共查询到20条相似文献，搜索用时 15 毫秒
1.
R. Anitha D. Siva Sundhara Raja 《International journal of imaging systems and technology》2017,27(4):354-360
The abnormal development of cells in brain leads to the formation of tumors in brain. In this article, image fusion based brain tumor detection and segmentation methodology is proposed using convolutional neural networks (CNN). This proposed methodology consists of image fusion, feature extraction, classification, and segmentation. Discrete wavelet transform (DWT) is used for image fusion and enhanced brain image is obtained by fusing the coefficients of the DWT transform. Further, Grey Level Co‐occurrence Matrix features are extracted and fed to the CNN classifier for glioma image classifications. Then, morphological operations with closing and opening functions are used to segment the tumor region in classified glioma brain image. 相似文献
2.
Balakumaresan Ragupathy Manivannan Karunakaran 《International journal of imaging systems and technology》2021,31(1):379-390
This article develops a methodology for meningioma brain tumor detection process using fuzzy logic based enhancement and co‐active adaptive neuro fuzzy inference system and U‐Net convolutional neural network classification methods. The proposed meningioma tumor detection process consists of the following stages as, enhancement, feature extraction, and classifications. The enhancement of the source brain image is done using fuzzy logic and then dual tree‐complex wavelet transform is applied to this enhanced image at different levels of scale. The features are computed from the decomposed sub band images and these features are further classified using CANFIS classification method which identifies the meningioma brain image from nonmeningioma brain image. The performance of the proposed meningioma brain tumor detection and segmentation system is analyzed in terms of sensitivity, specificity, segmentation accuracy, and Dice coefficient index with detection rate. 相似文献
3.
Tumors are formed in brain due to the uncontrolled development of cells. These tumors can be cured if it is timely detected and by proper medication. This article proposes a computer‐aided automatic detection and diagnosis of meningioma brain tumors in brain images using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. The proposed system consists of feature extraction, classification, and segmentation and diagnosis sections. In this article, Grey level Co‐occurrence Matric (GLCM) and Grid features are extracted from the brain image and these features are classified using ANFIS classifier into normal or abnormal. Then, morphological operations are used to segment the abnormal regions in brain image. Based on the location of these abnormal regions in brain tissues, the segmented tumor regions are diagnosed. 相似文献
4.
Jasmine Hephzipah Johnpeter Thirumurugan Ponnuchamy 《International journal of imaging systems and technology》2019,29(4):431-438
The development of abnormal cells in human brain leads to the formation of tumors. This article proposes an efficient approach for brain tumor detection and segmentation using image fusion and co-active adaptive neuro fuzzy inference system (CANFIS) classification method. The brain MRI images are fused and the dual tree complex wavelet transform is applied on the fused image. Then, the statistical features, local ternary pattern features and gray level co-occurrence matrix features. These extracted features are classified using CANFIS classification approach for the classification of source brain MRI image into either normal or abnormal. Further, morphological operations are applied on the abnormal brain MRI image for segmenting the tumor regions. The proposed methodology is evaluated with respect to the performance metrics sensitivity, specificity, positive predictive value, negative predictive value, tumor segmentation accuracy with detection rate. The proposed image fusion based brain tumor detection and classification methodology stated in this article achieves 96.5% of average sensitivity, 97.7% of average specificity, 87.6% of positive predictive value, 96.6% of negative predictive value, and 98.8% of average accuracy. 相似文献
5.
Muhammad Javaid Iqbal Muhammad Waseem Iqbal Muhammad Anwar Muhammad Murad Khan Abd Jabar Nazimi Mohammad Nazir Ahmad 《计算机、材料和连续体（英文）》2023,74(3):5267-5281
The brain tumour is the mass where some tissues become old or damaged, but they do not die or not leave their space. Mainly brain tumour masses occur due to malignant masses. These tissues must die so that new tissues are allowed to be born and take their place. Tumour segmentation is a complex and time-taking problem due to the tumour’s size, shape, and appearance variation. Manually finding such masses in the brain by analyzing Magnetic Resonance Images (MRI) is a crucial task for experts and radiologists. Radiologists could not work for large volume images simultaneously, and many errors occurred due to overwhelming image analysis. The main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning approaches. This research study proposed an automatic model for tumor segmentation in MRI images. The proposed model has a few significant steps, which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative (NIFTI) volumes into the 3D NumPy array. In the second step, the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated parameters. In the third step, the proposed model uses state-of-the-art Medical Image Computing and Computer-Assisted Intervention (MICCAI) BRATS 2018 dataset with MRI modalities such as T1, T1Gd, T2, and Fluid-attenuated inversion recovery (FLAIR). Tumour types in MRI images are classified according to the tumour masses. Labelling of these masses carried by state-of-the-art approaches such that the first is enhancing tumour (label 4), edema (label 2), necrotic and non-enhancing tumour core (label 1), and the remaining region is label 0 such that edema (whole tumour), necrosis and active. The proposed model is evaluated and gets the Dice Coefficient (DSC) value for High-grade glioma (HGG) volumes for their test set-a, test set-b, and test set-c 0.9795, 0.9855 and 0.9793, respectively. DSC value for the Low-grade glioma (LGG) volumes for the test set is 0.9950, which shows the proposed model has achieved significant results in segmenting the tumour in MRI using deep learning approaches. The proposed model is fully automatic that can implement in clinics where human experts consume maximum time to identify the tumorous region of the brain MRI. The proposed model can help in a way it can proceed rapidly by treating the tumor segmentation in MRI. 相似文献
6.
