Fuzzy C-mean based brain MRI segmentation algorithms

Brain image segmentation is one of the most important parts of clinical diagnostic tools. Fuzzy C-mean (FCM) is one of the most popular clustering based segmentation methods. In this paper, a review of the FCM based segmentation algorithms for brain MRI images is presented. The review covers algorithms for FCM based segmentation algorithms, their comparative evaluations based on reported results and the result of experiments for neighborhood based extensions for FCM.     

Review of brain MRI image segmentation methods

Brain image segmentation is one of the most important parts of clinical diagnostic tools. Brain images mostly contain noise, inhomogeneity and sometimes deviation. Therefore, accurate segmentation of brain images is a very difficult task. However, the process of accurate segmentation of these images is very important and crucial for a correct diagnosis by clinical tools. We presented a review of the methods used in brain segmentation. The review covers imaging modalities, magnetic resonance imaging and methods for noise reduction, inhomogeneity correction and segmentation. We conclude with a discussion on the trend of future research in brain segmentation.     

Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm

Computer-aided detection/diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. The objective of this paper is to review the recent published segmentation and classification techniques and their state-of-the-art for the human brain magnetic resonance images (MRI). The review reveals the CAD systems of human brain MRI images are still an open problem. In the light of this review we proposed a hybrid intelligent machine learning technique for computer-aided detection system for automatic detection of brain tumor through magnetic resonance images. The proposed technique is based on the following computational methods; the feedback pulse-coupled neural network for image segmentation, the discrete wavelet transform for features extraction, the principal component analysis for reducing the dimensionality of the wavelet coefficients, and the feed forward back-propagation neural network to classify inputs into normal or abnormal. The experiments were carried out on 101 images consisting of 14 normal and 87 abnormal (malignant and benign tumors) from a real human brain MRI dataset. The classification accuracy on both training and test images is 99% which was significantly good. Moreover, the proposed technique demonstrates its effectiveness compared with the other machine learning recently published techniques. The results revealed that the proposed hybrid approach is accurate and fast and robust. Finally, possible future directions are suggested.     

MRI脑肿瘤图像分割的深度学习方法综述

磁共振成像（MRI）作为一种典型的非侵入式成像技术，可产生高质量的无损伤和无颅骨伪影的脑影像，为脑肿瘤的诊断和治疗提供更为全面的信息，是脑肿瘤诊疗的主要技术手段。MRI脑肿瘤自动分割利用计算机技术从多模态脑影像中自动将肿瘤区（坏死区、水肿区、非增强肿瘤区和增强肿瘤区）和正常组织区进行分割和标注，对于辅助脑肿瘤的诊疗具有重要作用。本文对MRI脑肿瘤图像分割的深度学习方法进行了总结与分析，给出了各类方法的基本思想、网络架构形式、代表性改进方案以及优缺点总结等，并给出了部分典型方法在BraTS（multimodal brain tumor segmentation）数据集上的性能表现与分析结果。通过对该领域研究方法进行综述，对现有基于深度学习的MRI脑肿瘤分割研究方法进行了梳理，作为新的发展方向，MRI脑肿瘤图像分割的深度学习方法较传统方法已取得明显的性能提升，已成为领域主流方法并持续展现出良好的发展前景，有助于进一步推动MRI脑肿瘤分割在临床诊疗上的应用。     

