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An image segmentation approach based on fuzzy-neural-network hybrid system

Yuntao Qian, Weixin Xie
1997 Journal of Electronics (China)  
This paper presents a new solution to the image segmentation problem, which is based on fuzzy-neural-network hybrid system (FNNHS).  ...  The segmentation process consists of pre-segmentation based on region growing algorithm and region merging based on FNNHS.  ...  In this paper, we propose a new segmentation method which is based on fuzzy-neural-network hybrid system (FNNHS).  ... 
doi:10.1007/s11767-997-0009-0 fatcat:63kpqlln2nhrlfergqxrcbfb5q

Automated Segmentation of Optical Nerves by Neural Network based Region Growing

Z. FaizalKhan, Syed Usama Quadri
2015 Communications on Applied Electronics  
This research reports on segmentation of the nerves by segmenting the retinal images using Echo State Neural Networks along with the combination of region growing algorithm.  ...  From the experimental results, it has been observed that the proposed segmentation approach provides better segmentation accuracy.  ...  Communications on Applied Electronics (CAE) -ISSN : 2394-4714 Foundation of Computer Science FCS, New York, USA Volume 1 -No.5, April 2015www.caeaccess.org  ... 
doi:10.5120/cae-1543 fatcat:glq3fihbzzgajjzqvw6gibntie

UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images [article]

Hidetoshi Urakubo, Torsten Bullmann, Yoshiyuki Kubota, Shigeyuki Oba, Shin Ishii
2019 bioRxiv   pre-print
The spatial scale of the 3D reconstruction grows rapidly owing to deep neural networks (DNNs) that enable automated image segmentation.  ...  electron microscopic (EM) images.  ...  The benchmark studies have demonstrated the effectiveness of a specific class of deep neural networks (DNNs), i.e., convolutional neural networks (CNNs) (e.g., Ciresan et al. 9 ).  ... 
doi:10.1101/607366 fatcat:nswpqdmlcbdgxanx4ctk36nlle

Real-time object segmentation based on convolutional neural network with saliency optimization for picking

2018 Journal of Systems Engineering and Electronics  
By the combination of the region proposal method based on the convolutional neural network and superpixel method, the category and location information can be used to segment objects and image redundancy  ...  A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regions, allowing more processing is reserved only for these regions.  ...  locations over the entire image, region-based convolutional neural network (R-CNN) running classifier on the boxes generated by the region proposal methods; some systems do semantic segmentation on the  ... 
doi:10.21629/jsee.2018.06.17 fatcat:e6hff35mu5arnanlhvbn7efjtu

Segmentation of Aorta 3D CT Images Based on 2D Convolutional Neural Networks

Simone Bonechi, Paolo Andreini, Alessandro Mecocci, Nicola Giannelli, Franco Scarselli, Eugenio Neri, Monica Bianchini, Giovanna Maria Dimitri
2021 Electronics  
In this paper, an automatic method for aortic segmentation, based on 2D convolutional neural networks (CNNs), using 3D CT (computed axial tomography) scans as input is presented.  ...  The results obtained are promising and show that the neural networks employed can provide accurate segmentation of the aorta.  ...  In recent years, several semantic segmentation models, based on deep neural networks, have been proposed [22] [23] [24] [25] [26] .  ... 
doi:10.3390/electronics10202559 fatcat:yyeuwakgkveuxi63wpiof4xa5i

Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism

Fei Xie, Panpan Zhang, Tao Jiang, Jiao She, Xuemin Shen, Pengfei Xu, Wei Zhao, Gang Gao, Ziyu Guan
2021 Electronics  
In this paper, we proposed an end-to-end medical lesion segmentation framework based on convolutional neural networks with a dual attention mechanism, which integrates both fully and weakly supervised  ...  segmentation.  ...  Recently, lesion segmentation methods based on deep convolutional neural networks have been widely applied to medical image segmentation.  ... 
doi:10.3390/electronics10243103 fatcat:zs7ol6kkprhqza24rxrhcaw4ai

UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images

Hidetoshi Urakubo, Torsten Bullmann, Yoshiyuki Kubota, Shigeyuki Oba, Shin Ishii
2019 Scientific Reports  
The spatial scale of the 3D reconstruction increases rapidly owing to deep convolutional neural networks (CNNs) that enable automated image segmentation.  ...  We further wrapped flood-filling networks (FFNs) as a representative 3D CNN-based neuron segmentation algorithm.  ...  For automated neuron segmentation, studies have validated the effectiveness of deep convolutional neural networks (CNNs) 7 .  ... 
doi:10.1038/s41598-019-55431-0 pmid:31857624 pmcid:PMC6923391 fatcat:to7xn56hi5gwli43h25ggjpaiq

