Analysis of denoising filters on MRI brain images

The magnetic resonance imaging (MRI) modality is an effective tool in the diagnosis of the brain. These MR images are introduced with noise during acquisition which reduces the image quality and limits the accuracy in diagnosis. Elimination of noise in medical images is an important task in preprocessing and there exist different methods to eliminate noise in medical images. In this article, different denoising algorithms such as nonlocal means, principal component analysis, bilateral, and spatially adaptive nonlocal means (SANLM) filters are studied to eliminate noise in MR. Comparative analysis of these techniques have been with help of various metrics such as signal‐to‐noise ratio, peak signal‐to‐noise ratio (PSNR), mean squared error, root mean squared error, and structure similarity (SSIM). This comparative study shows that the SANLM denoising filter gives the best performance in terms of better PSNR and SSIM in visual interpretation. It also helps in clinical diagnosis of the brain.     

Robust brain MRI denoising and segmentation using enhanced non‐local means algorithm

Image denoising is an integral component of many practical medical systems. Non‐local means (NLM) is an effective method for image denoising which exploits the inherent structural redundancy present in images. Improved adaptive non‐local means (IANLM) is an improved variant of classical NLM based on a robust threshold criterion. In this paper, we have proposed an enhanced non‐local means (ENLM) algorithm, for application to brain MRI, by introducing several extensions to the IANLM algorithm. First, a Rician bias correction method is applied for adapting the IANLM algorithm to Rician noise in MR images. Second, a selective median filtering procedure based on fuzzy c‐means algorithm is proposed as a postprocessing step, in order to further improve the quality of IANLM‐filtered image. Third, different parameters of the proposed ENLM algorithm are optimized for application to brain MR images. Different variants of the proposed algorithm have been presented in order to investigate the influence of the proposed modifications. The proposed variants have been validated on both T1‐weighted (T1‐w) and T2‐weighted (T2‐w) simulated and real brain MRI. Compared with other denoising methods, superior quantitative and qualitative denoising results have been obtained for the proposed algorithm. Additionally, the proposed algorithm has been applied to T2‐weighted brain MRI with multiple sclerosis lesion to show its superior capability of preserving pathologically significant information. Finally, impact of the proposed algorithm has been tested on segmentation of brain MRI. Quantitative and qualitative segmentation results verify that the proposed algorithm based segmentation is better compared with segmentation produced by other contemporary techniques.     

MRI brain segmentation in combination of clustering methods with Markov random field

Medical image segmentation is a preliminary stage of inclusion in identification tools. The correct segmentation of brain Magnetic Resonance Imaging (MRI) images is crucial for an accurate detection of the disease diagnosis. Due to in‐homogeneity, low distinction and noise the segmentation of the brain MRI images is treated as the most challenging task. In this article, we proposed hybrid segmentation, by combining the clustering methods with Hidden Markov Random Field (HMRF) technique. This aims to decrease the computational load and improves the runtime of segmentation method, as MRF methodology is used in post‐processing the images. Its evaluation has performed on real imaging data, resulting in the classification of brain tissues with dice similarity metric. These results indicate the improvement in performance of the proposed method with various noise levels, compared with existing algorithms. In implementation, selection of clustering method provides better results in the segmentation of MRI brain images.     

Improved fuzzy entropy clustering algorithm for MRI brain image segmentation

Magnetic resonance imaging (MRI) brain image segmentation is essential at preliminary stage in the neuroscience research and computer‐aided diagnosis. However, presence of noise and intensity inhomogeneity in MRI brain images leads to improper segmentation. The fuzzy entropy clustering (FEC) is often used to deal with noisy data. One major disadvantage of the FEC algorithm is that it does not consider the local spatial information. In this article, we have proposed an improved fuzzy entropy clustering (IFEC) algorithm by introducing a new fuzzy factor, which incorporates both local spatial and gray‐level information. The IFEC algorithm is insensitive to noise, preserves the image detail during clustering, and is free of parameter selection. The efficacy of IFEC algorithm is demonstrated by comparing it quantitatively with the state‐of‐the‐art segmentation approaches in terms of similarity index on publically available real and simulated MRI brain images.     