Yasmeen Al-Saeed Wael A. Gab-Allah Hassan Soliman Maysoon F. Abulkhair Wafaa M. Shalash Mohammed Elmogy 《计算机、材料和连续体（英文）》2022,71(3):4871-4894
One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main stages; liver segmentation using Fast Generalized Fuzzy C-Means, tumor segmentation using dynamic thresholding, and the tumor's classification into malignant/benign using support vector machines classifier. The performance of the proposed system was evaluated using three liver benchmark datasets, which are MICCAI-Sliver07, LiTS17, and 3Dircadb. The proposed computer adided diagnosis system achieved an average accuracy of 96.75%, sensetivity of 96.38%, specificity of 95.20% and Dice similarity coefficient of 95.13%. 相似文献
7.
Yifan Wu;Longjiao Zhu;Yangzi Zhang;Wentao Xu; 《Small (Weinheim an der Bergstrasse, Germany)》2024,20(2):2304852
Riboswitches have received significant attention over the last two decades for their multiple functionalities and great potential for applications in various fields. This article highlights and reviews the recent advances in biosensing and biotherapy. These fields involve a wide range of applications, such as food safety detection, environmental monitoring, metabolic engineering, live cell imaging, wearable biosensors, antibacterial drug targets, and gene therapy. The discovery, origin, and optimization of riboswitches are summarized to help readers better understand their multidimensional applications. Finally, this review discusses the multidimensional challenges and development of riboswitches in order to further expand their potential for novel applications. 相似文献
8.
Ezhilmathi Nagarathinam Thirumurugan Ponnuchamy 《International journal of imaging systems and technology》2019,29(4):510-517
Abnormal cells in human brain lead to the development of tumors. Manual detection of this tumor region is a time-consuming process. Hence, this paper proposes an efficient and automated computer-aided methodology for brain tumor detection and segmentation using image registration technique and classification approaches. This proposed work consists of the following modules: image registration, contourlet transform, and feature extraction with feature normalization, classification, and segmentation. The extracted features are optimized using genetic algorithm, and then an adaptive neuro-fuzzy inference system classification approach is used to classify the features for the detection and segmentation of tumor regions in brain magnetic resonance imaging. A quantitative analysis is performed to evaluate the proposed methodology for brain tumor detection using sensitivity, specificity, segmentation accuracy, precision, and Dice similarity coefficient. 相似文献
9.
Visual tracking is a challenging issue in the field of computer vision due to the objects’ intricate appearance variation. To adapt the change of the appearance, multiple channel features which could provide more information are used. However, the low level feature could not represent the structure of the object. In this paper, a superpixel-based adaptive tracking algorithm by using color histogram and haar-like feature is proposed, whose feature is classified into the middle level. Based on the superpixel representation of video frames, the haar-like feature is extracted at the superpixel level as the local feature, and the color histogram feature is applied with the combination of background subtraction method as the frame feature. Then, local features are clustered and weighted according to the target label and the location center. Superpixel-based appearance model is measured by using the sum of the voting map, and the candidate with the highest score is selected as the tracking result. Finally, an efficient template updating scheme is introduced to obtain the robust results and improve the computational efficiency. The proposed algorithm is evaluated on eight challenging video sequences and experimental results demonstrate that the proposed method can get better performance on occlusion, illumination variation and transformation. 相似文献
10.
Statistical process control charts have been successfully used to monitor process stability in various industries. The need to simultaneously monitor two or more quality characteristics has led to the prevalent adoption of multivariate control charts. However, out-of-control signals in multivariate control charts may be caused by one or more variables, or a set of variables. Therefore, effective quality control requires not only the rapid detection of process fluctuations, but also the correct identification of the variable(s) responsible for those changes. This study approaches the diagnosis of out-of-control signals as a classification task and proposes a support vector machine (SVM)-based ensemble classification model focused on variance shifts in multivariate processes. We address the issues of data diversity and ensemble method in constructing an ensemble model. Simulation results demonstrate the effectiveness of the proposed ensemble classification model in identifying the source of variance change. The proposed method clearly outperforms single classifiers as well as other comparable models including bagging and boosting. The results also reveal that the use of extracted features as input vectors for SVM provides better classification performance than the use of raw data. The proposed SVM-based ensemble classification system provides a reliable tool for the interpretation of out-of-control signals in multivariate process control. 相似文献
11.
