Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier

An accurate and early diagnosis of brain tumors based on medical ima-ging modalities is of great interest because brain tumors are a harmful threat to a person’s health worldwide.Several medical imaging techniques have been used to analyze brain tumors,including computed tomography(CT)and magnetic reso-nance imaging(MRI).CT provides information about dense tissues,whereas MRI gives information about soft tissues.However,the fusion of CT and MRI images has little effect on enhancing the accuracy of the diagnosis of brain tumors.Therefore,machine learning methods have been adopted to diagnose brain tumors in recent years.This paper intends to develop a novel scheme to detect and classify brain tumors based on fused CT and MRI images.The pro-posed approach starts with preprocessing the images to reduce the noise.Then,fusion rules are applied to get the fused image,and a segmentation algorithm is employed to isolate the tumor region from the background to isolate the tumor region.Finally,a machine learning classifier classified the brain images into benign and malignant tumors.Computing statistical measures evaluate the classi-fication potential of the proposed scheme.Experimental outcomes are provided,and the Enhanced Flower Pollination Algorithm(EFPA)system shows that it out-performs other brain tumor classification methods considered for comparison.

Consistent and Specific Multi-View Functional Brain Networks Fusion for Autism Spectrum Disorder Diagnosis

Functional brain networks (FBN) based on resting-state functional magnetic resonance imaging (rs-fMRI) have become an important tool for exploring underlying organization patterns in the brain, which can provide an objective basis for brain disorders such as autistic spectrum disorder (ASD). Due to its importance, researchers have proposed a number of FBN estimation methods. However, most existing methods only model a type of functional connection relationship between brain regions-of-interest (ROIs), such as partial correlation or full correlation, which is difficult to fully capture the subtle connections among ROIs since these connections are extremely complex. Motivated by the multi-view learning, in this study we propose a novel Consistent and Specific Multi-view FBNs Fusion (CSMF) approach. Concretely, we first construct multi-view FBNs (i.e., multiple types of FBNs modelling various relationships among ROIs), and then these FBNs are decomposed into a consistent representation matrix and their own specific matrices which capture their common and unique information, respectively. Lastly, to obtain a better brain representation, it is fusing the consistent and specific representation matrices in the latent representation spaces of FBNs, but not directly fusing the original FBNs. This potentially makes it more easily to find the comprehensively brain connections. The experimental results of ASD identification on the ABIDE datasets validate the effectiveness of our proposed method compared to several state-of-the-art methods. Our proposed CSMF method achieved 72.8% and 76.67% classification performance on the ABIDE dataset.

The role of snapin in regulation of brain homeostasis

Brain homeostasis refe rs to the normal working state of the brain in a certain period,which is impo rtant for overall health and normal life activities.Currently,there is a lack of effective treatment methods for the adverse consequences caused by brain homeostasis imbalance.Snapin is a protein that assists in the formation of neuronal synapses and plays a crucial role in the normal growth and development of synapses.Recently,many researchers have reported the association between snapin and neurologic and psychiatric disorders,demonstrating that snapin can improve brain homeostasis.Clinical manifestations of brain disease often involve imbalances in brain homeostasis and may lead to neurological and behavioral sequelae.This article aims to explo re the role of snapin in restoring brain homeostasis after injury or diseases,highlighting its significance in maintaining brain homeostasis and treating brain diseases.Additionally,it comprehensively discusses the implications of snapin in other extracerebral diseases such as diabetes and viral infections,with the objective of determining the clinical potential of snapin in maintaining brain homeostasis.

