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Efficient Multi-Kernel DCNN with Pixel Dropout for Stroke MRI Segmentation
In this study, we propose a deep convolution neural network for stroke magnetic resonance imaging(MRI) segmentation. The main structure of our network consists of two symmetrical deep sub-networks, in which dense blocks are embedded for extracting effective features from sparse pixels to alleviate the over-fitting problem of deep networks. We use the multi-kernel to divide the network into two sub-networks for acquiring more receptive fields, and the dropout regularization method to achieve an effective feature mapping. For the post-processing of the soft segmentation, we use image median filtering to alleviate noises and ...
Source: Neurocomputing - April 19, 2019 Category: Neuroscience Source Type: research

An active learning approach for stroke lesion segmentation on multimodal MRI data
We report encouraging results over a dataset combining functional, anatomical and diffusion data.
Source: Neurocomputing - November 21, 2014 Category: Neuroscience Source Type: research

Whole-brain functional connectome-based multivariate classification of post-stroke aphasia
Publication date: 20 December 2017 Source:Neurocomputing, Volume 269 Author(s): Mi Yang, Jiao Li, Zhiqiang Li, Dezhong Yao, Wei Liao, Huafu Chen Patients with post-stroke aphasia (PSA) show abnormalities of intrinsic functional connectivity. However, whether the whole-brain functional connectome can be used as a feature to distinguish patients with PSA from healthy controls is poorly understood. We aim to distinguish PSA patients from controls using whole-brain functional connectivity-based multivariate pattern analysis. These features would be helpful in the understanding of the pathophysiology of PSA. In the present stu...
Source: Neurocomputing - September 10, 2017 Category: Neuroscience Source Type: research

Deep Convolutional Neural Network for Accurate Segmentation and Quantification of White Matter Hyperintensities
Publication date: Available online 19 December 2019Source: NeurocomputingAuthor(s): Liangliang Liu, Shaowu Chen, Xiaofeng Zhu, Xing-Ming Zhao, Fang-Xiang Wu, Jianxin WangAbstractWhite matter hyperintensities (WMHs) appear as regions of abnormally signal intensity on magnetic resonance imaging (MRI) images, that can be identified in MRI images of elderly people and ischemic stroke patients. However, manual segmentation and quantification of images with WMHs is laborious and time-consuming. Moreover, ischemic stroke lesion and WMHs appear as similar signals in MRI images, making it difficult to accurately segment the WMHs. A...
Source: Neurocomputing - December 19, 2019 Category: Neuroscience Source Type: research

Features and models for human activity recognition
In this study, an Information Correlation Coefficient (ICC) analysis was carried out followed by a wrapper Feature Selection (FS) method on the reduced input space. Additionally, a novel HAR method is proposed for this specific problem of stroke early diagnosing, comprising an adaptation of the well-known Genetic Fuzzy Finite State Machine (GFFSM) method. To the best of the author׳s knowledge, this is the very first analysis of the feature space concerning all the previously published feature transformations on raw acceleration data. The main contributions of this study are the optimization of the sample rate, selection o...
Source: Neurocomputing - July 10, 2015 Category: Neuroscience Source Type: research

TENDER: Tensor non-local deconvolution enabled radiation reduction in CT perfusion
Publication date: 15 March 2017 Source:Neurocomputing, Volume 229 Author(s): Ruogu Fang, Ajay Gupta, Junzhou Huang, Pina Sanelli Stroke is the leading cause of long-term disability and the second leading cause of mortality in the world, and exerts an enormous burden on the public health. Computed Tomography (CT) remains one of the most widely used imaging modality for acute stroke diagnosis. However when coupled with CT perfusion, the excessive radiation exposure in repetitive imaging to assess treatment response and prognosis has raised significant public concerns regarding its potential hazards to both short- and long-t...
Source: Neurocomputing - January 24, 2017 Category: Neuroscience Source Type: research

Text detection in natural scene images based on color prior guided MSER
Publication date: 13 September 2018 Source:Neurocomputing, Volume 307 Author(s): Xiangnan Zhang, Xinbo Gao, Chunna Tian In this paper, we focus on text detection in natural scene images which is conducive to content-based wild image analysis and understanding. This task is still an open problem and usually includes two key issues: text candidate extraction and verification. For text candidate extraction, we introduce a color prior to guide the character candidate extraction by Maximally Stable Extremal Region (MSER). The principle of color prior acquirement is to obtain stroke-like textures with modified Stroke Width Tran...
Source: Neurocomputing - June 12, 2018 Category: Neuroscience Source Type: research

Neural Network Based Modeling and Control of Elbow Joint Motion Under Functional Electrical Stimulation
Publication date: Available online 6 March 2019Source: NeurocomputingAuthor(s): Yurong Li, Wenxin Chen, Jun Chen, Xin Chen, Jie Liang, Min DuAbstractIn patients with stroke and spinal cord injury, motor function is reduced or even lost because motor nerve signals cannot be transmitted due to nerve injury. Functional electrical stimulation (FES) is one of the most important rehabilitation techniques for the treatment of motor impairment in patients with stroke and spinal cord injury, which has been widely used in the recovery and reconstruction of limb motor function. In this paper, we propose a neural network based modelin...
Source: Neurocomputing - March 6, 2019 Category: Neuroscience Source Type: research

