of brain medical images, so that rough sets, fuzzy sets and Rough sets are mathematical tools evaluate and handle uncertainty and vagueness in medical brain images. Traditionally, different type of fuzzy sets, Rough sets and rough sets based approaches were introduced, they have different several drawbacks with respect to different parameters. So this paper introduces a novel image segmentation calculation method i.e. Enhanced and Explored Intuitionistic Rough based Fuzzy C-means Approach (EEISFCMA) with estimation of weight bias parameter for brain image segmentation. Intuitionistic Rough based fuzzy sets are generalized form of fuzzy, Rough sets and their representative elements are evaluated with non-membership and membership value. Proposed algorithm of this paper consists standard features of existing clustering without spatial weight context data, it defines sensitive of noise in brain images, so that our proposed algorithm deals with intensity and noise reduction of brain image effectively. Furthermore, to reduce iterations in clustering, proposed algorithm initializes cluster centroid based on weight measure using max-dist evaluation method before execution of proposed algorithm. Experimental results of proposed approach carried out efficient image segmentation results compared to existing segmented approaches developed in brain image and other related images. Mainly proposed approach have consists better experimental evaluation based on results.