Texture Segmentation Based Video Compression Using Convolutional Neural Networks

Chichen Fu, Di Chen, Edward Delp, Zoe Liu, Fengqing Zhu
2018 IS&T International Symposium on Electronic Imaging Science and Technology  
The proposed method uses convolutional neural networks to extract texture regions in a frame, which are then reconstructed using a global motion model.  ...  There has been a growing interest in using different approaches to improve the coding efficiency of modern video codec in recent years as demand for web-based video consumption increases.  ...  In this paper, we propose a block-based texture segmentation method to extract texture region in a video frame using convolutional neural networks.  ... 
doi:10.2352/issn.2470-1173.2018.2.vipc-155 fatcat:4wo6mehm5fblbegff2xsvqvgxi

Tubule Segmentation of Fluorescence Microscopy Images Based on Convolutional Neural Networks With Inhomogeneity Correction

Soonam Lee, Chichen Fu, Paul Salama, Kenneth W. Dunn, Edward J. Delp
2018 IS&T International Symposium on Electronic Imaging Science and Technology  
This paper describes a segmentation method for tubular structures in fluorescence microscopy images using convolutional neural networks with data augmentation and inhomogeneity correction.  ...  The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods.  ...  Convolutional neural network (CNN) has been used to address segmentation problems in biomedical imaging [22] .  ... 
doi:10.2352/issn.2470-1173.2018.15.coimg-199 fatcat:sbxnh2er3zahbm2zv7zx3yjz6a

Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique

Ahmad Zoebad Foeady, Siti Ria Riqmawatin, Dian Candra Rini Novitasari
2021 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
This image feature extracted using gray level co-occurrence matrix (GLCM) and classified using 2 method of neural network which is feed forward neural network (FFNN) dan feed backward neural network (FBNN  ...  This research aims to obtain the best neural network model to classify lung cancer a.  ...  based on CT scan image by applying FCM segmentation and neural network technique Figure 4 . 4 (a) Grayscale, (b) Median Filter, (c) Histogram Equalization, (d) FCM Segmentation Figure 5 .Figure 6  ... 
doi:10.12928/telkomnika.v19i4.18874 fatcat:qkcbbydcprcunjx2nhz2gido5y

Microscopy images segmentation algorithm based on shearlet neural network

Nemir Ahmed Al-Azzawi
2021 Bulletin of Electrical Engineering and Informatics  
A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced.  ...  The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data.  ...  In this paper, a robust segmentation of microscopy images based on the shearlet neural network is developed.  ... 
doi:10.11591/eei.v10i2.2743 fatcat:xbvxd5l76jezpat75kbwjhocsm

Applications of deep learning in electron microscopy

Kevin P Treder, Chen Huang, Judy S Kim, Angus I Kirkland
2022 Microscopy  
We review the growing use of machine learning in electron microscopy (EM) driven in part by the availability of fast detectors operating at kiloHertz frame rates leading to large data sets that cannot  ...  We summarize the various network architectures and error metrics that have been applied to a range of EM-related problems including denoising and inpainting.  ...  Applications in physical sciences Atomic-resolution scanning transmission electron microscopy Early examples of the use of deep neural networks for semantic segmentation of EM data are based on atomic-resolution  ... 
doi:10.1093/jmicro/dfab043 pmid:35275181 fatcat:3rncxrklt5atratggpunhdxsam

Towards Automated Electron Microscopy Image Segmentation for Nanoparticles of Complex Shape by Convolutional Neural Networks

Bastian Rühle, Vasile-Dan Hodoroaba
2020 Microscopy and Microanalysis  
automated particle segmentation with convolutional neural networks (CNN).  ...  Neural networks, especially Convolutional Neural Networks (CNNs) have shown enormous potential in image classification and segmentation tasks, and their scope has been extended towards automated image  ...  automated particle segmentation with convolutional neural networks (CNN).  ... 
doi:10.1017/s1431927620017262 fatcat:jib7yfn5rnd4jks5bx73hjahwe

Simulation and Optimization of Artificial Neural Network Based Air Quality Estimator

2019 International journal of recent technology and engineering  
The available data is broken into the number of segments .The length of data segment and the neurons in hidden layer is varied in number to find the optimized model of artificial neural network model using  ...  The mean squared error and regression are the artificial neural network model performance parameter  ...  The Number of neurons and data segment length is varied to optimize the artificial neural network Model.  ... 
doi:10.35940/ijrte.c4985.098319 fatcat:e44kbaaauzappg7gdbfsfozzya

Convolutional Neural Network Pruning to Accelerate Membrane Segmentation in Electron Microscopy [article]

Joris Roels, Jonas De Vylder, Jan Aelterman, Yvan Saeys, Wilfried Philips
2018 arXiv   pre-print
Recent advances in electron microscopy membrane segmentation are able to cope with such difficulties by training convolutional neural networks.  ...  This way, we manage to obtain real-time membrane segmentation performance, for our specific electron microscopy setup.  ...  We propose a membrane extraction method based on convolutional neural networks that can help segmenting complete mitochondria and ER in Sec. 2.  ... 
arXiv:1810.09735v1 fatcat:i3an746eg5fb5cqkicd46wairi
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