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.     

Tumor detection in T1, T2, FLAIR and MPR brain images using a combination of optimization and fuzzy clustering improved by seed‐based region growing algorithm

Tumor and Edema region present in Magnetic Resonance (MR) brain image can be segmented using Optimization and Clustering merged with seed‐based region growing algorithm. The proposed algorithm shows effectiveness in tumor detection in T1 ‐ w, T2 – w, Fluid Attenuated Inversion Recovery and Multiplanar Reconstruction type MR brain images. After an initial level segmentation exhibited by Modified Particle Swarm Optimization (MPSO) and Fuzzy C – Means (FCM) algorithm, the seed points are initialized using the region growing algorithm and based on these seed points; tumor detection in MR brain images is done. The parameters taken for comparison with the conventional techniques are Mean Square Error, Peak Signal to Noise Ratio, Jaccard (Tanimoto) index, Dice Overlap indices and Computational Time. These parameters prove the efficacy of the proposed algorithm. Heterogeneous type tumor regions present in the input MR brain images are segmented using the proposed algorithm. Furthermore, the algorithm shows augmentation in the process of brain tumor identification. Availability of gold standard images has led to the comparison of the suggested algorithm with MPSO‐based FCM and conventional Region Growing algorithm. Also, the algorithm recommended through this research is capable of producing Similarity Index value of 0.96, Overlap Fraction value of 0.97 and Extra Fraction value of 0.05, which are far better than the values articulated by MPSO‐based FCM and Region Growing algorithm. The proposed algorithm favors the segmentation of contrast enhanced images. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 33–45, 2017     

Brain segmentation in magnetic resonance human head scans using multi-seeded region growing

This paper presents a skull stripping method to segment the brain from MRI human head scans using multi-seeded region growing technique. The proposed method has two stages. In Stage-1, the brain in the middle slice is segmented, the brains in the remaining slices are segmented in Stage-2. In each stage, the proposed method is required to identify the rough brain mask. The fine brain region in the rough brain mask is segmented using multi-seeded region growing approach. The proposed method uses multiple seed points which are selected automatically based on the intensity profile of grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) of the brain image. The proposed brain segmentation method using multi-seeded region growing (BSMRG) was validated using 100 volumes of T1, T2 and PD-weighted MR brain images obtained from Internet Brain Segmentation Repository (IBSR), LONI and Whole Brain Atlas (WBA). The best Dice (D) value of 0·971 and Jaccard (J) value of 0·944 were recorded by the proposed BSMRG method on IBSR dataset. For LONI dataset, the best values of D?=?0·979 and J?=?0·960 were obtained for the sagittal oriented images by the proposed method. The performance consistency of the proposed method was tested on the brain images of all types and orientation and have and produced better and stable results than the existing methods Brain Extraction Tool (BET), Brain Surface Extraction (BSE), Watershed Algorithm (WAT), Hybrid Watershed Algorithm (HWA) and Skull Stripping using Graph Cuts (GCUT).     

A rapid automatic brain tumor detection method for MRI images using modified minimum error thresholding technique

This proposed work is aimed to develop a rapid automatic method to detect the brain tumor from T2‐weighted MRI brain images using the principle of modified minimum error thresholding (MET) method. Initially, modified MET method is applied to produce well segmented and sub‐structural clarity for MRI brain images. Further, using FCM clustering the appearance of tumor area is refined. The obtained results are compared with corresponding ground truth images. The quantitative measures of results were compared with the results of those conventional methods using the metrics predictive accuracy (PA), dice coefficient (DC), and processing time. The PA and DC values of the proposed method attained maximum value and processing time is minimum while compared to conventional FCM and k‐means clustering techniques. This proposed method is more efficient and faster than the existing segmentation methods in detecting the tumor region from T2‐weighted MRI brain images. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 77–85, 2015     

