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Detection and Localization of Early-Stage Multiple Brain Tumors Using a Hybrid Technique of Patch-Based Processing, k-means Clustering and Object Counting. 基于Patch-Based Processing、k-means聚类和目标计数混合技术的早期多发性脑肿瘤检测与定位。
IF 7.6 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2020-01-06 eCollection Date: 2020-01-01 DOI: 10.1155/2020/9035096
Mohamed Nasor, Walid Obaid

Brain tumors are a major health problem that affect the lives of many people. These tumors are classified as benign or cancerous. The latter can be fatal if not properly diagnosed and treated. Therefore, the diagnosis of brain tumors at the early stages of their development can significantly improve the chances of patient's full recovery after treatment. In addition to laboratory analyses, clinicians and surgeons extract information from medical images, recorded by various systems such as magnetic resonance imaging (MRI), X-ray, and computed tomography (CT). The extracted information is used to identify the essential characteristics of brain tumors (location, size, and type) in order to achieve an accurate diagnosis to determine the most appropriate treatment protocol. In this paper, we present an automated machine vision technique for the detection and localization of brain tumors in MRI images at their very early stages using a combination of k-means clustering, patch-based image processing, object counting, and tumor evaluation. The technique was tested on twenty real MRI images and was found to be capable of detecting multiple tumors in MRI images regardless of their intensity level variations, size, and location including those with very small sizes. In addition to its use for diagnosis, the technique can be integrated into automated treatment instruments and robotic surgery systems.

