Segmentation Of Brain MR Images Research Articles

Brain MR image segmentation for tumor detection based on Riesz probability distributions

ABSTRACT This research introduces a new approach using the Riesz mixture model for medical image segmentation, specifically for diagnosing and treating brain tumors. We developed a novel technique for pixel classification based on the Riesz distribution, which is generated using an extended Bartlett decomposition. Our work is pioneering, as there are no existing studies addressing this issue in the literature. We aim to demonstrate the effectiveness of this distribution for brain image segmentation. We used the Expectation-Maximization algorithm to estimate the mixture parameters. To validate our segmentation algorithm, we conducted a comparative study with a recent method based on the Wishart distribution using Matlab software. Experiments with the Riesz mixture model showed that our method produces more intuitive results with a recognition rate of 94.52%. These results confirm the reliability of our method in detecting tumors using both synthetic and real brain images.

SVM for Classification of Brain MRI with K-Means for Detection

The segmentation of MR brain images using this method is based on K-means clustering, feature extraction using a discrete wavelet transform (DWT), and feature selection using a grey level co-occurrence matrix (GLCM). For this procedure, we have used a Perfect Radial Basis Function (RBF) - Support Vector Machine (SVM) Classifier. Based on fractions of selectivity and sensitivity, the classifier's performance was measured in terms of accuracy. The proposed classifier was found to be 93% accurate. Additionally, the Histogram methodology was applied in this proposed method in place of randomly choosing the cluster centres. Keywords: K-means clustering, Histon creation, Discrete Wavelet Transform (DWT), GLCM feature selection, and RBF- SVM Classifier.

Uncertainty parameter weighted entropy-based fuzzy c-means algorithm using complemented membership functions for noisy volumetric brain MR image segmentation

In this paper, we propose an uncertainty parameter weighted entropy-based fuzzy c-means clustering algorithm for noisy volumetric (3D) brain MR image segmentation using complemented global and spatially constraint local membership functions. Due to inherent noise and intensity inhomogeneity (IIH), the acquired MR images have blurry tissue boundaries. This leads to a situation where uncertainty arises while labeling a pixel/voxel into its proper tissue region. Further, it magnifies in the regions of different tissue boundaries. The proposed algorithm addresses this issue by introducing a class-level uncertainty parameter for each voxel and weightedly incorporating in the fuzzy objective function using complemented global and spatially constraint local fuzzy membership functions. It also incorporates total uncertainties in the 3D image domain by means of Shannon entropy. The complemented local fuzzy membership function estimates the degree of non-association, constraint by the local region-level intensity distribution. This framework allows the algorithm to utilize the spatial intensity distribution both in locally and globally within the image domain and produce more accurate cluster prototypes. We validate the proposed algorithm qualitatively and quantitatively with 10 3D simulated brain MR images, contaminated by noise and intensity inhomogeneity, 4 volumes of clinical real patient 3D brain MR images and a synthetic 3D image with added Rician noise. The simulation results in several evaluation indices indicate its superiority over state-of-the-art algorithms developed in recent past.

DeepTumor: Framework for Brain MR Image Classification, Segmentation and Tumor Detection.

The proper segmentation of the brain tumor from the image is important for both patients and medical personnel due to the sensitivity of the human brain. Operation intervention would require doctors to be extremely cautious and precise to target the brain's required portion. Furthermore, the segmentation process is also important for multi-class tumor classification. This work primarily concentrated on making a contribution in three main areas of brain MR Image processing for classification and segmentation which are: Brain MR image classification, tumor region segmentation and tumor classification. A framework named DeepTumor is presented for the multistage-multiclass Glioma Tumor classification into four classes; Edema, Necrosis, Enhancing and Non-enhancing. For the brain MR image binary classification (Tumorous and Non-tumorous), two deep Convolutional Neural Network) CNN models were proposed for brain MR image classification; 9-layer model with a total of 217,954 trainable parameters and an improved 10-layer model with a total of 80,243 trainable parameters. In the second stage, an enhanced Fuzzy C-means (FCM) based technique is proposed for the tumor segmentation in brain MR images. In the final stage, an enhanced CNN model 3 with 11 hidden layers and a total of 241,624 trainable parameters was proposed for the classification of the segmented tumor region into four Glioma Tumor classes. The experiments are performed using the BraTS MRI dataset. The experimental results of the proposed CNN models for binary classification and multiclass tumor classification are compared with the existing CNN models such as LeNet, AlexNet and GoogleNet as well as with the latest literature.