MRI脑肿瘤图像分割研究进展及挑战

脑肿瘤分割是医学图像处理中的一项重要内容,其目的是辅助医生做出准确的诊断和治疗,在临床脑部医学领域具有重要的实用价值。核磁共振成像（MRI）是临床医生研究脑部组织结构的主要影像学工具,为了使更多研究者对MRI脑肿瘤图像分割理论及其发展进行探索,本文对该领域研究现状进行综述。首先总结了用于MRI脑肿瘤图像分割的方法,并对现有方法进行了分类,即分为监督分割和非监督分割;然后重点综述了基于深度学习的脑肿瘤分割方法,在研究其关键技术基础上归纳了优化策略;最后介绍了脑肿瘤分割（BraTS）挑战,并结合挑战中所用方法展望了脑肿瘤分割领域未来的发展趋势。MRI脑肿瘤图像分割领域的研究已经取得了一些显著进展,尤其是深度学习的发展为该领域的研究提供了新的思路。但由于脑肿瘤在大小、形状和位置方面的高度变化,以及脑肿瘤图像数据有限且类别不平衡等问题,使得脑肿瘤图像分割仍是一个极具挑战的课题。由于分割过程缺乏可解释性和透明性,如何将全自动分割方法应用于临床试验,还需要进行深入研究。     

基于D-S证据理论的多发性硬化症病灶分割算法*

多发性硬化症是一种严重威胁中枢神经功能的疾病,对其病灶自动检测方法的研究正受到越来越多的关注.基于D-S证据理论和模糊C-均值(FCM)聚类算法,提出了一种融合T1和T2加权MR图像信息的多发性硬化症自动分割算法.首先运用FCM聚类算法分别分割T1和T2加权MR图像,然后利用根据D-S证据理论得到的融合两种加权图像信息...     

Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies

Image segmentation consists in partitioning an image into different regions. MRI image segmentation is especially interesting, since an accurate segmentation of the different brain tissues provides a way to identify many brain disorders such as dementia, schizophrenia or even the Alzheimer's disease. A large variety of image segmentation approaches have been implemented before. Nevertheless, most of them use a priori knowledge about the voxel classification, which prevents figuring out other tissue classes different from the classes the system was trained for. This paper presents two unsupervised approaches for brain image segmentation. The first one is based on the use of relevant information extracted from the whole volume histogram which is processed by using self-organizing maps (SOM). This approach is faster and computationally more efficient than previously reported methods. The second method proposed consists of four stages including MRI brain image acquisition, first and second order feature extraction using overlapping windows, evolutionary computing-based feature selection and finally, map units are grouped by means of a novel SOM clustering algorithm. While the first method is a fast procedure for the segmentation of the whole volume and provides a way to model tissue classes, the second approach is a more robust scheme under noisy or bad intensity normalization conditions that provides better results using high resolution images, outperforming the results provided by other algorithms in the state-of-the-art, in terms of the average overlap metric. The proposed algorithms have been successfully evaluated using the IBSR and IBSR 2.0 databases, as well as high-resolution MR images from the Nuclear Medicine Department of the “Virgen de las Nieves” Hospital, Granada, Spain (VNH), providing in any case good segmentation results.     

基于U-net的海马体分割算法应用

为促进阿尔兹海默症的诊断及治疗,实现对海马体的精确分割,针对海马体MRI图像,提出一种基于U-net模型改进的分割算法。使用CLAHE等对原始图像进行预处理,经处理后的图像有效提高了分割效果;将残差模块加入实现分割算法的卷积网络,增强网络性能,避免网络性能退化。对原始数据集进行扩充,将扩充后的样本数据用以训练网络,解决数据量的问题。实验结果表明,该算法在脑部MRI图像中对海马体实现了良好的分割效果,能较好辅助医生诊断。     

A K-Means-Galactic Swarm Optimization-Based Clustering Algorithm with Otsu’s Entropy for Brain Tumor Detection

Image segmentation is a technique in order to segment an image into various parts and derive meaningful information out of each one. In this article, problem of image segmentation is applied on brain MRI images. This is done in order to detect and capture the location, size and shape of five different types of tumors. Here, image segmentation is viewed as an clustering problem and a new hybrid K-means Galatic Swarm Optimization (GSO) algorithm is proposed for effective solution. The Otsus entropy measure is used as the fitness function for deriving the segments. Extensive simulation studies with five performance measures on five different brain MRI images reveal the superior performance of the proposed approach over GSO, Real Coded Genetic Algorithm (RCGA), and K-Means clustering algorithms.     