A deep learning model integrating convolution neural network and multiple kernel K means clustering for segmenting brain tumor in magnetic resonance images

In medical imaging, segmenting brain tumor becomes a vital task, and it provides a way for early diagnosis and treatment. Manual segmentation of brain tumor in magnetic resonance (MR) images is a time‐consuming and challenging task. Hence, there is a need for a computer‐aided brain tumor segmentation approach. Using deep learning algorithms, a robust brain tumor segmentation approach is implemented by integrating convolution neural network (CNN) and multiple kernel K means clustering (MKKMC). In this proposed CNN‐MKKMC approach, classification of MR images into normal and abnormal is performed by CNN algorithm. At next, MKKMC algorithm is employed to segment the brain tumor from the abnormal brain image. The proposed CNN‐MKKMC algorithm is evaluated both visually and objectively in terms of accuracy, sensitivity, and specificity with the existing segmentation methods. The experimental results demonstrate that the proposed CNN‐MKKMC approach yields better accuracy in segmenting brain tumor with less time cost.     

Modified transform-based gamma correction for MRI tumor image denoising and segmentation by optimized histon-based elephant herding algorithm

Medical image processing is typically performed to diagnose a patient's brain tumor prior to surgery. In this study, a technique in denoising and segmentation was developed to improve medical image processing. The proposed approach employs multiple modules. In the first module, the noisy brain tumor image is transformed into multiple low- and high-pass tetrolet coefficients. In the second module, multiple low-pass tetrolet coefficients are applied through a modified transform-based gamma correction method. Generalized cross-validation is used on multiple high-pass tetrolet coefficients to obtain the best threshold value. In the third module, all enhanced coefficients are applied to the partial differential equation method. In the final module, the denoised image is applied to Atanassov's intuitionistic fuzzy set histon-based fuzzy clustering method with centroid optimization using an elephant herding method. Accordingly, the tumor part is segmented from the nontumor part in the magnetic resonance imaging brain images. The method was assessed in terms of peak signal-to-noise ratio, mean square error, specificity, sensitivity, and accuracy. The experimental results showed that the suggested method is superior to traditional methods.     

Comprehensive analysis of LPG‐PCA algorithms in denoising and deblurring of medical images

This article presents the detailed analysis of the local pixel grouping–principle component analysis (LPG‐PCA) algorithm in denoising and deblurring of medical images. Inefficient diagnosis of the medical images containing lot of information is often affected by the noise and artifacts. In order to remove these noises and artifacts, a statistical decorrelation technique, LPG‐PCA is used which is found to be one of the efficient methods, which could be used in improving the performance of medical images. For better preservation of local structures of the image, a pixel and its nearest neighbors are modeled as a vector variable, which leads to the selection of similar intensity characteristics. Denoising method used in this article is done in two stages for improving the denoising performance. The smoothening caused by the denoising process is removed by using LPG‐PCA along with adaptive sparse domain representations in the deblurring process. This involves clustering of data and finding the subdictionary of each cluster using LPG‐PCA. Experimental results show that an average improvement of 2.9 and 5.1 dB is found in the computed tomography and magnetic resonance imaging images using denoising and deblurring process. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 157–170, 2013     

Hybrid GrabCut Hidden Markov Model for Segmentation

Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify in MRI such as low-grade tumors or cerebral spinal fluid (CSF) leaks in the brain. The aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging (MRI) images and another problem also relates to efficiency and less execution time for segmentation of medical images. For tumor and CSF segmentation using trained light field database (LFD) datasets of MRI images. This research proposed the new framework of the hybrid k-Nearest Neighbors (k-NN) model that is a combination of hybridization of Graph Cut and Support Vector Machine (GCSVM) and Hidden Markov Model of k-Mean Clustering Algorithm (HMMkC). There are four different methods are used in this research namely (1) SVM, (2) GrabCut segmentation, (3) HMM, and (4) k-mean clustering algorithm. In this framework, on the one hand, phase one is to perform the classification of SVM and Graph Cut algorithm to create the maximum margin distance. This research use GrabCut segmentation method which is the application of the graph cut algorithm and extract the data with the help of scale-invariant features transform. On the other hand, in phase two, segment the low-grade tumors and CSF using a method adapted for HMkC and extract the information of tumor or CSF fluid by GCHMkC including iterative conditional maximizing mode (ICMM) with identifying the range of distant. Comparative evaluation is also performing by the comparison of existing techniques in this research. In conclusion, our proposed model gives better results than existing. This proposed model helps to common man and doctor that can identify their condition of brain easily. In future, this will model will use for other brain related diseases.     