Local Average Based Kinetic Gas Molecular (LA-KGMO) Optimized MR Brain Image Segmentation Using Modified Self Organizing Map (MSOM)

Local Average Based Kinetic Gas Molecular (LA-KGMO) Optimized MR Brain Image Segmentation Using Modified Self Organizing Map (MSOM)

A domain adaptation benchmark for T1-weighted brain magnetic resonance image segmentation.

Accurate brain segmentation is critical for magnetic resonance imaging (MRI) analysis pipelines. Machine-learning-based brain MR image segmentation methods are among the state-of-the-art techniques for this task. Nevertheless, the segmentations produced by machine learning models often degrade in the presence of expected domain shifts between the test and train sets data distributions. These domain shifts are expected due to several factors, such as scanner hardware and software differences, technology updates, and differences in MRI acquisition parameters. Domain adaptation (DA) methods can make machine learning models more resilient to these domain shifts. This paper proposes a benchmark for investigating DA techniques for brain MR image segmentation using data collected across sites with scanners from different vendors (Philips, Siemens, and General Electric). Our work provides labeled data, publicly available source code for a set of baseline and DA models, and a benchmark for assessing different brain MR image segmentation techniques. We applied the proposed benchmark to evaluate two segmentation tasks: skull-stripping; and white-matter, gray-matter, and cerebrospinal fluid segmentation, but the benchmark can be extended to other brain structures. Our main findings during the development of this benchmark are that there is not a single DA technique that consistently outperforms others, and hyperparameter tuning and computational times for these methods still pose a challenge before broader adoption of these methods in the clinical practice.

Magnetic Resonance Features of Acquired Immune Deficiency Syndrome Involving Central Nervous System Diseases by Intelligent Fuzzy C-Means Clustering (FCM) Algorithm.

This study was aimed to explore the application of fuzzy C-means (FCM) algorithm in MR images of acquired immune deficiency syndrome (AIDS) patients. Sixty AIDS patients with central nervous disease were selected as the research object. A method of brain MR image segmentation based on FCM clustering optimization was proposed, and FCM was optimized based on the neighborhood pixel correlation of gray difference. The correlation was introduced into the objective function to obtain more accurate pixel membership and segmentation features of the image. The segmented image can retain the original image information. The proposed algorithm can clearly distinguish gray matter from white matter in images. The average time of image segmentation was 0.142 s, the longest time of level set algorithm was 2.887 s, and the running time of multithreshold algorithm was 1.708 s. FCM algorithm had the shortest running time, and the average time was significantly better than other algorithms (P < 0.05). FCM image segmentation efficiency was above 90%, and patients can clearly display the location of lesions after MRI imaging examination. In summary, FCM algorithm can effectively combine the spatial neighborhood information of the brain image, segment the BRAIN MR image, analyze the characteristics of AIDS patients from different directions, and provide effective treatment for patients.

A hybrid deep learning based brain tumor classification and segmentation by stationary wavelet packet transform and adaptive kernel fuzzy c means clustering

One of the deadly and dangerous types of cancer seen in children and adults named Brain tumor. Brain tumor's accurate and early diagnosis is significant for the treatment process. The tumor segmentation's accuracy is very crucial to diagnosis accuracy. As a consequence, employing brain MR image segmentation, this research provides a hybrid deep learning-based brain tumor diagnosis and classification. The primary goal of this suggested approach is to accurately and efficiently assess the tumor. The MRI images were first preprocessed for the segmentation phase. The preprocessed photos are then used throughout the feature extraction procedure. Stationary wavelet packet transform (SWPT) is applied for the feature extraction process. Then, a hybrid Adaptive Black widow optimization with Moth Flame optimization (HABWMFO) is utilized for the optimal feature selection. Following that, the feature values are being sent to clustering for segmentation. For segmentation, the Adaptive Kernel Fuzzy C Means clustering technique (AKFCM) was developed. Finally, a Hybrid Convolution Neural Network- Long Short-Term Memory (CNN-LSTM) deep learning is used to improve tumor classification accuracy. The suggested technique is implemented on the MATLAB platform, and its effectiveness is assessed using performance measures such as F1 score, accuracy, precision, loss, and recall. These analyses show that the suggested strategy is far more successful than the existing ones.