Graph-based tools for microscopic cellular image segmentation

We propose a framework of graph-based tools for the segmentation of microscopic cellular images. This framework is based on an object oriented analysis of imaging problems in pathology. Our graph tools rely on a general formulation of discrete functional regularization on weighted graphs of arbitrary topology. It leads to a set of useful tools which can be combined together to address various image segmentation problems in pathology. To provide fast image segmentation algorithms, we also propose an image simplification based on graphs as a pre processing step. The abilities of this set of image processing discrete tools are illustrated through automatic and interactive segmentation schemes for color cytological and histological images segmentation problems.     

融合跨阶段深度学习的脑肿瘤MRI图像分割

目的 磁共振成像（magnetic resonance imaging,MRI）作为一种非侵入性的软组织对比成像方式,可以提供有关脑肿瘤的形状、大小和位置等有价值的信息,是用于脑肿瘤患者检查的主要方法,在脑肿瘤分割任务中发挥着重要作用。由于脑肿瘤本身复杂多变的形态、模糊的边界、低对比度以及样本梯度复杂等问题,导致高精度脑肿瘤MRI图像分割非常具有挑战性,目前主要依靠专业医师手动分割,费时且可重复性差。对此,本文提出一种基于U-Net的改进模型,即CSPU-Net （cross stage partial U-Net）脑肿瘤分割网络,以实现高精度的脑肿瘤MRI图像分割。方法 CSPU-Net在U-Net结构的上下采样中分别加入两种跨阶段局部网络结构（cross stage partial module,CSP）提取图像特征,结合GDL （general Dice loss）和WCE （weighted cross entropy）两种损失函数解决训练样本类别不平衡问题。结果 在BraTS （brain tumor segmentation）2018和BraTS 2019两个数据集上进行实验,在BraTS 2018数据集中的整体肿瘤分割精度、核心肿瘤分割精度和增强肿瘤分割精度分别为87.9%、80.6%和77.3%,相比于传统U-Net的改进模型（ResU-Net）分别提升了0.80%、1.60%和2.20%。在BraTS 2019数据集中的整体肿瘤分割精度、核心肿瘤分割精度和增强肿瘤分割精度分别为87.8%、77.9%和70.7%,相比于ResU-Net模型提升了0.70%、1.30%和1.40%。结论 本文提出的跨阶段局部网络结构,通过增加梯度路径、减少信息损失,可以有效提高脑肿瘤分割精度,实验结果证明了该模块对脑肿瘤分割任务的有效性。     

Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. However, the performance of most of the algorithms still falls far below expert expectations. In this paper, we review the main approaches to automated MS lesion segmentation. The main features of the segmentation algorithms are analysed and the most recent important techniques are classified into different strategies according to their main principle, pointing out their strengths and weaknesses and suggesting new research directions. A qualitative and quantitative comparison of the results of the approaches analysed is also presented. Finally, possible future approaches to MS lesion segmentation are discussed.     

Study on the improved fuzzy clustering algorithm and its application in brain image segmentation

Brain magnetic resonance image segmentation has become a hotspot and a difficult point in the field of medical image segmentation, and its segmentation effect directly affects the later pathological analysis and clinical treatment. For the problem of brain image segmentation, firstly, the image is subjected to pre-processing such as histogram equalization to eliminate irrelevant information in the image and enhance the detectability of the information. Then, through the research and analysis of Fuzzy C-Means(FCM) algorithm, Kernel-based FCM(KFCOM) and Weighted fuzzy kernel clustering(WKFCOM) algorithms are proposed. The WKFSOM algorithm combines the advantages of the two algorithms. It not only uses image space information as prior knowledge, but also can deal with image ambiguity. Finally, the KFCOM and WKFCOM algorithms are used to analyze the MRI images of the brain, and the segmentation effects of various algorithms are quantitatively evaluated by MCR. The KFCOM algorithm has a misclassification rate of 9.03% and the WKFCOM algorithm has a misclassification rate of 6.67%. It can be concluded that the WKFCOM algorithm can accurately segment brain tissue efficiently and unsupervised, and has a good inhibitory effect on noise. This will make it easier to obtain clinical information about the disease and bring great convenience to the clinician’s diagnosis.     