Stance detection is the task of attitude identification toward a standpoint. Previous work of stance detection has focused on feature extraction but ignored the fact that irrelevant features exist as noise during higher-level abstracting. Moreover, because the target is not always mentioned in the text, most methods have ignored target information. In order to solve these problems, we propose a neural network ensemble method that combines the timing dependence bases on long short-term memory (LSTM) and the excellent extracting performance of convolutional neural networks (CNNs). The method can obtain multi-level features that consider both local and global features. We also introduce attention mechanisms to magnify target information-related features. Furthermore, we employ sparse coding to remove noise to obtain characteristic features. Performance was improved by using sparse coding on the basis of attention employment and feature extraction. We evaluate our approach on the SemEval-2016Task 6-A public dataset, achieving a performance that exceeds the benchmark and those of participating teams. 相似文献
12.
实验采用电化学沉积法在钛合金表面制备了纳米羟基磷灰石涂层(nHA)、纳米和微米级羟基磷灰石/壳聚糖复合涂层(nHA/CTS,mHA/CTS),并应用XRD、SEM和FTIR对涂层的理化特性进行了表征。然后将人脑胶质母细胞瘤细胞系U87(U87)与3种涂层共培养,并比较3种涂层诱导U87细胞凋亡的能力。通过MTT法细胞生长抑制实验检测以及电镜下膜层表面细胞形态观察,发现nHA膜层比nHA/CTS和mHA/CTS能更有效地抑制胶质瘤细胞的增殖,具有明显的体外抗肿瘤作用。 相似文献
13.
Asif Mehmood Muhammad Attique Khan Usman Tariq Chang-Won Jeong Yunyoung Nam Reham R. Mostafa Amira ElZeiny 《计算机、材料和连续体（英文）》2022,70(1):343-361
Background—Human Gait Recognition (HGR) is an approach based on biometric and is being widely used for surveillance. HGR is adopted by researchers for the past several decades. Several factors are there that affect the system performance such as the walking variation due to clothes, a person carrying some luggage, variations in the view angle. Proposed—In this work, a new method is introduced to overcome different problems of HGR. A hybrid method is proposed or efficient HGR using deep learning and selection of best features. Four major steps are involved in this work-preprocessing of the video frames, manipulation of the pre-trained CNN model VGG-16 for the computation of the features, removing redundant features extracted from the CNN model, and classification. In the reduction of irrelevant features Principal Score and Kurtosis based approach is proposed named PSbK. After that, the features of PSbK are fused in one materix. Finally, this fused vector is fed to the One against All Multi Support Vector Machine (OAMSVM) classifier for the final results. Results—The system is evaluated by utilizing the CASIA B database and six angles 00°, 18°, 36°, 54°, 72°, and 90° are used and attained the accuracy of 95.80%, 96.0%, 95.90%, 96.20%, 95.60%, and 95.50%, respectively. Conclusion—The comparison with recent methods show the proposed method work better. 相似文献
14.
Linda Lee Ho Roberto da Costa Quinino Anderson Laécio Galindo Trindade 《Quality and Reliability Engineering International》2011,27(8):1087-1093
The np‐control chart has been used to monitor the conforming fraction in process production, and it is assumed that no classification errors occur during the inspection process. Increases in the sample size and/or the number of repeated classifications of the inspected items can reduce the impact of the classification errors. In this paper, an np ‐control chart is proposed, and the monitored statistics are based on the results of independent repeated classifications with classification errors during the inspection process. The aim of the proposed control chart is to have the same performance as a control chart without classification errors. Numerical examples illustrate the proposal. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
15.
Real-time detection of driver fatigue status is of great significance for road traffic safety. In this paper, a proposed novel driver fatigue detection method is able to detect the driver’s fatigue status around the clock. The driver’s face images were captured by a camera with a colored lens and an infrared lens mounted above the dashboard. The landmarks of the driver’s face were labeled and the eye-area wassegmented. By calculating the aspect ratios of the eyes, the duration of eye closure, frequency of blinks and PERCLOS of both colored and infrared, fatigue can be detected. Based on the change of light intensity detected by a photosensitive device, the weight matrix of the colored features and the infrared features was adjusted adaptively to reduce the impact of lighting on fatigue detection. Video samples of the driver’s face were recorded in the test vehicle. After training the classification model, the results showed that our method has high accuracy on driver fatigue detection in both daytime and nighttime. 相似文献
16.
