3,974 research outputs found
Efficient Image Processing Via Compressive Sensing Of Integrate-And-Fire Neuronal Network Dynamics
Integrate-and-fire (I&F) neuronal networks are ubiquitous in diverse image processing applications, including image segmentation and visual perception. While conventional I&F network image processing requires the number of nodes composing the network to be equal to the number of image pixels driving the network, we determine whether I&F dynamics can accurately transmit image information when there are significantly fewer nodes than network input-signal components. Although compressive sensing (CS) theory facilitates the recovery of images using very few samples through linear signal processing, it does not address whether similar signal recovery techniques facilitate reconstructions through measurement of the nonlinear dynamics of an I&F network. In this paper, we present a new framework for recovering sparse inputs of nonlinear neuronal networks via compressive sensing. By recovering both one-dimensional inputs and two-dimensional images, resembling natural stimuli, we demonstrate that input information can be well-preserved through nonlinear I&F network dynamics even when the number of network-output measurements is significantly smaller than the number of input-signal components. This work suggests an important extension of CS theory potentially useful in improving the processing of medical or natural images through I&F network dynamics and understanding the transmission of stimulus information across the visual system
Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemes
Image analysis using more than one modality (i.e. multi-modal) has been
increasingly applied in the field of biomedical imaging. One of the challenges
in performing the multimodal analysis is that there exist multiple schemes for
fusing the information from different modalities, where such schemes are
application-dependent and lack a unified framework to guide their designs. In
this work we firstly propose a conceptual architecture for the image fusion
schemes in supervised biomedical image analysis: fusing at the feature level,
fusing at the classifier level, and fusing at the decision-making level.
Further, motivated by the recent success in applying deep learning for natural
image analysis, we implement the three image fusion schemes above based on the
Convolutional Neural Network (CNN) with varied structures, and combined into a
single framework. The proposed image segmentation framework is capable of
analyzing the multi-modality images using different fusing schemes
simultaneously. The framework is applied to detect the presence of soft tissue
sarcoma from the combination of Magnetic Resonance Imaging (MRI), Computed
Tomography (CT) and Positron Emission Tomography (PET) images. It is found from
the results that while all the fusion schemes outperform the single-modality
schemes, fusing at the feature level can generally achieve the best performance
in terms of both accuracy and computational cost, but also suffers from the
decreased robustness in the presence of large errors in any image modalities.Comment: Zhe Guo and Xiang Li contribute equally to this wor
Structural Similarity based Anatomical and Functional Brain Imaging Fusion
Multimodal medical image fusion helps in combining contrasting features from
two or more input imaging modalities to represent fused information in a single
image. One of the pivotal clinical applications of medical image fusion is the
merging of anatomical and functional modalities for fast diagnosis of malignant
tissues. In this paper, we present a novel end-to-end unsupervised
learning-based Convolutional Neural Network (CNN) for fusing the high and low
frequency components of MRI-PET grayscale image pairs, publicly available at
ADNI, by exploiting Structural Similarity Index (SSIM) as the loss function
during training. We then apply color coding for the visualization of the fused
image by quantifying the contribution of each input image in terms of the
partial derivatives of the fused image. We find that our fusion and
visualization approach results in better visual perception of the fused image,
while also comparing favorably to previous methods when applying various
quantitative assessment metrics.Comment: Accepted at MICCAI-MBIA 201
Image retrieval based on colour and improved NMI texture features
This paper proposes an improved method for extracting NMI features. This method uses Particle Swarm Optimization in advance to optimize the two-dimensional maximum class-to-class variance (2OTSU) in advance. Afterwards, the optimized 2OUSU is introduced into the Pulse Coupled Neural Network (PCNN) to automatically obtain the number of iterations of the loop. We use an improved PCNN method to extract the NMI features of the image. For the problem of low accuracy of single feature, this paper proposes a new method of multi-feature fusion based on image retrieval. It uses HSV colour features and texture features, where, the texture feature extraction methods include: Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Improved PCNN. The experimental results show that: on the Corel-1k dataset, compared with similar algorithms, the retrieval accuracy of this method is improved by 13.6%; On the AT&T dataset, the retrieval accuracy is improved by 13.4% compared with the similar algorithm; on the FD-XJ dataset, the retrieval accuracy is improved by 17.7% compared with the similar algorithm. Therefore, the proposed algorithm has better retrieval performance and robustness compared with the existing image retrieval algorithms based on multi-feature fusion
Infrared and Visible Image Fusion using a Deep Learning Framework
In recent years, deep learning has become a very active research tool which
is used in many image processing fields. In this paper, we propose an effective
image fusion method using a deep learning framework to generate a single image
which contains all the features from infrared and visible images. First, the
source images are decomposed into base parts and detail content. Then the base
parts are fused by weighted-averaging. For the detail content, we use a deep
learning network to extract multi-layer features. Using these features, we use
l_1-norm and weighted-average strategy to generate several candidates of the
fused detail content. Once we get these candidates, the max selection strategy
is used to get final fused detail content. Finally, the fused image will be
reconstructed by combining the fused base part and detail content. The
experimental results demonstrate that our proposed method achieves
state-of-the-art performance in both objective assessment and visual quality.
The Code of our fusion method is available at
https://github.com/hli1221/imagefusion_deeplearningComment: 6 pages, 6 figures, 2 tables, ICPR 2018(accepted
- …