MRI Brain Tumor Segmentation with Intuitionist Possibilistic Fuzzy Clustering and Morphological Operations

Digital Image Processing (DIP) is a well-developed field in the biological sciences which involves classification and detection of tumour. In medical science, automatic brain tumor diagnosis is an important phase. Brain tumor detection is performed by Computer-Aided Diagnosis (CAD) systems. The human image creation is greatly achieved by an approach namely medical imaging which is exploited for medical and research purposes. Recently Automatic brain tumor detection from MRI images has become the emerging research area of medical research. Brain tumor diagnosis mainly performed for obtaining exact location, orientation and area of abnormal tissues. Cancer and edema regions inference from brain magnetic resonance imaging (MRI) information is considered to be great challenge due to brain tumors complex structure, blurred borders, besides exterior features like noise. The noise compassion is mainly reduced along with segmentation stability by suggesting efficient hybrid clustering method merged with morphological process for brain cancer segmentation. Combined form of Median Modified Wiener filter (CMMWF) is chiefly deployed for denoising, and morphological operations which in turn eliminate nonbrain tissue, efficiently dropping technique’s sensitivity to noise. The proposed system contains the main phases such as preprocessing, brain tumor extraction and post processing. Image segmentation is greatly achieved by presenting Intuitionist Possibilistic Fuzzy Clustering (IPFC) algorithm. The algorithm’s stability is greatly enhanced by this clustering along with clustering parameters sensitivity reduction. Then, the post processing of images are done through morphological operations along with Hybrid Median filtering (HMF) for attaining exact tumors representations. Additionally, suggested algorithm is substantiated by comparing with other existing segmentation algorithms. The outcomes reveal that suggested algorithm achieves improved outcomes pertaining to accuracy, sensitivity, specificity, and recall.     

Sketch‐Based Editing Tools for Tumour Segmentation in 3D Medical Images

In the past years sophisticated automatic segmentation algorithms for various medical image segmentation problems have been developed. However, there are always cases where automatic algorithms fail to provide an acceptable segmentation. In these cases the user needs efficient segmentation editing tools, a problem which has not received much attention in research. We give a comprehensive overview on segmentation editing for three‐dimensional (3D) medical images. For segmentation editing in two‐dimensional (2D) images, we discuss a sketch‐based approach where the user modifies the segmentation in the contour domain. Based on this 2D interface, we present an image‐based as well as an image‐independent method for intuitive and efficient segmentation editing in 3D in the context of tumour segmentation in computed tomography (CT). Our editing tools have been evaluated on a database containing 1226 representative liver metastases, lung nodules and lymph nodes of different shape, size and image quality. In addition, we have performed a qualitative evaluation with radiologists and technical experts, proving the efficiency of our tools.     

稀疏形状先验的脑肿瘤图像分割

目的 在脑部肿瘤图像的分析过程中,准确分割出肿瘤区域对于计算机辅助脑部肿瘤疾病的诊断及治疗过程具有重要意义。然而,由于脑部图像常存在结构复杂、边界模糊、灰度不均以及肿瘤内部存在明暗区域的问题,使得肿瘤图像分割工作面临严峻挑战。为了克服上述困难,更好地实现脑部肿瘤图像分割,提出一种基于稀疏形状先验的脑肿瘤图像分割算法。方法 首先,研究脑部肿瘤图像的配准与形状描述,并以此为基础构建脑部肿瘤的稀疏形状先验约束模型;继而,将该稀疏形状先验约束模型与区域能量描述方法相结合,构建基于稀疏形状先验的能量函数;最后,对能量函数进行优化及迭代,输出脑部肿瘤区域分割结果。结果 本文使用脑胶质瘤公开数据集BraTS2017进行算法测试,本文算法的分割结果与真实数据之间的平均相似度达到93.97%,灵敏度达到91.3%,阳性预测率达到95.9%。本文算法的实验准确度较高,误判率较低,鲁棒性较强。结论 本文算法能够结合水平集方法在拓扑结构描述和稀疏表达方法在复杂形状表达方面的优势,同时由于加入了形状约束,能够有效削弱肿瘤内部明暗区域对分割结果造成的影响,从而更准确和稳定地实现脑部肿瘤图像分割。     