Brain Time Stack图像融合技术在CT中的应用

目的:分析Brain Time Stack图像融合技术在CT中的应用。方法:选取2021年3月—2022年9月衡水市第四人民医院收治的50例CT检查患者作为研究对象。所有患者进行CT检查并进行Brain Time Stack后处理。比较四组不同部位CT值、标准差(SD)、信噪比(SNR)。比较四组图像主观质量评分。分析不同部位CT值、SD、SNR与图像主观质量评分的相关性。结果:B组的延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于A组;C组的延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值高于A组;D组延髓、额叶灰质、颞肌肌肉CT值明显低于A组,脑室、额叶白质、小脑外侧CT值明显高于A组;C组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于C组;D组脑室CT值明显高于C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值明显低于A组;C组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值均明显高于B组;C组额叶灰质SD明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、肌肉SD均明显低于B组、C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR均明显高于A组;C组、D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR值明显高于B组;C组、D组脑室SNR明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR明显高于C组,差异有统计学意义(P<0.05)。D组图像主观质量评分最高,差异有统计学意义(P<0.05)。延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧及颞肌肌肉SD与主观质量评分呈明显负相关,SNR与主观质量评分间呈明显正相关,差异有统计学意义(P<0.05)。结论:利用Brain Time Stack图像融合技术对头部CT扫描检查图像处理,动脉期结合前一期及后一期的图像数据在处理后具有更好的质量和更少的噪音。

Application of Preoperative CT/MRI Image Fusion in Target Positioning for Deep Brain Stimulation

Objective To explore the efficacy of target positioning by preoperative CT/MRI image fusion technique in deep brain stimulation.Methods We retrospectively analyzed the clinical data and images of 79 cases(68 with Parkinson's disease,11 with dystonia) who received preoperative CT/MRI image fusion in target positioning of subthalamic nucleus in deep brain stimulation.Deviation of implanted electrodes from the target nucleus of each patient were measured.Neurological evaluations of each patient before and after the treatment were performed and compared.Complications of the positioning and treatment were recorded.Results The mean deviations of the electrodes implanted on X,Y,and Z axis were 0.5 mm,0.6 mm,and 0.6 mm,respectively.Postoperative neurologic evaluations scores of unified Parkinson's disease rating scale(UPDRS) for Parkinson's disease and Burke-Fahn-Marsden Dystonia Rating Scale(BFMDRS) for dystonia patients improved significantly compared to the preoperative scores(P<0.001); Complications occurred in 10.1%(8/79) patients,and main side effects were dysarthria and diplopia.Conclusion Target positioning by preoperative CT/MRI image fusion technique in deep brain stimulation has high accuracy and good clinical outcomes.

Optimal Fusion-Based Handcrafted with Deep Features for Brain Cancer Classification

Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist radiologists.With the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided design.This study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer classification.The proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain tumors.The proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of MRI images.In addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the MRI image.Moreover,a fusion of handcrafted with deep features using Residual Network(ResNet)is utilized as feature extractors.Finally,HGSO algorithm with kernel extreme learning machine(KELM)model was utilized for identifying the presence of brain tumors.For examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BRATS 2015 dataset.

Deep Learning-Enhanced Brain Tumor Prediction via Entropy-Coded BPSO in CIELAB Color Space

Early detection of brain tumors is critical for effective treatment planning.Identifying tumors in their nascent stages can significantly enhance the chances of patient survival.While there are various types of brain tumors,each with unique characteristics and treatment protocols,tumors are often minuscule during their initial stages,making manual diagnosis challenging,time-consuming,and potentially ambiguous.Current techniques predominantly used in hospitals involve manual detection via MRI scans,which can be costly,error-prone,and time-intensive.An automated system for detecting brain tumors could be pivotal in identifying the disease in its earliest phases.This research applies several data augmentation techniques to enhance the dataset for diagnosis,including rotations of 90 and 180 degrees and inverting along vertical and horizontal axes.The CIELAB color space is employed for tumor image selection and ROI determination.Several deep learning models,such as DarkNet-53 and AlexNet,are applied to extract features from the fully connected layers,following the feature selection using entropy-coded Particle Swarm Optimization(PSO).The selected features are further processed through multiple SVM kernels for classification.This study furthers medical imaging with its automated approach to brain tumor detection,significantly minimizing the time and cost of a manual diagnosis.Our method heightens the possibilities of an earlier tumor identification,creating an avenue for more successful treatment planning and better overall patient outcomes.

