中国科学院机构知识库网格系统: 检索

Deep learning methods for medical image fusion: A review 期刊论文 OAI收割
COMPUTERS IN BIOLOGY AND MEDICINE, 2023, 卷号: 160
作者: Zhou, Tao; Cheng, QianRu; Lu, HuiLing; Li, Qi; Zhang, XiangXiang

|

收藏 | 浏览/下载：15/0 | 提交时间：2023/06/12

Deep learning Medical image fusion Convolutional neural network Generative adversarial network Encoder -decoder network

A shape-guided deep residual network for automated CT lung segmentation 期刊论文 OAI收割
KNOWLEDGE-BASED SYSTEMS, 2022, 卷号: 250, 页码: 10
作者: Yang, Lei; Gu, Yuge; Huo, Benyan; Liu, Yanhong; Bian, Guibin

|

收藏 | 浏览/下载：43/0 | 提交时间：2022/09/19

Deep network architecture Medical image analysis Shape stream network Residual unit Attention fusion unit

Medical lesion segmentation by combining multimodal images with modality weighted UNet 期刊论文 OAI收割
MEDICAL PHYSICS, 2022
作者: Zhu, Xiner; Wu, Yichao; Hu, Haoji; Zhuang, Xianwei; Yao, Jincao

|

收藏 | 浏览/下载：69/0 | 提交时间：2022/05/16

attention deep neural networks medical image segmentation multimodality fusion

Medical image fusion via discrete stationary wavelet transform and an enhanced radial basis function neural network 期刊论文 OAI收割
Applied Soft Computing, 2022, 卷号: 118, 页码: 1-13
作者: Chao, Zhen; Duan, Xingguang; Jia, Shuangfu; Guo, Xuejun; Liu H(刘浩)

|

收藏 | 浏览/下载：67/0 | 提交时间：2022/03/07

Discrete stationary wavelet transform Enhanced radial basis function neural network Medical image fusion

Richer fusion network for breast cancer classification based on multimodal data 期刊论文 OAI收割
BMC Medical Informatics and Decision Making, 2021, 卷号: 21, 期号: Suppl 1
作者: Yan,Rui; Zhang,Fa; Rao,Xiaosong; Lv,Zhilong; Li,Jintao

|

收藏 | 浏览/下载：38/0 | 提交时间：2021/12/01

Pathological image Electronic medical record Multimodal fusion Breast cancer classification Convolutional neural network

Multimodal Glioma Image Segmentation Using Dual Encoder Structure and Channel Spatial Attention Block 期刊论文 OAI收割
FRONTIERS IN NEUROSCIENCE, 2020, 卷号: 14
作者: Su, Run; Liu, Jinhuai; Zhang, Deyun; Cheng, Chuandong; Ye, Mingquan

|

收藏 | 浏览/下载：83/0 | 提交时间：2020/12/28

medical image fusion glioma segmentation fully convolutional neural networks DES CSAB F-S-Net

Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering 期刊论文 OAI收割
SIGNAL PROCESSING-IMAGE COMMUNICATION, 2020, 卷号: 83, 页码: 10
作者: Hu, Qiu; Hu, Shaohai; Zhang, Fengzhen

|

收藏 | 浏览/下载：21/0 | 提交时间：2021/12/06

Image fusion Multi-modality medical image Sparse representation Gabor filter Non-subsampled contourlet transform

Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering 期刊论文 OAI收割
SIGNAL PROCESSING-IMAGE COMMUNICATION, 2020, 卷号: 83, 页码: 10
作者: Hu, Qiu; Hu, Shaohai; Zhang, Fengzhen

|

收藏 | 浏览/下载：16/0 | 提交时间：2021/12/06

Image fusion Multi-modality medical image Sparse representation Gabor filter Non-subsampled contourlet transform

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.; Wang M.-J.; Han G.-L.

收藏 | 浏览/下载：72/0 | 提交时间：2013/03/25

Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.

Multimodal medical image fusion using fuzzy radial basis function neural networks 会议论文 OAI收割
Beijing, China
作者: IEEE; Li, Sha; Li, Qiang; Dang, Jian-Wu; Wang, Yang-Ping

|

收藏 | 浏览/下载：21/0 | 提交时间：2018/08/20

multimodal medical image fusion fuzzy inference radial basis function neural networks genetic algorithm blurry image