Computational intelligence techniques for human brain MRI classification

This study proposes an image classification methodology that automatically classifies human brain magnetic resonance (MR) images. The proposed methods contain four main stages: Data acquisition, preprocessing, feature extraction, feature reduction and classification, followed by evaluation. First stage starts by collecting MRI images from Harvard and our constructed Egyptian database. Second stage starts with noise reduction in MR images. Third stage obtains the features related to MRI images, using stationary wavelet transformation. In the fourth stage, the features of MR images have been reduced using principles of component analysis and kernel linear discriminator analysis (KLDA) to the more essential features. In last stage, the classification stage, two classifiers have been developed to classify subjects as normal or abnormal MRI human images. The first classifier is based on K‐Nearest Neighbor (KNN) on Euclidean distance. The second classifier is based on Levenberg‐Marquardt (LM‐ANN). Classification accuracy of 100% for KNN and LM‐ANN classifiers has been obtained. The result shows that the proposed methodologies are robust and effective compared with other recent works.     

A fast and efficient computer aided diagnostic system to detect tumor from brain magnetic resonance imaging

In this work, a simple and efficient CAD (computer‐aided diagnostic) system is proposed for tumor detection from brain magnetic resonance imaging (MRI). Poor contrast MR images are preprocessed by using morphological operations and DSR (dynamic stochastic resonance) technique. The appropriate segmentation of MR images plays an important role in yielding the correct detection of tumor. On examination of three views of brain MRI, it was visible that the region of interest (ROI) lies in the middle and its size ranges from 240 × 240 mm² to 280 × 280 mm². The proposed system makes effective use of this information and identifies four blocks from the desired ROI through block‐based segmentation. Texture and shape features are extracted for each block of all MRIs in the training set. The range of these feature values defines the threshold to distinguish tumorous and nontumorous MRIs. Features of each block of an MRI view are checked against the threshold. For a particular feature, if a block is found tumorous in a view, then the other views are also checked for the presence of tumor. If corresponding blocks in all the views are found to be tumorous, then the MRI is classified as tumorous. This selective block processing technique improves computational efficiency of the system. The proposed technique is well adaptive and fast, and it is compared with well‐known existing techniques, like k‐means, fuzzy c‐means, etc. The performance analysis based on accuracy and precision parameters emphasizes the effectiveness and efficiency of the proposed work.     

A comprehensive review: Segmentation of MRI images—brain tumor

Segmentation of tumors in human brain aims to classify different abnormal tissues (necrotic core, edema, active cells) from normal tissues (cerebrospinal fluid, gray matter, white matter) of the brain. In existence, detection of abnormal tissues is easy for studying brain tumor, but reproducibility, characterization of abnormalities and accuracy are complicated in the process of segmentation. The magnetic resonance imaging (MRI)‐based segmentation of tumors in brain images is more enhancing and attracting in current years of research studies. It is due to non‐invasive examination and good contrast prone to soft tissues of images obtained from MRI modality. Medical approval of different segmentation techniques depends on the benchmark and simplicity of the method. This article incorporates both fully‐automatic and semi‐automatic methods for segmentation. The outlook study of this article is to provide the summary of most significant segmentation methods of tumors in brain using MRI. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 295–304, 2016     

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.     

CANFIS based glioma brain tumor classification and retrieval system for tumor diagnosis

Brain tumor classification and retrieval system plays an important role in medical field. In this paper, an efficient Glioma Brain Tumor detection and its retrieval system is proposed. The proposed methodology consists of two modules as classification and retrieval. The classification modules are designed using preprocessing, feature extraction and tumor detection techniques using Co‐Active Adaptive Neuro Fuzzy Inference System (CANFIS) classifier. The image enhancement can be achieved using Heuristic histogram equalization technique as preprocessing and further texture features as Local Ternary Pattern (LTP) features and Grey Level Co‐occurrence Matrix (GLCM) features are extracted from the enhanced image. These features are used to classify the brain image into normal and abnormal using CANFIS classifier. The tumor region in abnormal brain image is segmented using normalized graph cut segmentation algorithm. The retrieval module is used to retrieve the similar segmented tumor regions from the dataset for diagnosing the tumor region using Euclidean algorithm. The proposed Glioma Brain tumor classification methodology achieves 97.28% sensitivity, 98.16% specificity and 99.14% accuracy. The proposed retrieval system achieves 97.29% precision and 98.16% recall rate with respect to ground truth images.     