Online variational learning of finite inverted Beta-Liouville mixture model for biomedical analysis

Image segmentation is widely applied for biomedical image analysis. However, segmentation of medical images is challenging due to many image modalities, such as, CT, X-ray, MRI, microscopy among others. An additional challenge to this is the high variability, inconsistent regions with missing edges, absence of texture contrast, and high noise in the background of biomedical images. Thus, many segmentation approaches have been investigated to address these issues and to transform medical images into meaningful information. During the past decade, finite mixture models have been revealed to be one of the most flexible and popular approaches in data clustering. In this article, we propose a statistical framework for online variational learning of finite inverted Beta-Liouville mixture model for clustering medical images. The online variational learning framework is used to estimate the parameters and the number of mixture components simultaneously, thus decreasing the computational complexity of the model. To this end, we evaluated our proposed algorithm on five different biomedical image data sets including optic disc detection and localization in diabetic retinopathy, digital imaging in melanoma lesion detection and segmentation, brain tumor detection, colon cancer detection and computer aid detection (CAD) of Malaria. Furthermore, we compared the proposed algorithm with three other popular algorithms. In our results, we analyze that the proposed online variational learning of finite IBL mixture model algorithm performs accurately on multiple modalities of medical images. It detects the disease patterns with high confidence. Computational and statistical approaches like the one presented in this article hold a significant impact on medical image analysis and interpretation in both clinical applications and scientific research. We believe that the proposed algorithm has the capacity to address multi modal biomedical image data sets and can be further applied by researchers to analyze correct disease patterns.     

Computer aided automated detection and classification of brain tumors using CANFIS classification method

The development of abnormal cells in human brain leads to the formation of tumors. This article proposes an efficient approach for brain tumor detection and segmentation using image fusion and co-active adaptive neuro fuzzy inference system (CANFIS) classification method. The brain MRI images are fused and the dual tree complex wavelet transform is applied on the fused image. Then, the statistical features, local ternary pattern features and gray level co-occurrence matrix features. These extracted features are classified using CANFIS classification approach for the classification of source brain MRI image into either normal or abnormal. Further, morphological operations are applied on the abnormal brain MRI image for segmenting the tumor regions. The proposed methodology is evaluated with respect to the performance metrics sensitivity, specificity, positive predictive value, negative predictive value, tumor segmentation accuracy with detection rate. The proposed image fusion based brain tumor detection and classification methodology stated in this article achieves 96.5% of average sensitivity, 97.7% of average specificity, 87.6% of positive predictive value, 96.6% of negative predictive value, and 98.8% of average accuracy.     

3D brain magnetic resonance imaging segmentation by using bitplane and adaptive fast marching

Diagnosis using medical images helps doctors detect diseases and treat patients effectively. A system that segments objects automatically from magnetic resonance imaging (MRI) plays an important role when doctors diagnose injuries and brain diseases. This article presents a method for automatic brain, scalp, and skull segmentation from MRI that uses Bitplane and the Adaptive Fast Marching method (FMM). We focus on the segmentation of these tissues, especially the brain, because they are the essential objects, and their segmentation is the first step in the segmentation of other tissues. First, the type of each slice is set based on the shape of the brain, and the head region is segmented by removing its background. Second, the sure region and the unsure region are segmented based on the Bitplane method. Finally, this work proposes an approach for classification that is based on the Adaptive FMM. This approach is evaluated with the BrainWeb and Neurodevelopmental MRI databases and compared with other methods. The Dice Averages for brain, scalp, and skull segmentation are 96%, 80%, and 93%, respectively, on the BrainWeb database and 91%, 67%, and 80%, respectively, on the Neurodevelopmental MRI database.     

