共查询到20条相似文献，搜索用时 15 毫秒
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Saladi Saritha N. Amutha Prabha 《International journal of imaging systems and technology》2016,26(4):295-304
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 相似文献
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图像在经过平移、旋转和尺度变化后是否仍具有很好的检索效果是基于形状的图像检索研究的一个难点.本文提出了一种利用Krawtchouk矩不变量实现基于形状的图像检索方法.该方法首先对图像进行灰度变换,然后提取图像的低阶矩,取16个低阶矩不变量作为图像的特征向量,并按照相似性度量输出相似图像从而实现基于形状的图像检索.文中给出了实验结果,并与基于几何矩不变量和基于Zernike矩不变量的图像检索方法进行了比较.结果表明本文的方法具有更好的检索性能,和上述两种方法相比,查全率分别提高了21.52%和7.6%,查准率则分别提高了16.25%和6.25%. 相似文献
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Nidhi Gupta Pritee Khanna 《International journal of imaging systems and technology》2015,25(2):123-130
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. 相似文献
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Balakumaresan Ragupathy Manivannan Karunakaran 《International journal of imaging systems and technology》2021,31(1):118-127
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. 相似文献
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Shan Jin Hongming Xu Yue Dong Xinyu Hao Fengying Qin Qi Xu Yong Zhu Fengyu Cong 《International journal of imaging systems and technology》2023,33(1):362-377
Automatic cervical cancer segmentation in multimodal magnetic resonance imaging (MRI) is essential because tumor location and delineation can support patients' diagnosis and treatment planning. To meet this clinical demand, we present an encoder–decoder deep learning architecture which employs an EfficientNet encoder in the UNet++ architecture (E-UNet++). EfficientNet helps in effectively encoding multiscale image features. The nested decoders with skip connections aggregate multiscale features from low-level to high-level, which helps in detecting fine-grained details. A cohort of 228 cervical cancer patients with multimodal MRI sequences, including T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient imaging, contrast enhancement T1-weighted imaging, and dynamic contrast-enhanced imaging (DCE), has been explored. Evaluations are performed by considering either single or multimodal MRI with standard segmentation quantitative metrics: dice similarity coefficient (DSC), intersection over union (IOU), and 95% Hausdorff distance (HD). Our results show that the E-UNet++ model can achieve DSC values of 0.681–0.786, IOU values of 0.558–0.678, and 95% HD values of 3.779–7.411 pixels in different single sequences. Meanwhile, it provides DSC values of 0.644 and 0.687 on three DCE subsequences and all MRI sequences together. Our designed model is superior to other comparative models, which shows the potential to be used as an artificial intelligence tool for cervical cancer segmentation in multimodal MRI. 相似文献
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Deepika Selvaraj Arunachalam Venkatesan Vijayalakshmi G. V. Mahesh Alex Noel Joseph Raj 《International journal of imaging systems and technology》2021,31(1):28-46
The novel coronavirus disease (SARS‐CoV‐2 or COVID‐19) is spreading across the world and is affecting public health and the world economy. Artificial Intelligence (AI) can play a key role in enhancing COVID‐19 detection. However, lung infection by COVID‐19 is not quantifiable due to a lack of studies and the difficulty involved in the collection of large datasets. Segmentation is a preferred technique to quantify and contour the COVID‐19 region on the lungs using computed tomography (CT) scan images. To address the dataset problem, we propose a deep neural network (DNN) model trained on a limited dataset where features are selected using a region‐specific approach. Specifically, we apply the Zernike moment (ZM) and gray level co‐occurrence matrix (GLCM) to extract the unique shape and texture features. The feature vectors computed from these techniques enable segmentation that illustrates the severity of the COVID‐19 infection. The proposed algorithm was compared with other existing state‐of‐the‐art deep neural networks using the Radiopedia and COVID‐19 CT Segmentation datasets presented specificity, sensitivity, sensitivity, mean absolute error (MAE), enhance‐alignment measure (EMφ), and structure measure (Sm) of 0.942, 0.701, 0.082, 0.867, and 0.783, respectively. The metrics demonstrate the performance of the model in quantifying the COVID‐19 infection with limited datasets. 相似文献