基于多模态融合和自适应剪枝Transformer的脑肿瘤图像分割算法

脑肿瘤是目前世界上最致命的肿瘤之一,所以脑肿瘤图像的自动分割在临床诊疗中变得日益重要.近年来,基于CNN和Transformer的脑肿瘤分割方法在医学图像分割领域取得了令人欣喜的成就.然而,大多数方法没有充分利用脑肿瘤多模态间的互补性和差异性,并且模型中的Transformer在捕获远程依赖性的同时,忽略了其较大的计算复杂性、冗余依赖性等问题.针对此问题,提出一种基于多模态融合和自适应剪枝Transformer的脑肿瘤图像分割方法(MF-MAPT Swin UNETR),其中多模态融合模块可以充分学习性质相近的模态间信息和不同模态不同尺度的特征变化,为后续分割提供了充分的准备;基于多模态的自适应剪枝Transformer可以降低计算复杂度,对提升性能有一定的帮助,将MF-MAPT Swin UNETR模型在两个公共数据集上进行了实验验证,结果表明,该模型较最先进的方法整体具有突出的分割性能.

采用多任务特征融合的脑电情绪识别方法

特征选择与融合是提升脑电信号情绪解码精度的重要手段之一。然而,当前脑电情绪解码中的特征选择方法常忽略了脑电信号内在数据结构的隐含信息。该文提出一种基于近邻传播聚类的多任务特征融合方法,通过L_(2,1)范数约束实现稀疏特征选择,同时利用图拉普拉斯正则化保持不同子类间的潜在关系。该算法在不揭示真实样本标签的情况下,在子任务空间有效融合脑网络空间拓扑结构信息和微分熵信息,为高精度脑电信号情绪解码提供具有更高情绪表征能力的特征。DEAP和SEED数据集以及本实验室数据集的分析结果表明,该文提出的方法能显著提高脑电情绪解码的精度。

城市智慧公交云脑建设与应用

针对公交海量数据多源、异构、分散,不能充分收集、治理和应用,传统智能公交不同系统间联动性不强的实际现状,本文通过对北京、杭州、广州、上海等城市数据大脑、云脑平台的调查研究,结合云计算、人工智能等新一代信息技术,提出智慧公交云脑平台建设的总体架构,深挖十大核心应用功能,分析六大发展趋势。通过多源数据的融合、挖掘,核心模型算法的搭建,支撑公交业务应用,并加强各应用系统间的信息耦合。

基于有效感受野和注意力融合机制的脑肿瘤全自动分割

深度学习在医学图像分割领域取得了显著成果,但其在脑肿瘤分割任务中,仍面临感受野不足、冗余信息过多、信息丢失等问题;为此,本研究提出一种基于编-解码结构的脑肿瘤分割网络模型(EAU-Net)。EAU-Net采用有效感受野拓展模块和注意力融合模块改善脑肿瘤分割网络感受野不足与冗余信息过多带来的不利影响;同时,引入基于倒残差结构的瓶颈重采样模块,有效避免上下采样时造成的信息损失,并采用深度卷积降低网络的计算量。在BraTS2020数据集上的实验结果表明,EAU-Net获得最优的分割精度,验证了其在脑肿瘤分割任务中的可行性和有效性。

Multimodal Fusion of Brain Imaging Data: Methods and Applications

Neuroimaging data typically include multiple modalities,such as structural or functional magnetic resonance imaging,dif-fusion tensor imaging,and positron emission tomography,which provide multiple views for observing and analyzing the brain.To lever-age the complementary representations of different modalities,multimodal fusion is consequently needed to dig out both inter-modality and intra-modality information.With the exploited rich information,it is becoming popular to combine multiple modality data to ex-plore the structural and functional characteristics of the brain in both health and disease status.In this paper,we first review a wide spectrum of advanced machine learning methodologies for fusing multimodal brain imaging data,broadly categorized into unsupervised and supervised learning strategies.Followed by this,some representative applications are discussed,including how they help to under-stand the brain arealization,how they improve the prediction of behavioral phenotypes and brain aging,and how they accelerate the biomarker exploration of brain diseases.Finally,we discuss some exciting emerging trends and important future directions.Collectively,we intend to offer a comprehensive overview of brain imaging fusion methods and their successful applications,along with the chal-lenges imposed by multi-scale and big data,which arises an urgent demand on developing new models and platforms.