Retraction Note to: Rough fuzzy region based bounded support fuzzy C-means clustering for brain MR image segmentation

Retraction Note to: Rough fuzzy region based bounded support fuzzy C-means clustering for brain MR image segmentation

An Efficient Convolutional Neural Network Model for Brain MRI Segmentation

Medical image analysis is a very interesting research area, and it is a significant challenge for researchers. Due to the complexity of the brain structure, accurate diagnosis of brain tumors is extremely difficult. In recent years, research focused on medical image processing to solve this problem by relying on deep learning techniques, and it has achieved good results in this field. This paper proposes an efficient convolutional neural network model for MR brain image segmentation and analysis. The novel model consists of segmentation efficient-CNN and pre-efficient-CNN blocks for dataset diminution and improvement blocks. The unique efficient-CNN is specially designed according to the model proposed by ASCNN (application) CNN-specific) to perform unidirectional and transverse feature extraction and tumor and pixel classification. The recommended Full-ReLU activation feature halves the number of cores in a high-coil filtered winding layer without reducing process quality. In this specific efficient-CNN consists of 8 convolutional layers and 110 kernels. The experiment results were done using the MR brain database from the Arizona university, including eluding with and without tumor images. The proposal model achieved an accuracy of 97.2% to 98%, which proves the efficiency of the model and its ability to assist in the early diagnosis of brain tumors with sufficient accuracy to support the doctors' decision during diagnosis.

Tweedie hidden Markov random field and the expectation-method of moments and maximisation algorithm for brain MR image segmentation

ABSTRACT In this paper, a segmentation algorithm of brain magnetic resonance imaging is elaborated. The proposed method rests on Tweedie hidden Markov random field processing and the Expectation Method of moments and Maximisation algorithm. This new type of stochastic process stands for a Markov Random Field whose state sequence cannot be observed directly and whose spatial information is encoded through the mutual influences of neighbouring sites. Tweedie models are a special case of exponential dispersion models with power mean variance. In image processing problems as medical image segmentation no work has been recorded in terms of demonstrating the applicability of the Tweedie models with hidden Markov random field and different Tweedie parameters values p. Hence, our research stands for the pioneering work aiming at proving the efficiency of the Tweedie models in brain image segmentation. Expectation- Method of moments and Maximisation algorithm results from the combination of both EM (Expectation-Maximisation) algorithm and the method of moments. The algorithm was validated on synthetic data and tested on real images. The basic merit of our proposed algorithm of segmentation lies in its ability to identify the brain tumour. Experiments using Tweedie hidden Markov random field showed that the proposed method produces more intuitive results with a recognition rate of 93.72. In fact, the experimental results highlighted that our approach outperforms other approaches that are reported in literature.

A spatially constrained skew Student’s-t mixture model for brain MR image segmentation and bias field correction

Accurate segmentation of brain magnetic resonance images is a key step in quantitative analysis of brain images. Finite mixture model is one of the most widely used methods in brain magnetic resonance image segmentation. However, due to the presence of intensity inhomogeneity artifact and noise, the image histogram distribution of brain MR images may follow a heavy tailed distribution or asymmetric distribution, which makes traditional finite mixture model, such as Gaussian mixture model, hard to achieve accurate segmentation results. To alleviate these problems, a novel spatially constrained finite skew student’s-t mixture model is proposed in this paper. Firstly, we propose anisotropic two-level spatial information, which combines the prior and posterior probabilities, to reduce the impact of noise. The proposed spatial information can preserve rich details, such as edges and corners. Secondly, we couple the anisotropic spatial information into the skew student’s-t distribution to fit the intensity distribution of observation data with heavy tail distribution or asymmetric distribution. Thirdly, we use a linear combination of a set of orthogonal basis functions to model the intensity inhomogeneities. Finally, the objective function integrates both tissue segmentation and the bias field estimation. In the implementation, we used an improved expectation maximization (EM) algorithm to estimate the model parameters. The experimental results of our model on synthetic data and brain magnetic resonance images are better than other state-of-the-art segmentation methods.

An Interval Iteration Based Multilevel Thresholding Algorithm for Brain MR Image Segmentation.