脑肿瘤是影响许多人生活的主要健康问题。这些肿瘤分为良性和癌性。如果诊断和治疗不当,后者可能是致命的。因此,在脑肿瘤发展的早期阶段进行诊断,可以显著提高患者治疗后完全康复的机会。除了实验室分析外,临床医生和外科医生还从磁共振成像(MRI)、x射线和计算机断层扫描(CT)等各种系统记录的医学图像中提取信息。提取的信息用于识别脑肿瘤的基本特征(位置、大小和类型),以实现准确的诊断,确定最合适的治疗方案。在本文中,我们提出了一种自动机器视觉技术,用于在MRI图像的早期阶段检测和定位脑肿瘤,该技术结合了k-means聚类、基于补丁的图像处理、目标计数和肿瘤评估。该技术在20张真实的MRI图像上进行了测试,发现能够检测MRI图像中的多个肿瘤,无论其强度水平变化,大小和位置如何,包括那些非常小的肿瘤。除了用于诊断之外,该技术还可以集成到自动化治疗仪器和机器人手术系统中。
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引用次数: 24
Corrigendum to “Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery” “肿瘤切除手术中脑转移的术中成像方式和补偿”的勘误表
IF 7.6 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2019-10-01 DOI: 10.1155/2019/9249016
Siming Bayer, A. Maier, M. Ostermeier, R. Fahrig
[This corrects the article DOI: 10.1155/2017/6028645.].
[这更正了文章DOI: 10.1155/2017/6028645]。
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引用次数: 0
A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems 交互式图像分割系统的半自动化可用性评估框架
IF 7.6 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2019-09-01 DOI: 10.1155/2019/1464592
Mario Amrehn, S. Steidl, Reinier Kortekaas, Maddalena Strumia, M. Weingarten, M. Kowarschik, A. Maier
For complex segmentation tasks, the achievable accuracy of fully automated systems is inherently limited. Specifically, when a precise segmentation result is desired for a small amount of given data sets, semi-automatic methods exhibit a clear benefit for the user. The optimization of human computer interaction (HCI) is an essential part of interactive image segmentation. Nevertheless, publications introducing novel interactive segmentation systems (ISS) often lack an objective comparison of HCI aspects. It is demonstrated that even when the underlying segmentation algorithm is the same throughout interactive prototypes, their user experience may vary substantially. As a result, users prefer simple interfaces as well as a considerable degree of freedom to control each iterative step of the segmentation. In this article, an objective method for the comparison of ISS is proposed, based on extensive user studies. A summative qualitative content analysis is conducted via abstraction of visual and verbal feedback given by the participants. A direct assessment of the segmentation system is executed by the users via the system usability scale (SUS) and AttrakDiff-2 questionnaires. Furthermore, an approximation of the findings regarding usability aspects in those studies is introduced, conducted solely from the system-measurable user actions during their usage of interactive segmentation prototypes. The prediction of all questionnaire results has an average relative error of 8.9%, which is close to the expected precision of the questionnaire results themselves. This automated evaluation scheme may significantly reduce the resources necessary to investigate each variation of a prototype's user interface (UI) features and segmentation methodologies.
对于复杂的分割任务,完全自动化系统的可实现精度是固有的有限的。具体来说,当需要对少量给定数据集进行精确分割时,半自动方法对用户有明显的好处。人机交互优化是交互式图像分割的重要组成部分。然而,介绍新型交互式分割系统(ISS)的出版物往往缺乏对HCI方面的客观比较。研究表明,即使整个交互式原型的底层分割算法相同,其用户体验也可能有很大差异。因此,用户更喜欢简单的界面以及相当大的自由度来控制分割的每个迭代步骤。本文在广泛的用户研究的基础上,提出了一种客观的ISS比较方法。总结性的定性内容分析是通过抽象参与者给出的视觉和言语反馈来进行的。用户通过系统可用性量表(SUS)和AttrakDiff-2问卷对分割系统进行直接评估。此外,还介绍了这些研究中关于可用性方面的发现的近似值,仅从系统可测量的用户在使用交互式分割原型期间的行为中进行。所有问卷结果的预测平均相对误差为8.9%,接近问卷结果本身的预期精度。这种自动化评估方案可以显著减少调查原型的用户界面(UI)特征和分割方法的每个变化所需的资源。
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引用次数: 6
Automated Estimation of Acute Infarct Volume from Noncontrast Head CT Using Image Intensity Inhomogeneity Correction 使用图像强度不均匀性校正的非对比头部CT急性梗死体积的自动估计
IF 7.6 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2019-08-21 DOI: 10.1155/2019/1720270
K. Cauley, G. Mongelluzzo, S. Fielden
Identification of early ischemic changes (EIC) on noncontrast head CT scans performed within the first few hours of stroke onset may have important implications for subsequent treatment, though early stroke is poorly delimited on these studies. Lack of sharp lesion boundary delineation in early infarcts precludes manual volume measures, as well as measures using edge-detection or region-filling algorithms. We wished to test a hypothesis that image intensity inhomogeneity correction may provide a sensitive method for identifying the subtle regional hypodensity which is characteristic of early ischemic infarcts. A digital image analysis algorithm was developed using image intensity inhomogeneity correction (IIC) and intensity thresholding. Two different IIC algorithms (FSL and ITK) were compared. The method was evaluated using simulated infarcts and clinical cases. For synthetic infarcts, measured infarct volumes demonstrated