基于多模态模糊特征融合的脑龄协同预测算法

深度神经网络可通过训练从大脑图像中预测年龄,作为识别衰老相关疾病的生物标志物.传统的脑龄预测方法往往依赖于单一模态的图像数据,而多模态数据可提供更全面的信息,提高预测精度.然而,现有的多模态融合方法往往不能充分利用不同模态之间的相关性和互补性.为了克服上述问题,文中提出基于多模态模糊特征融合的脑龄协同预测算法(CMFF),设计模糊融合模块和多模态协同卷积模块,可有效利用多模态信息之间的相关信息和互补信息.首先,利用卷积神经网络从多模态脑图中提取特征张量,径向拼接后整合到一个全局特征张量中.然后,利用模糊融合模块学习被模糊化的特征,再将特征应用到多模态协同卷积模块,通过特定的卷积层增强模态间的互补信息.最后,基于性别信息和经过模糊协同处理的特征执行年龄预测回归任务,得到准确的预测年龄.在SRPBS多重障碍MRI数据集上的实验表明,CMFF性能较优.

基于距匹配及判别表征学习的多模态特征融合分类模型研究:高级别胶质瘤与单发性脑转移瘤的鉴别诊断

目的探索基于距匹配及判别表征学习的多模态特征融合分类模型在鉴别高级别胶质瘤(HGG)与单发性脑转移(SBM)中的鉴别能力和应用价值。方法收集了121例患者(61例HGG和60例SBM)的多参数磁共振成像(MRI)扫描图像,在T1W1、T2W1、T2加权液体衰减反转恢复(T2_FLAIR)和T1WI增强图像(CE_T1WI)4种常规轴位MRI图像上勾画目标感兴趣区域(ROI),并使用开源影像组学工具Pyradiomics从4个MRI序列分别提取影像组学特征。使用本研究提出的基于距匹配及判别表征学习的多模态特征融合分类模型对4个MRI序列的影像组学特征进行融合并得到分类模型。采用五折交叉验证方法和特异性(SPE)、灵敏度(SEN)、准确率(ACC)、ROC曲线下面积(AUC)评价该分类模型的鉴别性能。将本研究所提模型与其他特征融合分类模型对于HGG与SBM的鉴别能力进行定量比较,同时对本研究提出特征融合方法得到的融合特征进行样本散点可视化实验,验证本研究所提出的多模态特征融合分类模型的可行性和有效性。结果五折交叉验证结果显示本研究所提出的基于距匹配及判别表征学习的多模态特征融合分类模型在鉴别高级别胶质瘤与单发性脑转移瘤中的SPE、SEN、ACC、AUC分别为:0.871、0.817、0.843、0.930,且特征融合方法在可视化实验中具有优秀的表现。结论基于距匹配及判别表征学习的多模态特征融合分类模型在鉴别高级别胶质瘤与单发性脑转移瘤中的应用具有优秀的鉴别能力和较高的应用价值。同时,与其他特征融合分类模型相比,本研究提出的分类模型在HGG与SBM的鉴别分类任务中具有较大的优势。

跨模态融合的双注意力脑肿瘤分割算法

针对脑肿瘤多模态信息融合不充分以及肿瘤区域细节信息丢失等问题,提出了一种跨模态融合的双注意力脑肿瘤图像分割网络(CFDA-Net).在编码器-解码器的基础结构上,首先在编码器分支采用密集块与大内核注意力并行的新卷积块,可以使全局和局部信息有效融合且可以防止反向传播时梯度消失的问题;其次在编码器的第2、3和4层的左侧加入多模态深度融合模块,有效地利用不同模态间的互补信息;然后在解码器分支使用Shuffle Attention注意力将特征图分组处理后再聚合,其中分组的子特征一分为二地获取空间与通道的重要注意特征.最后使用二进制交叉熵(binary cross entropy,BCE)、Dice Loss与L2 Loss组成新的混合损失函数,缓解了脑肿瘤数据的类别不平衡问题,进一步提升分割性能.在BraTS2019脑肿瘤数据集上的实验结果表明,该模型在整体肿瘤区域、肿瘤核心区域和肿瘤增强区域的平均Dice系数值分别为0.887、0.892和0.815.与其他先进的分割方法ADHDC-Net、SDS-MSA-Net等相比,该模型在肿瘤核心区域和增强区域具有更好的分割效果.