Fuzzy C means integrated with spatial information and contrast enhancement for segmentation of MR brain images

This paper proposes a fully automated method for MR brain image segmentation into Gray Matter, White Matter and Cerebro‐spinal Fluid. It is an extension of Fuzzy C Means Clustering Algorithm which overcomes its drawbacks, of sensitivity to noise and inhomogeneity. In the conventional FCM, the membership function is computed based on the Euclidean distance between the pixel and the cluster center. It does not take into consideration the spatial correlation among the neighboring pixels. This means that the membership values of adjacent pixels belonging to the same cluster may not have the same range of membership value due to the contamination of noise and hence misclassified. Hence, in the proposed method, the membership function is convolved with mean filter and thus the local spatial information is incorporated in the clustering process. The method further includes pixel re‐labeling and contrast enhancement using non‐linear mapping to improve the segmentation accuracy. The proposed method is applied to both simulated and real T1‐weighted MR brain images from BrainWeb and IBSR database. Experiments show that there is an increase in segmentation accuracy of around 30% over the conventional methods and 6% over the state of the art methods.     

Fully automatic method for segmentation of brain tumor from multimodal magnetic resonance images using wavelet transformation and clustering technique

Fully automatic brain tumor segmentation is one of the critical tasks in magnetic resonance imaging (MRI) images. This proposed work is aimed to develop an automatic method for brain tumor segmentation process by wavelet transformation and clustering technique. The proposed method using discrete wavelet transform (DWT) for pre‐ and post‐processing, fuzzy c‐means (FCM) for brain tissues segmentation. Initially, MRI images are preprocessed by DWT to sharpen the images and enhance the tumor region. It assists to quicken the FCM clustering technique and classified into four major classes: gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and background (BG). Then check the abnormality detection using Fuzzy symmetric measure for GM, WM, and CSF classes. Finally, DWT method is applied in segmented abnormal region of images respectively and extracts the tumor portion. The proposed method used 30 multimodal MRI training datasets from BraTS2012 database. Several quantitative measures were calculated and compared with the existing. The proposed method yielded the mean value of similarity index as 0.73 for complete tumor, 0.53 for core tumor, and 0.35 for enhancing tumor. The proposed method gives better results than the existing challenging methods over the publicly available training dataset from MICCAI multimodal brain tumor segmentation challenge and a minimum processing time for tumor segmentation. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 305–314, 2016     

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.     

A fully automated hybrid methodology using Cuckoo‐based fuzzy clustering technique for magnetic resonance brain image segmentation

This article aims at developing an automated hybrid algorithm using Cuckoo Based Search (CBS) and interval type‐2 fuzzy based clustering, so as to exhibit efficient magnetic resonance (MR) brain image segmentation. An automatic MR brain image segmentation facilitates and enables a radiologist to have a brief review and easy analysis of complicated tumor regions of imprecise gray level regions with minimal user interface. The tumor region having severe intensity variations and suffering from poor boundaries are to be detected by the proposed hybrid technique that could ease the process of clinical diagnosis and this tends to be the core subject of this article. The ability of the proposed technique is compared using standard comparison parameters such as mean squared error, peak signal to noise ratio, computational time, Dice Overlap Index, and Jaccard T animoto C oefficient Index. The proposed CBS combined with interval type‐2 fuzzy based clustering produces a sensitivity of 0.7143 and specificity of 0.9375, which are far better than the conventional techniques such as kernel based, entropy based, graph‐cut based, and self‐organizing maps based clustering. Appreciable segmentation results of tumor region that enhances clinical diagnosis is made available through this article and two of the radiologists who have hands on experience in the field of radiology have extended their support in validating the efficiency of the proposed methodology and have given their consent in utilizing the proposed methodology in the processes of clinical oncology.     