strong correlation to the true lesion volume (for 20% decreased density “infarcts,” Pearson r = 0.998 for both algorithms); both algorithms demonstrated improved accuracy with increasing lesion size and decreasing lesion density. In clinical cases (41 acute infarcts in 30 patients), calculated infarct volumes using FSL IIC correlated with the ASPECTS scores (Pearson r = 0.680) and the admission NIHSS (Pearson r = 0.544). Calculated infarct volumes were highly correlated with the clinical decision to treat with IV-tPA. Image intensity inhomogeneity correction, when applied to noncontrast head CT, provides a tool for image analysis to aid in detection of EIC, as well as to evaluate and guide improvements in scan quality for optimal detection of EIC.
在中风发作的最初几个小时内进行的非光栅头部CT扫描中,识别早期缺血性变化(EIC)可能对后续治疗具有重要意义,尽管这些研究对早期中风的界定很差。早期梗死缺乏清晰的病变边界,无法进行手动体积测量,也无法使用边缘检测或区域填充算法进行测量。我们希望检验这样一种假设,即图像强度不均匀性校正可以为识别早期缺血性梗死特有的细微区域低密度提供一种灵敏的方法。利用图像强度不均匀性校正(IIC)和强度阈值技术,开发了一种数字图像分析算法。比较了两种不同的IIC算法(FSL和ITK)。该方法通过模拟梗死和临床病例进行评估。对于合成梗死,测量的梗死体积与真实病变体积具有很强的相关性(对于密度降低20%的“梗死”,两种算法的Pearson r=0.998);两种算法都显示出随着病变大小的增加和病变密度的降低而提高的准确性。在临床病例中(30例患者中有41例急性梗死),使用FSL IIC计算的梗死体积与ASPECTS评分(Pearson r=0.680)和入院NIHSS(Pearsonr=0.544)相关。计算的梗死容量与静脉注射tPA治疗的临床决定高度相关。当应用于非光栅头CT时,图像强度不均匀性校正提供了一种用于图像分析的工具,以帮助检测EIC,并评估和指导扫描质量的改进,以实现EIC的最佳检测。
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引用次数: 9
Magnetic Resonance Angiography Shows Increased Arterial Blood Supply Associated with Murine Mammary Cancer. 磁共振血管造影显示动脉供血增加与小鼠乳腺癌有关。
IF 7.6 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2019-01-01 DOI: 10.1155/2019/5987425
Devkumar Mustafi, Abby Leinroth, Xiaobing Fan, Erica Markiewicz, Marta Zamora, Jeffrey Mueller, Suzanne D Conzen, Gregory S Karczmar
Breast cancer is a major cause of morbidity and mortality in Western women. Tumor neoangiogenesis, the formation of new blood vessels from pre-existing ones, may be used as a prognostic marker for cancer progression. Clinical practice uses dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) to detect cancers based on increased blood flow and capillary permeability. However, DCE-MRI requires repeated injections of contrast media. Therefore we explored the use of noninvasive time-of-flight (TOF) MR angiography for serial studies of mouse mammary glands to measure the number and size of arteries feeding mammary glands with and without cancer. Virgin female C3(1) SV40 TAg mice (n=9), aged 18-20 weeks, were imaged on a 9.4 Tesla small animal scanner. Multislice T2-weighted (T2W) images and TOF-MRI angiograms were acquired over inguinal mouse mammary glands. The data were analyzed to determine tumor burden in each mammary gland and the volume of arteries feeding each mammary gland. After in vivo MRI, inguinal mammary glands were excised and fixed in formalin for histology. TOF angiography detected arteries with a diameter as small as 0.1 mm feeding the mammary glands. A significant correlation (r=0.79; p< 0.0001) was found between tumor volume and the arterial blood volume measured in mammary glands. Mammary arterial blood volumes ranging from 0.08 mm3 to 3.81 mm3 were measured. Tumors and blood vessels found on in vivo T2W and TOF images, respectively, were confirmed with ex vivo histological images. These results demonstrate increased recruitment of arteries to mammary glands with cancer, likely associated with neoangiogenesis. Neoangiogenesis may be detected by TOF angiography without injection of contrast agents. This would be very useful in mouse models where repeat placement of I.V. lines is challenging. In addition, analogous methods could be tested in humans to evaluate the vasculature of suspicious lesions without using contrast agents.
乳腺癌是西方妇女发病和死亡的主要原因。肿瘤新生血管生成,即由原有血管形成的新血管,可作为癌症进展的预后标志。临床实践使用动态对比增强磁共振成像(DCE-MRI)来检测基于血流量和毛细血管通透性增加的癌症。然而,DCE-MRI需要反复注射造影剂。因此,我们探索了使用无创飞行时间(TOF)磁共振血管造影对小鼠乳腺进行系列研究,以测量喂养患有和未患癌症的乳腺的动脉的数量和大小。雌性C3(1) SV40 TAg小鼠(n=9),年龄18-20周龄,在9.4特斯拉小动物扫描仪上成像。获得小鼠腹股沟乳腺的T2W和TOF-MRI血管造影。对数据进行分析,以确定每个乳腺的肿瘤负荷和每个乳腺供血动脉的体积。在体内MRI后,切除腹股沟乳腺,在福尔马林中固定组织学。TOF血管造影检测到直径小至0.1 mm的动脉喂养乳腺。相关性显著(r=0.79;乳腺动脉血容量与肿瘤体积的差异有P < 0.0001)。测量乳腺动脉血容量范围为0.08 ~ 3.81 mm3。在体内T2W和TOF图像上发现的肿瘤和血管分别用离体组织学图像证实。这些结果表明,患癌乳腺的动脉募集增加,可能与新生血管生成有关。在不注射造影剂的情况下,可以通过TOF血管造影检测新血管生成。这将在小鼠模型中非常有用,因为重复放置静脉注射线是具有挑战性的。此外,类似的方法可以在人类中进行测试,以评估可疑病变的血管系统,而无需使用造影剂。
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引用次数: 4
Review: On Segmentation of Nodules from Posterior and Anterior Chest Radiographs. 回顾:胸部前后片结节分割的研究。
IF 7.6 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2018-10-18 eCollection Date: 2018-01-01 DOI: 10.1155/2018/9752638
S K Chaya Devi, T Satya Savithri