基于多尺度融合的轻量级脑肿瘤分割算法

脑肿瘤是一种常见的神经系统疾病,准确的肿瘤分割对于诊断和治疗至关重要。然而,传统的自动分割方法受限于计算复杂度和精度,限制了其实际的临床应用。此外,脑肿瘤在不同尺度下具有多样性,因此需要一种方法来融合多尺度信息以提高分割精度。首先设计一种轻量级脑肿瘤分割模型,通过减小参数量和计算复杂度,使其更适合点对点临床分析;其次,引入了多尺度信息融合策略和注意力机制,以考虑不同尺度下的脑肿瘤特征,提高分割准确度和鲁棒性;最后,实验优化后的模型完整肿瘤、核心区域、增强区域的Dice分数分别为0.851、0.834、0.778,参数量和计算复杂度仅为0.73 M和0.20 G,优于最先进的分割方法。

结合扩张金字塔的脑部医学图像融合

针对现有脑部医学图像融合算法存在的融合图像细节模糊和边缘性差等问题,设计一种扩张金字塔特征提取算法,由特征提取器、特征融合器和特征重构器3部分组成。特征提取器由扩张金字塔特征模块提取浅层和深层图像特征的结合,防止图像细节信息的丢失;特征融合器采用改进的功能能量比(Functional Energy Ratio,FER)特征融合策略增强融合图像边缘信息;特征重构器由4层卷积构成归一化图像。实验结果表明,相较于当前通用的脑部融合算法,所提出的算法具有较好的视觉效果和细节信息,客观评价指标有更好的表现。

基于改进TransUNet模型的脑肿瘤图像分割方法研究

针对肿瘤细胞图像与正常组织图像之间具有强相似性、边界模糊以及染色变化大等特点,提出了基于TransUNet网络的优化改进分割模型。此分割模型在以TransUNet为主干网络的基础上于编码器部分引入注意力机制,抑制不相关的部分以突显深层特征的语义信息。同时,改变上采样过程中的融合方式,引入BiFusion模块进行选择性地融合,从而使特征数据能够保留更多高分辨率细节信息。该分割模型在Kaggle脑部低级别胶质瘤数据集上验证。实验结果表明,改进后算法的均交并比,召回率和平均精度均值分别为:97.31%,99.91%和98.72%,与目前医学图像分割的主流方法相比具有更优的性能。

未知复杂环境下基于兴趣驱动的类脑自主导航技术

随着无人系统的应用越发广泛,传统导航技术很难满足无人系统在面对复杂任务和未知环境时对自主智能导航性能的要求。哺乳动物能够在兴趣驱动下实现高效智能且自适应环境的导航行为,受此启发的基于兴趣驱动的类脑自主导航技术有潜力克服传统导航实时、准确和低功耗不能同时满足的缺点。本文首先阐述了哺乳动物大脑导航机理;其次,总结概括出基于兴趣驱动的类脑自主导航技术框架;再次,从自身感知、环境认知、记忆推理和兴趣决策4个方面梳理了类脑自主导航的关键技术和实现途径,指出了相关研究的缺陷;最后,分析了现阶段类脑自主导航技术的不足,并对未来一体化发展做出展望。

3D UNeXt:轻量级快速脑提取网络

为了解决现有脑提取网络结构复杂、参数量大且推理速度不高的问题,受UNeXt启发,提出一种基于3D卷积、3D多层感知机(multilayer perception,MLP)和多尺度特征融合的轻量级快速脑提取网络3D UNeXt,极大地减少了参数和浮点运算量,取得了令人满意的结果。3D UNeXt以U-Net为基本架构,在编码阶段使用3D卷积模块获取局部特征;在瓶颈阶段通过3D MLP模块获取全局特征和特征之间的远程依赖;在解码阶段借助多尺度特征融合模块高效融合浅层特征和深层特征。特别地,3D MLP模块在三个不同特征轴向进行线性移位操作,以获取不同维度特征的全局感受野并建立它们之间的远程依赖。在IBSR、NFBS和HTU-BrainMask三个数据集上进行实验,以和先进网络进行对比。实验结果表明,3D UNeXt在网络参数、浮点运算量、推理精度和速度等方面显著优于现有模型。