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目的针对当前较多图像复制-粘贴篡改检测算法主要依靠度量图像的结构特征来实现篡改检测,忽略了图像的强度特征,使其在各种几何变换下难以准确检测出伪造内容,导致检测结果中存在漏检和误检等问题,设计一种基于Harris算子耦合强度特征的图像复制-粘贴篡改检测算法。方法利用Harris算子对图像的特征点进行精确的提取。通过特征点构造圆形特征区域,求取该区域的Zernike矩,通过Zernike矩的大小实现对特征点的描述。随后,利用不同阶数的Zernike矩来描述图像的强度特征和纹理特征,从而构造匹配模型,对图像特征进行粗匹配,并引入RANSAC方法对粗匹配结果进行优化。最后,利用形态学腐蚀与膨胀操作将特征区域进行连通,以确定篡改区域。结果实验结果表明,与已有的图像伪造检测方案相比,所提算法具备更高的检测精度和鲁棒性,在噪声和旋转等变换下仍有更好的检测效果。结论所提技术拥有较高的伪造检测准确性,在图像水印、信息安全领域具有一定的参考价值。 相似文献
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Hanuman Verma Ramesh K. Agrawal Naveen Kumar 《International journal of imaging systems and technology》2014,24(4):277-283
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. 相似文献
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大气湍流畸变相位屏的数值模拟方法研究 总被引：4，自引：1，他引：3
利用功率谱反演法和Zernike多项式展开法对符合Kolmogonov统计规律的大气湍流畸变波前相位屏进行了数值模拟研究。通过对比模拟相位屏的相位结构函数与理论结果的差异分析模拟相位屏的准确性。结果表明,功率谱反演法产生的相位屏在高空间频率部分与理论相符,在低空间频率部分明显偏离理论值,通过次谐波补偿有效改善低频不足,次谐波级数达到4级足够;Zernike多项式展开法产生的相位屏的低空间频率成分与理论相符,而高空间频率成分不足随着所用Zernike阶数的增加而有所改善,但同时也带来计算量增大的问题。 相似文献
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本文介绍了可直接窥视大脑内部活动过程的核磁共振功能成像技术的基本原理、技术要点、临床应用举例及其目前尚存在的问题。 相似文献
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目的 通过品牌区隔阶段中视觉形象的社群依附构建,可以更好地促进此阶段中品牌对该社群受众的吸引力,以及后续社群持续的稳固与扩展,从而强化品牌与社群受众之间的关联度,促进品牌区隔阶段中品牌与社群及周边受众同频共振的积极效应,为品牌价值独特性与排他性的构建提供坚实的基础。方法 以品牌生命周期中的第3阶段即品牌区隔阶段为基础,通过分析主要价值驱动力及品牌社群价值,提出视觉形象促进社群依附,强化品牌与社群及周边受众关系,并通过原型形象、信息互动及成本关系深化的方式,形成基于社群依附的品牌视觉形象共振效应的构建策略。结论 以原型形象强化品牌社群关联,以信息互动促进品牌社群交流,以成本关系深化品牌社群绑定。 相似文献
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(Aim) COVID-19 is an ongoing infectious disease. It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021. Traditional computer vision methods have achieved promising results on the automatic smart diagnosis. (Method) This study aims to propose a novel deep learning method that can obtain better performance. We use the pseudo-Zernike moment (PZM), derived from Zernike moment, as the extracted features. Two settings are introducing: (i) image plane over unit circle; and (ii) image plane inside the unit circle. Afterward, we use a deep-stacked sparse autoencoder (DSSAE) as the classifier. Besides, multiple-way data augmentation is chosen to overcome overfitting. The multiple-way data augmentation is based on Gaussian noise, salt-and-pepper noise, speckle noise, horizontal and vertical shear, rotation, Gamma correction, random translation and scaling. (Results) 10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06% ± 1.54%, a specificity of 92.56% ± 1.06%, a precision of 92.53% ± 1.03%, and an accuracy of 92.31% ± 1.08%. Its F1 score, MCC, and FMI arrive at 92.29% ±1.10%, 84.64% ± 2.15%, and 92.29% ± 1.10%, respectively. The AUC of our model is 0.9576. (Conclusion) We demonstrate “image plane over unit circle” can get better results than “image plane inside a unit circle.” Besides, this proposed PZM-DSSAE model is better than eight state-of-the-art approaches. 相似文献
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现有核磁共振设备面对主磁场不均匀多是采取贴磁片等补偿磁场不均匀等硬件方法,但这给成像带来图像伪影,图像模糊等不良影响。针对磁共振成像中磁场不均匀的问题,提出了一种主磁场不均匀下的分数域磁共振成像方法。首先选择待成像活体组织的某一层,在该层上选择若干个点,测量该层面上的磁场强度大小,在磁共振成像原理的基础上,建立成像区域磁场强度分布模型,然后建立磁场的多项式模型,按照测量的磁场中是否存在明显的二阶分量可以将该多项式模型分为二阶多项式模型和高阶多项式模型;之后,将这两个模型分别代入磁共振的自由感应衰减(FID)信号中,对于二阶模型可以用分数阶傅里叶变换工具进行求解成像物体某一层上的自旋密度函数,对于高阶模型需要通过求解代数方程的方法得到成像物体某一层面上的自旋密度函数,这样便建立了主磁场任意不均匀下的磁共振信号模型。实验结果表明,该方法达到与均匀主磁场下近似同样的效果。 相似文献
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Suting Peng Wei Chen Jiawei Sun Boqiang Liu 《International journal of imaging systems and technology》2020,30(1):5-17
Gliomas segmentation is a critical and challenging task in surgery and treatment, and it is also the basis for subsequent evaluation of gliomas. Magnetic resonance imaging is extensively employed in diagnosing brain and nervous system abnormalities. However, brain tumor segmentation remains a challenging task, because differentiating brain tumors from normal tissues is difficult, tumor boundaries are often ambiguous and there is a high degree of variability in the shape, location, and extent of the patient. It is therefore desired to devise effective image segmentation architectures. In the past few decades, many algorithms for automatic segmentation