Integration of an Actor-Critic Model and Generative Adversarial Networks for a Chinese Calligraphy Robot
Publication date: Available online 16 January 2020Source: NeurocomputingAuthor(s): Ruiqi Wu, Changle Zhou, Fei Chao, Longzhi Yang, Chih-Min Lin, Changjing ShangAbstractAs a combination of robotic motion planning and Chinese calligraphy culture, robotic calligraphy plays a significant role in the inheritance and education of Chinese calligraphy culture. Most existing calligraphy robots focus on enabling the robots to learn writing through human participation, such as human-robot interactions and manually designed evaluation functions. However, because of the subjectivity of art aesthetics, these existing methods require a l...
Source: Neurocomputing - January 16, 2020 Category: Neuroscience Source Type: research

A machine learning approach to measure and monitor physical activity in children
Publication date: 8 March 2017 Source:Neurocomputing, Volume 228 Author(s): Paul Fergus, Abir J. Hussain, John Hearty, Stuart Fairclough, Lynne Boddy, Kelly Mackintosh, Gareth Stratton, Nicky Ridgers, Dhiya Al-Jumeily, Ahmed J. Aljaaf, Jenet Lunn The growing trend of obesity and overweight worldwide has reached epidemic proportions with one third of the global population now considered obese. This is having a significant medical impact on children and adults who are at risk of developing osteoarthritis, coronary heart disease and stroke, type 2 diabetes, cancers, respiratory problems, and non-alcoholic fatty liver disease...
Source: Neurocomputing - January 16, 2017 Category: Neuroscience Source Type: research

Script independent approach for multi-oriented text detection in scene image
Publication date: 14 June 2017 Source:Neurocomputing, Volume 242 Author(s): Sounak Dey, Palaiahnakote Shivakumara, K.S. Raghunandan, Umapada Pal, Tong Lu, G. Hemantha Kumar, Chee Seng Chan Developing a text detection method which is invariant to scripts in natural scene images is a challenging task due to different geometrical structures of various scripts. Besides, multi-oriented of text lines in natural scene images make the problem more challenging. This paper proposes to explore ring radius transform (RRT) for text detection in multi-oriented and multi-script environments. The method finds component regions based on c...
Source: Neurocomputing - March 30, 2017 Category: Neuroscience Source Type: research

An analysis of Convolutional Long Short-Term Memory Recurrent Neural Networks for gesture recognition
Publication date: 13 December 2017 Source:Neurocomputing, Volume 268 Author(s): Eleni Tsironi, Pablo Barros, Cornelius Weber, Stefan Wermter In this research, we analyze a Convolutional Long Short-Term Memory Recurrent Neural Network (CNNLSTM) in the context of gesture recognition. CNNLSTMs are able to successfully learn gestures of varying duration and complexity. For this reason, we analyze the architecture by presenting a qualitative evaluation of the model, based on the visualization of the internal representations of the convolutional layers and on the examination of the temporal classification outputs at a frame lev...
Source: Neurocomputing - September 1, 2017 Category: Neuroscience Source Type: research

CRF based text detection for natural scene images using convolutional neural network and context information
Publication date: 21 June 2018 Source:Neurocomputing, Volume 295 Author(s): Yanna Wang, Cunzhao Shi, Baihua Xiao, Chunheng Wang, Chengzuo Qi This paper presents a novel scene text detection method based on conditional random field (CRF) framework. We estimate the confidence of Maximally Stable Extremal Region (MSER) being text by leveraging convolutional neural network (CNN) to define the unary cost item. In addition, we establish the neighboring interactions for MSERs using four different features including color, shape, stroke and spatial features to define the pairwise cost item. Considering the special layout of texts...
Source: Neurocomputing - April 17, 2018 Category: Neuroscience Source Type: research

Elite Loss for Scene Text Detection
Publication date: Available online 29 December 2018Source: NeurocomputingAuthor(s): Xu Zhao, Chaoyang Zhao, Haiyun Guo, Yousong Zhu, Ming Tang, Jinqiao WangAbstractMany scene text detection approaches generate foreground segmentation maps to detect the text instances. In these methods, usually all the pixels within the bounding box regions of the text are equally treated as foreground during the training process. However, different from the general object segmentation problem, we argue that not all the pixels across the text bounding box region contribute equally for locating the text instance. Specifically, some in-box no...
Source: Neurocomputing - December 29, 2018 Category: Neuroscience Source Type: research

Automatic Segmentation of Left Ventricle from Cardiac MRI via Deep Learning and Region Constrained Dynamic Programming
Publication date: Available online 16 February 2019Source: NeurocomputingAuthor(s): Hu Huaifei, Ning Pan, Jiayu Wang, Tailang Yin, Renzhen YeAbstractSegmentation of the left ventricle from cardiac magnetic resonance images (MRI) is an essential step to quantitatively analyze global and regional cardiac function. The aim of this study is to develop a novel and robust algorithm which can improve the accuracy of automatic left ventricle segmentation on short-axis cardiac MRI. The database used in this study are 900 cardiac MRI cases from Hubei Cancer Hospital. Three key techniques are developed in this segmentation algorithm:...
Source: Neurocomputing - February 16, 2019 Category: Neuroscience Source Type: research

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