Lung cancer is one of the major types of cancer in the world. Survival rate can be increased if the disease can be identified early. Posterior and anterior chest radiography and computerized tomography scans are the most used diagnosis techniques for detecting tumor from lungs. Posterior and anterior chest radiography requires less radiation dose and is available in most of the diagnostic centers and it costs less compared to the remaining diagnosis techniques. So PA chest radiography became the most commonly used technique for lung cancer detection. Because of superimposed anatomical structures present in the image, sometimes radiologists cannot find abnormalities from the image. To help radiologists in diagnosing tumor from PA chest radiographic images range of CAD scheme has been developed for the past three decades. These computerized tools may be used by radiologists as a second opinion in detecting tumor. Literature survey on detecting tumors from chest graphs is presented in this paper.

肺癌是世界上主要的癌症类型之一。如果能及早发现这种疾病,可以提高生存率。胸部后路和前路x线摄影和计算机断层扫描是检测肺部肿瘤最常用的诊断技术。胸部后路和前路x线摄影需要较少的辐射剂量,在大多数诊断中心都可以获得,与其他诊断技术相比,它的成本更低。因此,PA胸片成为肺癌检测中最常用的技术。由于图像中存在重叠的解剖结构,有时放射科医生无法从图像中发现异常。为了帮助放射科医生从胸片图像中诊断肿瘤,CAD方案的范围已经发展了三十年。这些计算机化的工具可能被放射科医生用作检测肿瘤的第二意见。本文综述了从胸部图像中检测肿瘤的相关文献。
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引用次数: 4
An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm. 基于可调q -小波和萤火虫特征选择算法的癫痫自动检测方法。
IF 7.6 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2018-09-10 eCollection Date: 2018-01-01 DOI: 10.1155/2018/5812872
Ahmed I Sharaf, Mohamed Abu El-Soud, Ibrahim M El-Henawy

Detection of epileptic seizures using an electroencephalogram (EEG) signals is a challenging task that requires a high level of skilled neurophysiologists. Therefore, computer-aided detection provides an asset to the neurophysiologist in interpreting the EEG. This paper introduces a novel approach to recognize and classify the epileptic seizure and seizure-free EEG signals automatically by an intelligent computer-aided method. Moreover, the prediction of the preictal phase of the epilepsy is proposed to assist the neurophysiologist in the clinic. The proposed method presents two perspectives for the EEG signal processing to detect and classify the seizures and seizure-free signals. The first perspectives consider the EEG signal as a nonlinear time series. A tunable Q-wavelet is applied to decompose the signal into smaller segments called subbands. Then a chaotic, statistical, and power spectrum features sets are extracted from each subband. The second perspectives process the EEG signal as an image; hence the gray-level co-occurrence matrix is determined from the image to obtain the textures of contrast, correlation, energy, and homogeneity. Due to a large number of features obtained, a feature selection algorithm based on firefly optimization was applied. The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. Afterward, a dataset from the University of Bonn, Germany, is used for benchmarking and evaluation. The proposed approach provided a significant result compared with other recent work regarding accuracy, recall, specificity, F-measure, and Matthew's correlation coefficient.

使用脑电图(EEG)信号检测癫痫发作是一项具有挑战性的任务,需要高水平的熟练神经生理学家。因此,计算机辅助检测为神经生理学家解释脑电图提供了一种资产。本文介绍了一种利用智能计算机辅助对癫痫发作和非癫痫发作脑电信号进行自动识别和分类的新方法。此外,预测癫痫的前期提出,以协助临床神经生理学家。该方法为脑电图信号处理提供了检测和分类癫痫发作和非癫痫发作信号的两个视角。第一种观点认为脑电信号是一个非线性时间序列。可调谐的q -小波被应用于将信号分解成称为子带的更小的片段。然后从每个子带提取混沌、统计和功率谱特征集。第二种视角将脑电信号作为图像处理;由此,从图像中确定灰度共现矩阵,得到对比度、相关性、能量和均匀性纹理。由于获得的特征数量较多,采用了基于萤火虫优化的特征选择算法。萤火虫优化减少了原始特征集,并生成了一个简化的紧凑集。训练随机森林分类器对癫痫发作和非癫痫发作信号进行分类和预测。之后,使用来自德国波恩大学的数据集进行基准测试和评估。与其他最近的研究相比,所提出的方法在准确性、召回率、特异性、f测量和马修相关系数方面提供了显著的结果。
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引用次数: 26
Super-Resolution of Magnetic Resonance Images via Convex Optimization with Local and Global Prior Regularization and Spectrum Fitting. 基于局部和全局先验正则化和频谱拟合的凸优化磁共振图像超分辨率。
IF 7.6 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2018-09-02 eCollection Date: 2018-01-01 DOI: 10.1155/2018/9262847
Naoki Kawamura, Tatsuya Yokota, Hidekata Hontani