The ability of cells to adapt their mechanical properties to those of the surrounding microenvironment (tensional homeostasis) has been implicated in the progression of a variety of solid tumours, including the brain tumour glioblastoma multiforme (GBM). GBM tumour cells are highly sensitive to extracellular matrix (ECM) stiffness and overexpress a variety of focal adhesion proteins, such as talin. While talin has been shown to play critical early roles in integrin-based force-sensing in non-tumour cells, it remains unclear whether this protein contributes to tensional homeostasis in GBM cells. Here, we investigate the role of the talin isoform talin-1 in enabling human GBM cells to adapt to ECM stiffness. We show that human GBM cells express talin-1, and we use RNA interference to suppress talin-1 expression without affecting levels of talin-2, vinculin or phosphorylated focal adhesion kinase. Knockdown of talin-1 strongly reduces both cell spreading area and random migration speed but does not significantly affect overall focal adhesion size distributions. Most strikingly, atomic force microscopy indentation reveals that talin-1 suppression compromises adaptation of cell stiffness to changes in ECM stiffness. Together, these data support a role for talin-1 in the maintenance of tensional homeostasis in GBM and suggest a functional role for enriched talin expression in this tumour. 相似文献
17.
Human action recognition under complex environment is a challenging work. Recently, sparse representation has achieved excellent results of dealing with human action recognition problem under different conditions. The main idea of sparse representation classification is to construct a general classification scheme where the training samples of each class can be considered as the dictionary to express the query class, and the minimal reconstruction error indicates its corresponding class. However, how to learn a discriminative dictionary is still a difficult work. In this work, we make two contributions. First, we build a new and robust human action recognition framework by combining one modified sparse classification model and deep convolutional neural network (CNN) features. Secondly, we construct a novel classification model which consists of the representation-constrained term and the coefficients incoherence term. Experimental results on benchmark datasets show that our modified model can obtain competitive results in comparison to other state-of-the-art models. 相似文献
18.
Abstract In recent years, Active Contour Models (ACMs) have become powerful tools for object detection and image segmentation in computer vision and image processing applications. This paper presents a new energy function in parametric active contour models for object detection and image segmentation. In the proposed method, a new pressure energy called “texture pressure energy” is added to the energy function of the parametric active contour model to detect and segment a textured object against a textured background. In this scheme, the texture features of the contour are calculated by a moment based method. Then by comparing these features with texture features of the object, the contour curve is expanded or contracted in order to be adapted to the object boundaries. Experimental results show that the proposed method has more efficient and accurate segmenting functionality than the traditional method when both object and background have texture properties. 相似文献
19.
Hai Lu Wang Tianyu Chen Bojian Zhang Guohui Wang Xudong Yang Kunlin Wu Yifan Wang 《Small (Weinheim an der Bergstrasse, Germany)》2023,19(21):2206830
The progress from intelligent interactions and supplemented/augmented reality requires artificial skins to shift from the single-functional tactile paradigm. Dual-responsive sensors that can both detect pre-contact proximal events and tactile pressure levels enrich the perception dimensions and deliver additional cognitive information. Previous dual-responsive sensors show very limited utilizations only in proximity perception or approaching switches. Whereas, the approaching inputs from the environment should be able to convey more valuable messages. Herein, a flexible iontronic dual-responsive artificial skin is present. The artificial skin is sensitive to external object's applied pressure as well as its approaching, and can elicit information of target material categories encoded in the proximal inputs. Versatile applications are then demonstrated. Dual-mode human–machine interfaces are developed based on the devices, including a manipulation of virtual game characters, navigation and zooming in of electronic maps, and scrolling through electronic documents. More importantly, the proof-of-concept application of an entirely touchless material classification system is demonstrated. Three types of materials (metals, polymers, and human skins) are classified and predicted accurately. These features of the artificial skin make it highly promising for next-generation smart engineered electronics. 相似文献
20.
Image captioning involves two different major modalities (image and sentence) that convert a given image into a language that adheres to visual semantics. Almost all methods first extract image features to reduce the difficulty of visual semantic embedding and then use the caption model to generate fluent sentences. The Convolutional Neural Network (CNN) is often used to extract image features in image captioning, and the use of object detection networks to extract region features has achieved great success. However, the region features retrieved by this method are object-level and do not pay attention to fine-grained details because of the detection model’s limitation. We offer an approach to address this issue that more properly generates captions by fusing fine-grained features and region features. First, we extract fine-grained features using a panoramic segmentation algorithm. Second, we suggest two fusion methods and contrast their fusion outcomes. An X-linear Attention Network (X-LAN) serves as the foundation for both fusion methods. According to experimental findings on the COCO dataset, the two-branch fusion approach is superior. It is important to note that on the COCO Karpathy test split, CIDEr is increased up to 134.3% in comparison to the baseline, highlighting the potency and viability of our method. 相似文献