基于特征提取的脑部MRI肿瘤自动分割

对脑部磁共振图像中肿瘤的自动分割,有助于了解疾病特征和制定手术方案,评价治疗效果。该文通过提取基于灰度统计、对称性、纹理等的特征,结合AdaBoost方法,利用计算机进行自动脑肿瘤分割。该方法综合利用了磁共振(MR)各加权图像的信息和大脑解剖结构的知识,以及AdaBoost算法的特征选择能力。在20帧带有肿瘤的MR图像上进行实验,得到了96.82%的分类准确率。     

Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation

The segmentation of brain tumor plays an important role in diagnosis, treatment planning, and surgical simulation. The precise segmentation of brain tumor can help clinicians obtain its location, size, and shape information. We propose a fully automatic brain tumor segmentation method based on kernel sparse coding. It is validated with 3D multiple-modality magnetic resonance imaging (MRI). In this method, MRI images are pre-processed first to reduce the noise, and then kernel dictionary learning is used to extract the nonlinear features to construct five adaptive dictionaries for healthy tissues, necrosis, edema, non-enhancing tumor, and enhancing tumor tissues. Sparse coding is performed on the feature vectors extracted from the original MRI images, which are a patch of m×m×m around the voxel. A kernel-clustering algorithm based on dictionary learning is developed to code the voxels. In the end, morphological filtering is used to fill in the area among multiple connected components to improve the segmentation quality. To assess the segmentation performance, the segmentation results are uploaded to the online evaluation system where the evaluation metrics dice score, positive predictive value (PPV), sensitivity, and kappa are used. The results demonstrate that the proposed method has good performance on the complete tumor region (dice: 0.83; PPV: 0.84; sensitivity: 0.82), while slightly worse performance on the tumor core (dice: 0.69; PPV: 0.76; sensitivity: 0.80) and enhancing tumor (dice: 0.58; PPV: 0.60; sensitivity: 0.65). It is competitive to the other groups in the brain tumor segmentation challenge. Therefore, it is a potential method in differentiation of healthy and pathological tissues.     

基于t-混合模型的脑MR图像白质分割

核磁共振成像（MRI）作为临床辅助诊断和研究的重要工具,MR图像分割的准确性直接影响着后续处理的正确性和有效性。在目前的图像分割算法中,基于t-混合模型的图像分割方法因其快速和稳健性而受到重视。该方法的一般过程是先估计混合模型的参数,计算图像中每点的后验概率,然后根据贝叶斯最小错误率准则对图像进行分割。根据MR图像的特点,提出了基于t-混合模型的大脑MR图像白质分割的算法,并取得了较好的实验结果。     

基于改进粗糙集概率模型的鲁棒医学图像分割算法*

基于参数化模型的图像分割算法对复杂的医学图像分割精度较低,对此提出一种基于改进粗糙集概率模型的鲁棒医学图像分割算法。首先,将粗糙集的上下逼近与概率边界区引入最大期望算法中,表征每个类簇;然后,将图像的灰度分布建模为一个有限数量的混合粗糙集概率分布;最终,通过马尔可夫随机场引入图像的空间信息,提高图像分割算法的鲁棒性。基于合成脑部MR(核磁共振)图像库与真实脑部MR图像库的分割实验结果显示,本算法的分割精度与鲁棒性均优于其他参数化模型的分割算法及其他专门的脑部MR图像分割算法。