Given a low-resolution image, there are many challenges to obtain a super-resolved, high-resolution image. Many of those approaches try to simultaneously upsample and deblur an image in signal domain. However, the nature of the super-resolution is to restore high-frequency components in frequency domain rather than upsampling in signal domain. In that sense, there is a close relationship between super-resolution of an image and extrapolation of the spectrum. In this study, we propose a novel framework for super-resolution, where the high-frequency components are theoretically restored with respect to the frequency fidelities. This framework helps to introduce multiple simultaneous regularizers in both signal and frequency domains. Furthermore, we propose a new super-resolution model where frequency fidelity, low-rank (LR) prior, low total variation (TV) prior, and boundary prior are considered at once. The proposed method is formulated as a convex optimization problem which can be solved by the alternating direction method of multipliers. The proposed method is the generalized form of the multiple super-resolution methods such as TV super-resolution, LR and TV super-resolution, and the Gerchberg method. Experimental results show the utility of the proposed method comparing with some existing methods using both simulational and practical images.

对于低分辨率图像,获得超分辨率、高分辨率图像存在许多挑战。其中许多方法试图同时在信号域中对图像进行上采样和去模糊处理。然而,超分辨率的本质是在频域恢复高频成分,而不是在信号域上采样。从这个意义上说,在图像的超分辨率和光谱的外推之间有密切的关系。在这项研究中,我们提出了一种新的超分辨率框架,其中高频成分理论上相对于频率保真度恢复。该框架有助于在信号域和频域同时引入多个正则化器。此外,我们提出了一种同时考虑频率保真度、低秩(LR)先验、低总变差(TV)先验和边界先验的超分辨率模型。该方法被表述为一个凸优化问题,可以用乘子交替方向法求解。该方法是TV超分辨率、LR和TV超分辨率等多种超分辨率方法以及Gerchberg方法的推广形式。仿真和实际图像的实验结果表明了该方法与现有方法的有效性。
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引用次数: 4
Corrigendum to "Polychromatic Iterative Statistical Material Image Reconstruction for Photon-Counting Computed Tomography". “光子计数计算机断层扫描的多色迭代统计材料图像重建”的勘误表。
IF 7.6 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2018-08-09 eCollection Date: 2018-01-01 DOI: 10.1155/2018/5932653
Thomas Weidinger, Thorsten M Buzug, Thomas G Flohr, Steffen Kappler, Karl Stierstorfer

[This corrects the article DOI: 10.1155/2016/5871604.].

[这更正了文章DOI: 10.1155/2016/5871604]。
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引用次数: 0
Medical Image Blind Integrity Verification with Krawtchouk Moments. 基于克rawtchouk矩的医学图像盲完整性验证。
IF 7.6 Q2 ENGINEERING, BIOMEDICAL Pub Date : 2018-07-02 eCollection Date: 2018-01-01 DOI: 10.1155/2018/2572431
Xu Zhang, Xilin Liu, Yang Chen, Huazhong Shu

A new blind integrity verification method for medical image is proposed in this paper. It is based on a new kind of image features, known as Krawtchouk moments, which we use to distinguish the original images from the modified ones. Basically, with our scheme, image integrity verification is accomplished by classifying images into the original and modified categories. Experiments conducted on medical images issued from different modalities verified the validity of the proposed method and demonstrated that it can be used to detect and discriminate image modifications of different types with high accuracy. We also compared the performance of our scheme with a state-of-the-art solution suggested for medical images-solution that is based on histogram statistical properties of reorganized block-based Tchebichef moments. Conducted tests proved the better behavior of our image feature set.

提出了一种新的医学图像的盲完整性验证方法。它是基于一种新的图像特征,称为克劳tchouk矩,我们用它来区分原始图像和修改后的图像。基本上,我们的方案通过将图像分为原始类别和修改类别来完成图像完整性验证。对不同模式的医学图像进行了实验,验证了该方法的有效性,并表明该方法能够以较高的准确率检测和区分不同类型的图像修改。我们还将该方案的性能与针对医学图像提出的最先进的解决方案进行了比较,该解决方案基于重组块的chebichef矩的直方图统计特性。进行的测试证明了我们的图像特征集的更好的行为。
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引用次数: 5
期刊
International Journal of Biomedical Imaging
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