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.     

Performance analysis of brain tissues and tumor detection and grading system using ANFIS classifier

Abnormal growth of cells in brain leads to the formation of tumors, which are categorized into benign and malignant. In this article, Co‐Active Adaptive Neuro Fuzzy Inference System (CANFIS) classification based brain tumor detection and its grading system is proposed. It has two phases as brain tumor segmentation and brain tissue segmentation. In brain tumor segmentation, CANFIS classifier is used to classify the test brain image into benign or malignant. Then, morphological operations are applied over the malignant image in order to segment the tumor regions in brain image. The K‐means classifier is used to classify the brain tissues into Grey Matter (GM), White Matter (WM) and Cerebro Spinal Fluid (CSF) regions as three different classes. Next, the segmented tumor is graded as mild, moderate or severe based on the presence of segmented tumor region in brain tissues.     

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.     

Segmentation for brain magnetic resonance images using dual‐tree complex wavelet transform and spatial constrained self‐organizing tree map

In brain MR images, the noise and low‐contrast significantly deteriorate the segmentation results. In this paper, we introduce a novel application of dual‐tree complex wavelet transform (DT‐CWT), and propose an automatic unsupervised segmentation method integrating DT‐CWT with self‐organizing map for brain MR images. First, a multidimensional feature vector is constructed based on the intensity, low‐frequency subband of DT‐CWT, and spatial position information. Then, a spatial constrained self‐organizing tree map (SCSOTM) is presented as the segmentation system. It adaptively captures the complicated spatial layout of the individual tissues, and overcomes the problem of overlapping gray‐scale intensities for different tissues. SCSOTM applies a dual‐thresholding method for automatic growing of the tree map, which uses the information from the high‐frequency subbands of DT‐CWT. The proposed method is validated by extensive experiments using both simulated and real T1‐weighted MR images, and compared with the state‐of‐the‐art algorithms. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 208–214, 2014     

Integrated segmentation of brain tumor images for radiotherapy and neurosurgery

Segmentation of brain tumor images is an important task in diagnosis and treatment planning for cancer patients. To achieve this goal with standard clinical acquisition protocols, conventionally, either classification algorithms are applied on multimodal MR images or atlas‐based segmentation is used on a high‐resolution monomodal MR image. These two approaches have been commonly regarded separately. We propose to integrate all the available imaging information into one framework to be able to use the information gained from the tissue classification of the multimodal images to perform a more precise segmentation on the high‐resolution monomodal image by atlas‐based segmentation. For this, we combine a state of the art regularized classification method with an enhanced version of an atlas‐registration approach including multiscale tumor‐growth modeling. This contribution offers the possibility to simultaneously segment subcortical structures in the patient by warping the respective atlas labels, which is important for neurosurgical planning and radiotherapy planning. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 59–63, 2013     

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.     

人脸图像中人眼的检测与定位

利用人脸几何特征和图像分割原理，提出了一种在有背景的灰度和彩色人脸图像中自动检测与定位人眼的新算法。首先，基于人脸器官几何分布先验知识建立人眼位置判定准则；其次对人眼的分割阈值范围进行粗估计；然后采用分割阈值递增法，并结合人眼位置判定准则判定分割图像中双眼黑块是否出现；最后利用二雏相关系数作为对称相似性测度，检验检测到的双眼的真实性。为了避免图像背景对人眼检测的干扰，还运用了肤色分割原理来缩小检测人眼的搜索区域，从而进一步提高人眼定位的准确性。实验验证表明，所提出的人眼检测与定位方法在速度和准确性方面具有良好的性能。