In this paper, we propose an interval iteration multilevel thresholding method (IIMT). This approach is based on the Otsu method but iteratively searches for sub-regions of the image to achieve segmentation, rather than processing the full image as a whole region. Then, a novel multilevel thresholding framework based on IIMT for brain MR image segmentation is proposed. In this framework, the original image is first decomposed using a hybrid L1 − L0 layer decomposition method to obtain the base layer. Second, we use IIMT to segment both the original image and its base layer. Finally, the two segmentation results are integrated by a fusion scheme to obtain a more refined and accurate segmentation result. Experimental results showed that our proposed algorithm is effective, and outperforms the standard Otsu-based and other optimization-based segmentation methods.

Brain tumor identification with a hybrid feature extraction method based on discrete wavelet transform and principle component analysis

The Identification of brain tumors is a critical step that relies on the expertise and abilities of the physician. In order to enable radiologists to spot brain tumors, an automated tumor arrangement is extremely important. This paper presents a technique for MR brain image segmentation and classification to identify images as normal and abnormal. The proposed technique is a hybrid feature extraction submitted to enhance the classification results and basically consists of three stages. The first stage is used a 3-level of discrete wavelet transform (DWT) to extract image characteristics. In the second stage, the principle component analysis (PCA) is applied to reduce the size of characteristics. Finally, a random forest classifier (RF) was used with a feature selection for identification. 181 MR brain images are collected (81 normal and 100 abnormal), in distinguishing normal and abnormal tissues, the experimental results obtained an accuracy of 98%, the sensitivity achieved is 99.2%, specificity achieved is 97.8%, and showed the effectiveness of the proposed technique compared with many kinds of literature. The results show that the 3L-DWT+PCA+RF still achieved the best classification results. The proposed model could apply to the brain MRI sphere classification, which will help doctors to diagnose a tumor if it is normal or abnormal in certain degrees.

A novel k-mean clustering based graph cut for brain MR image segmentation

Image Segmentation is an influential method to detect and distinguish the diseased and normal sections of an image. Image segmentation extracts the regions of interest for precise diagnosis of tumours and scheduling appropriate line of treatment. The distinct tumours in brain have varied outlies, position and intensity values. Consequently, it is difficult to develop a common technique for brain MRI segmentation. Moreover, the extraction of anomalies from the brain MRI turns out to be a challenging task. In this research, we have developed a self-regulating method for selection of seed points for partitioning the graph of MR images with tumor to attain graph cut segmentation. The proposes method overcomes the key problem of primary seed point selection by utilizing the balanced brain layout and combines k - mean clustering into graph cut for segmenting an image. The outcomes attained by the proposed technique facilitates improved segmentation of the diseased region.

Marmoset brain segmentation from deconvolved magnetic resonance images and estimated label maps.

The proposed method aims to create label maps that can be used for the segmentation of animal brain MR images without the need of a brain template. This is achieved by performing a joint deconvolution and segmentation of the brain MR images. It is based on modeling locally the image statistics using a generalized Gaussian distribution (GGD) and couples the deconvolved image and its corresponding labels map using the GGD-Potts model. Because of the complexity of the resulting Bayesian estimators of the unknown model parameters, a Gibbs sampler is used to generate samples following the desired posterior probability. The performance of the proposed algorithm is assessed on simulated and real MR images by the segmentation of enhanced marmoset brain images into its main compartments using the corresponding label maps created. Quantitative assessment showed that this method presents results that are comparable to those obtained with the classical method-registering the volumes to a brain template. The proposed method of using labels as prior information for brain segmentation provides a similar or a slightly better performance compared with the classical reference method based on a dedicated template.

Harmonized neonatal brain MR image segmentation model for cross-site datasets

Accurate segmentation of white matter, gray matter and cerebrospinal fluid from neonatal brain MR images is of great importance in characterizing early brain development. Deep-learning-based methods have been successfully applied to neonatal brain MRIs with superior performance if testing subjects were acquired with the same imaging protocols/scanners as training subjects. However, for the testing subjects acquired with different imaging protocols/scanners, they cannot achieve accurate segmentation results due to large appearance/pattern differences between the testing and training subjects. Besides, imaging artifacts, like head motion, which are inevitable during the imaging acquisition process, also pose a challenge for the segmentation methods. To address these issues, in this paper, we propose a harmonized neonatal brain MR image segmentation model that harmonizes testing images acquired by different protocols/scanners into the domain of training images through a cycle-consistent generative adversarial network (CycleGAN). Meanwhile, the artifacts can be largely alleviated during the harmonization. Then, a densely-connected U-Net based segmentation model trained in the domain of training images can be applied robustly for segmenting the harmonized testing images. Comparisons with existing methods illustrate the better performance of the proposed method on neonatal brain MR images from cross-sites, a grand segmentation challenge, as well as images with artifacts.