of brain tumors have been proposed. Methods based on deep learning have achieved favorable performance for brain tumor segmentation. In this article, we propose a Multi-Scale 3D U-Nets architecture, which uses several U-net blocks to capture long-distance spatial information at different resolutions. We upsample feature maps at different resolutions to extract and utilize sufficient features, and we hypothesize that semantically similar features are easier to learn and process. In order to reduce the computational cost, we use 3D depthwise separable convolution instead of some standard 3D convolution. On BraTS 2015 testing set, we obtained dice scores of 0.85, 0.72, and 0.61 for the whole tumor, tumor core, and enhancing tumor, respectively. Our segmentation performance was competitive compared to other state-of-the-art methods. 相似文献
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Moona Mazher;Abdul Qayyum;Domenec Puig;Mohamed Abdel-Nasser; 《International journal of imaging systems and technology》2024,34(1):e23010
Automatic survival prediction of gliomas from brain magnetic resonance imaging (MRI) volumes is an essential step for a patient's prognosis analysis. Radiomics research delivers beneficial feature information from MRI imaging which is substantially required by clinicians and oncologists for predicting disease prognosis for precise surgical treatment and planning. In recent years, the success of deep learning has been vast in the field of medical imaging, and it shows state-of-the-art performance in applications like segmentation, classification, regression, and detection. Therefore, in this paper, we proposed a collective method using deep learning and radiomics techniques for the survival prediction of brain tumor patients. We first propose a hierarchical channel attention (HAM) module and a multi-scale-aware feature enhancement (MSAFE) to efficiently fuse adjacent hierarchical features in the proposed segmentation model. After segmentation, deep/latent features (LCNN) are extracted from the bottom layer of the proposed segmentation model. Later, we extracted selected radiomics features (histogram, location, and shape) using input images and segmented masks from the proposed segmentation model. Further, the 3D deep learning regressor has been trained for 3D regressor-based deep feature extraction. We proposed the method of overall survival prediction for the brain tumor patients by combining all the meaningful features including clinical features (age) that also favorably contribute to the survival days prediction for the glioma's patients. To predict the survival days for each patient, the selected features are trained to analyze the performance of various regression techniques like random forest (RF), decision tree (DT), and XGBoost. Our proposed combined feature-based method achieved the highest performance for survival days prediction over the state-of-the-art methods. We also perform extensive experiments to show the effectiveness of each feature extraction method. The experimental results infer that deep learning-based features along with radiomic features and clinical features are truly vital paradigms to estimate survival days. 相似文献
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A novel automatic image segmentation technique in magnetic resonance imaging (MRI) based on di-phase midway convolution and deconvolution network is proposed. It consists of three convolutional and deconvolutional blocks for downsampling and upsampling layers respectively. In first block, each input slice is separately convolved using two paths with 3 × 3 and 7 × 7 kernels to produce different feature maps. Then the mean value of these feature maps is processed into upcoming blocks in downsampling and upsampling layers. This processed outcome is classified and segmented using softmax classification. Further, the volume, probability density distribution of tumor, and normal tissue regions are calculated using tissue-type mapping technique. This method is extensively tested with BRATS 2012, BRATS 2013, and BRATS 2018 data sets. Our experimental results achieved higher dice similarity coefficient values of 24.3%, 27.5%, and 3.4%, respectively, for these three data sets when compared to the state-of-art brain tumor segmentation methods. 相似文献