Deploying swarm intelligence in medical imaging identifying metastasis,micro‐calcifications and brain image segmentation

This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of the aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X‐ray. The swarm intelligence algorithm presented in this study is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X‐rays whose status have been determined by senior radiologists. In addition, a hybrid swarm intelligence‐learning vector quantisation (LVQ) approach is proposed in the context of magnetic resonance (MR) brain image segmentation. The particle swarm optimisation is used to train the LVQ which eliminates the iteration‐dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.Inspec keywords: swarm intelligence, image segmentation, brain, neurophysiology, medical image processing, biomedical MRI, computerised tomography, diagnostic radiography, bone, diseases, learning (artificial intelligence), particle swarm optimisation, iterative methods, tumours, medical disordersOther keywords: medical imaging identifying metastasis, microcalcifications, umbrella deployment, stochastic diffusion, metastasis identification, bone scans, mammographs, CT imaging, aorta, nasogastric tube, chest X‐ray, hybrid swarm intelligence‐learning vector quantisation approach, magnetic resonance brain image segmentation, particle swarm optimisation, iteration‐dependent nature, tumour regions, abnormal MR brain imaging     

Automated brain tumour segmentation techniques— A review

Automatic segmentation of brain tumour is the process of separating abnormal tissues from normal tissues, such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The process of segmentation is still challenging due to the diversity of shape, location, and size of the tumour segmentation. The metabolic process, psychological process, and detailed information of the images, are obtained using positron emission tomography (PET) image, Computer Tomography (CT) image and Magnetic Resonance Image (MRI). Multimodal imaging techniques (such as PET/CT and PET/MRI) that combine the information from many imaging techniques contribute more for accurate brain tumour segmentation. In this article, a comprehensive overview of recent automatic brain tumour segmentation techniques of MRI, PET, CT, and multimodal imaging techniques has been provided. The methods, techniques, their working principle, advantages, their limitations, and their future challenges are discussed in this article. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 66–77, 2017     

A machine learning approach for brain image enhancement and segmentation

In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain's anatomical structures, for surgical planning, etc. However, due to presence of noise and uncertainty between different tissues in the brain image, the segmentation of brain is a challenging task. This problem is rectified in this article using two stages. In the first stage an enhancement technique called contrast limited fuzzy adaptive histogram equalization (CLFAHE) which is a combination of CLAHE and fuzzy enhancement is used to improve the contrast of MRI Brain images. Contrast of the image is controlled using contrast intensification operator (Clip limit). The second stage deals with the segmentation of enhanced image. The enhanced brain images are segmented using new level‐set method which has the property of both local and global segmentation. Signed pressure force (SPF) function is also used here which stops the contours at weak and blurred edged efficiently.     

Hybrid method combining superpixel,supervised learning,and random walk for glioma segmentation

Currently, the analysis of magnetic resonance imaging (MRI) brain images of pathological patients is performed manually, both for the recognition of brain structures or lesions and for their characterization. Physicians sometimes encounter difficulties in interpreting these images for a reliable diagnosis of the patient's condition. This is due to the difficulty of detecting the nature of the lesions, particularly glioma. Glioma is one of the most common tumors, and one of the most difficult to detect because of its shape, irregularities, and ambiguous limits. The segmentation of these tumors is one of the most crucial steps for their classification and surgical planning. This article presents a new, accurate, and automatic approach for the precise segmentation of early gliomas (benign tumors), combining the random walk (RW) algorithm and the simple linear iterative clustering algorithm. The study was carried out in four steps. The first step consisted of decomposing the image into superpixels to obtain an initial outline of the tumor. The superpixels were generated using the SLIC algorithm. In the second step, for each superpixel, a set of statistical and multifractal characteristics were calculated (gray‐level co‐occurrence matrix, multifractal detrending moving average). In the third step, the superpixels were classified using a supervised random forest (RF) type classier into healthy or tumorous brain tissue. In the final step, the contour of the detected tumor was enhanced using the customized RW algorithm. The proposed method was evaluated using the Brain Tumor Image Segmentation Challenge 2013 database. The results obtained are competitive compared to other existing methods.     