Kapur’s Entropy based Hybridised WCMFO Algorithm for Brain MR Image Segmentation

This article proposes a Kapur-based hybridized Water Cycle and Moth-Flame Optimisation (WCMFO) algorithm that combines a water cycle algorithm (WCA) and moth flame optimisation (MFO) in multilevel thresholding of brain MR image segmentation. The WCMFO algorithm, proposed by Khalilpourazari and Khalilpourazary, gives both WCA and MFO advantages, while avoiding some of the drawbacks of either approach on its own, as demonstrated by faster convergence with broader exploration and exploitation capabilities. Experiments on 10 axial, T2-weighted test images were performed using Kapur entropy as the objective function at a threshold level of m = 2–5. The spiral movement of the behaviour of the moths is used for better exploitation in the WCA to find the global optimum values. WCMFO results, such as objective function value, peak signal to noise ratio, Central processing unit time and standard deviation, are collated and compared with other existing adaptive wind-driven optimization algorithm, adaptive bacterial foraging and particle swarm optimization algorithms. Experimental findings and comparison demonstrated that hybridized WCMFO algorithm was superior to the other algorithms. Moreover, the best segmentation is achieved on grey matter, white matter and cerebrospinal fluid that allows for better clinical decision-making and diagnosis in the medical field. Therefore, the proposed multilevel thresholding-based hybridized WCMFO algorithm is believed to be the most prominent preference for segmenting such complex brain images.

Intelligent Diagnosis Method of MRI Brain Image Using Parallel Self-Organizing Feature Maps Neural Network

Advances in medical imaging skills have promoted the influence of medical imaging in neuroscience. Having advanced medical imaging technology is essential for the medical industry. Magnetic resonance imaging (MRI) plays a central role in medical imaging. It plays a key role in the treatment of various human diseases. Doctors analyze brain size, shape, and location in brain MR images to assess brain disease and develop a medical plan. The manual division of brain tissue by experts is heavy and subjective. Therefore, the study of automatic segmentation of brain MR images has practical significance. Because the characteristics of brain MRI images are low contrast and high noise, which seriously affects the accuracy of image segmentation, the current image segmentation methods have some limitations in this application. In this paper, multiple self-organizing feature maps neural network (SOM-NN) are utilized to construct a parallel self-organizing feature maps neural network (PSOM-NN), which converts the segmentation problem of brain images into the classification problem of PSOMNN. The experiments show that SOM has strong self-learning ability in learning and training, and the parallel ability of PSOM-NN model greatly reduces the segmentation time, improves the real-time performance of the model, and helps to realize fully automatic or semi-automatic segmentation of the lesion area. PSOM can promote the improvement of segmentation accuracy and facilitate intelligent diagnosis.

MR Brain Image Segmentation Using a Fuzzy Weighted Multiview Possibility Clustering Algorithm with Low-Rank Constraints

To expand the multiview clustering abilities of traditional PCM in increasingly complex MR brain image segmentation tasks, a fuzzy weighted multiview possibility clustering algorithm with low-rank constraints (LR-FW-MVPCM) is proposed. The LR-FW-MVPCM can effectively mine both the internal consistency and diversity of multiview data, which are two principles for constructing a multiview clustering algorithm. First, a kernel norm is introduced as a low-rank constraint of the fuzzy membership matrix among multiple perspectives. Second, to ensure the clustering accuracy of the algorithm, the view fuzzy weighted mechanism is introduced to the framework of possibility c-means clustering, and the weights of each view are adaptively allocated during the iterative optimization process. The segmentation results of different brain tissues based on the proposed algorithm and three other algorithms illustrate that the LR-FW-MVPCM algorithm can segment MR brain images much more effectively and ensure better segmentation performance.