Brain tumor segmentation using a deep Shuffled-YOLO network

Brain tumor is an anomalous proliferation of cells in the brain that can evolve to malignant and benign tumors. Currently, segmentation of brain tumor is the most important surgical and pharmaceutical procedures. However, manually segmenting brain tumors is hard because it is hard to find erratically shaped tumors with only one modality; the MRI modalities are integrated to provide multi-modal images with data that can be utilized to segment tumors. The recent developments in machine learning and the accessibility of medical diagnostic imaging have made it possible to tackle the challenges of segmenting brain tumors with deep neural networks. In this work, a novel Shuffled-YOLO network has been proposed for segmenting brain tumors from multimodal MRI images. Initially, the scalable range-based adaptive bilateral filer (SCRAB) pre-processing technique was used to eliminate the noise artifacts from MRI while preserving the edges. In the segmentation phase, we propose a novel deep Shuffled-YOLO architecture for segmenting the internal tumor structures that include non-enhancing, edema, necrosis, and enhancing tumors from the multi-modality MRI sequences. The experimental fallouts reveal that the proposed Shuffled-YOLO network achieves a better accuracy range of 98.07% for BraTS 2020 and 97.04% for BraTS 2019 with very minimal computational complexity compared to the state-of-the-art models.     

An extended non‐local means algorithm: Application to brain MRI

Improved adaptive nonlocal means (IANLM) is a variant of classical nonlocal means (NLM) denoising method based on adaptation of its search window size. In this article, an extended nonlocal means (XNLM) algorithm is proposed by adapting IANLM to Rician noise in images obtained by magnetic resonance (MR) imaging modality. Moreover, for improved denoising, a wavelet coefficient mixing procedure is used in XNLM to mix wavelet sub‐bands of two IANLM‐filtered images, which are obtained using different parameters of IANLM. Finally, XNLM includes a novel parameter‐free pixel preselection procedure for improving computational efficiency of the algorithm. The proposed algorithm is validated on T1‐weighted, T2‐weighted and Proton Density (PD) weighted simulated brain MR images (MRI) at several noise levels. Optimal values of different parameters of XNLM are obtained for each type of MRI sequence, and different variants are investigated to reveal the benefits of different extensions presented in this work. The proposed XNLM algorithm outperforms several contemporary denoising algorithms on all the tested MRI sequences, and preserves important pathological information more effectively. Quantitative and visual results show that XNLM outperforms several existing denoising techniques, preserves important pathological information more effectively, and is computationallyefficient.     

Fuzzy clustering-based image segmentation techniques used to segment magnetic resonance imaging/computed tomography scan brain tissues: Comparative analysis

Medical images are obtained with computer-aided diagnosis using electronic devices such as CT scanners and MRI machines. The captured computed tomography (CT)/magnetic resonance imaging (MRI) images typically have limited spatial resolution, low contrast, noise and nonuniform variability in intensity due to environmental effects. Therefore, the distinctions of the objects are blurred, distorted and the meanings of the objects are not quite precise. Fuzzy sets and fuzzy logic are best suited for addressing vagueness and ambiguity. Fuzzy clustering technique has been commonly used for segmentation of images throughout the last decade. This study presents a comparative study of 14 fuzzy-clustered image segmentation algorithms used in the CT scan and MRI brain image segments. This study used 17 data sets including 4 synthetic data sets, namely, Bensaid, Diamond, Square, and its noisy version, 5 real-world digital images, and 8 CT scan/MRI brain images to analyze the algorithms. Ground truth images are used for qualitative analysis. Apart from the qualitative analysis, the study also quantitatively evaluated the methods using three validity metrics, namely, partition coefficient, partition entropy, and Fukuyama-Sugeno. After a thorough and careful review of the results, it is observed that extension of the fuzzy C-means (EFCM) outperformed every other image segmentation algorithm, even in a noisy environment, followed by kernel-based FCM σ, the output of which is also very good after EFCM.     

A new images segmentation method based on modified particle swarm optimization algorithm

The partitioning of an image into several constituent components is called image segmentation. Many approaches have been developed; one of them is the particle swarm optimization (PSO) algorithm, which is widely used. PSO algorithm is one of the most recent stochastic optimization strategies. In this article, a new efficient technique for the magnetic resonance imaging (MRI) brain images segmentation thematic based on PSO is proposed. The proposed algorithm presents an improved variant of PSO, which is particularly designed for optimal segmentation and it is called modified particle swarm optimization. The fitness function is used to evaluate all the particle swarm in order to arrange them in a descending order. The algorithm is evaluated by performance measures such as run time execution and the quality of the image after segmentation. The performance of the segmentation process is demonstrated by using a defined set of benchmark images and compared against conventional PSO, genetic algorithm, and PSO with Mahalanobis distance based segmentation methods. Then we applied our method on MRI brain image to determinate normal and pathological tissues. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 265–271, 2013