Adaptive Pulse Coupled Neural Network Research Articles

A Novel Fusion Framework Based on Adaptive PCNN in NSCT Domain for Whole-Body PET and CT Images.

The PET and CT fusion images, combining the anatomical and functional information, have important clinical meaning. This paper proposes a novel fusion framework based on adaptive pulse-coupled neural networks (PCNNs) in nonsubsampled contourlet transform (NSCT) domain for fusing whole-body PET and CT images. Firstly, the gradient average of each pixel is chosen as the linking strength of PCNN model to implement self-adaptability. Secondly, to improve the fusion performance, the novel sum-modified Laplacian (NSML) and energy of edge (EOE) are extracted as the external inputs of the PCNN models for low- and high-pass subbands, respectively. Lastly, the rule of max region energy is adopted as the fusion rule and different energy templates are employed in the low- and high-pass subbands. The experimental results on whole-body PET and CT data (239 slices contained by each modality) show that the proposed framework outperforms the other six methods in terms of the seven commonly used fusion performance metrics.

Technique for multi-focus image fusion based on fuzzy-adaptive pulse-coupled neural network

Multi-focus image fusion technique can solve the problem that not all the targets in an image are clear in case of imaging in the same scene. In this paper, a novel multi-focus image fusion technique is presented, which is developed by using the nonsubsampled contourlet transform (NSCT) and a proposed fuzzy logic based adaptive pulse-coupled neural network (PCNN) model. In our method, sum-modified Laplacian (SML) is calculated as the motivation for PCNN neurons in NSCT domain. Since the linking strength plays an important role in PCNN, we propose an adaptively fuzzy way to determine it by computing each coefficient’s importance relative to the surrounding coefficients. Combined with human visual perception characteristics, the fuzzy membership value is employed to automatically achieve the degree of importance of each coefficient, which is utilized as the linking strength in PCNN model. Experimental results on simulated and real multi-focus images show that the proposed technique has a superior performance to series of exist fusion methods.

Multimodal medical image fusion using PCNN optimized by the QPSO algorithm

This paper proposed a method to fuse multimodal medical images using the adaptive pulse-coupled neural networks (PCNN), which was optimized by the quantum-behaved particle swarm optimization (QPSO) algorithm. In this fusion model, two source images, A and B, were first processed by the QPSO-PCNN model, respectively. Through the QPSO algorithm, the PCNN model could find the optimal parameters for the source images, A and B. To improve the efficiency and quality of QPSO, three evaluation criteria, image entropy (EN), average gradient (AG) and spatial frequency (SF) were selected as the hybrid fitness function. Then, the output of the fusion model was obtained by the judgment factor according to the firing maps of two source images, which maybe was the pixel value of the image A, or that of the image B, or the tradeoff value of them. Based on the output of the fusion model, the fused image was gained. Finally, we used five pairs of multimodal medical images as experimental data to test and verify the proposed method. Furthermore, the mutual information (MI), structural similarity (SSIM), image entropy (EN), etc. were used to judge the performances of different methods. The experimental results illustrated that the proposed method exhibited better performances.

Multifocus Color Image Fusion Based on NSST and PCNN

This paper proposed an effective multifocus color image fusion algorithm based on nonsubsampled shearlet transform (NSST) and pulse coupled neural networks (PCNN); the algorithm can be used in different color spaces. In this paper, we take HSV color space as an example, H component is clustered by adaptive simplified PCNN (S-PCNN), and then the H component is fused according to oscillation frequency graph (OFG) of S-PCNN; at the same time, S and V components are decomposed by NSST, and different fusion rules are utilized to fuse the obtained results. Finally, inverse HSV transform is performed to get the RGB color image. The experimental results indicate that the proposed color image fusion algorithm is more efficient than other common color image fusion algorithms.

Feature-Motivated Simplified Adaptive PCNN-Based Medical Image Fusion Algorithm in NSST Domain.

Multimodality medical image fusion plays a vital role in diagnosis, treatment planning, and follow-up studies of various diseases. It provides a composite image containing critical information of source images required for better localization and definition of different organs and lesions. In the state-of-the-art image fusion methods based on nonsubsampled shearlet transform (NSST) and pulse-coupled neural network (PCNN), authors have used normalized coefficient value to motivate the PCNN-processing both low-frequency (LF) and high-frequency (HF) sub-bands. This makes the fused image blurred and decreases its contrast. The main objective of this work is to design an image fusion method that gives the fused image with better contrast, more detail information, and suitable for clinical use. We propose a novel image fusion method utilizing feature-motivated adaptive PCNN in NSST domain for fusion of anatomical images. The basic PCNN model is simplified, and adaptive-linking strength is used. Different features are used to motivate the PCNN-processing LF and HF sub-bands. The proposed method is extended for fusion of functional image with an anatomical image in improved nonlinear intensity hue and saturation (INIHS) color model. Extensive fusion experiments have been performed on CT-MRI and SPECT-MRI datasets. Visual and quantitative analysis of experimental results proved that the proposed method provides satisfactory fusion outcome compared to other image fusion methods.

基于自适应脉冲耦合神经网络的水下激光图像分割方法

基于自适应脉冲耦合神经网络的水下激光图像分割方法

Image fusion algorithm based on redundant-lifting NSWMDA and adaptive PCNN

Pyramid decomposition in the NSCT transformation is a band-pass filtering process in the frequency domain where different scales of images are orthogonal. However, from the perspective of the image content, correlation is likely to exist between the fused images, and this kind of decomposition makes images of different scales contain redundant information, as a result of which the fused image may not capture the subtle information from the original images. In order to overcome the above-mentioned problem, an effective image fusion method based on redundant-lifting non-separable wavelet multi-directional analysis (NSWMDA) and adaptive pulse coupled neural network (PCNN) has been proposed. The original images are firstly decomposed by using the NSWMDA into several sub-bands in order to retain texture detail and contrast information of the images, and then adaptive PCNN algorithm is applied on the high-frequency directional sub-bands to extract the high-frequency information. The low-frequency sub-bands are evaluated by weighted average based on Gaussian kernel with a chosen maximum fusion rule. Results from experiments show that the proposed method can make the fused image maintains more texture details and contrast information.

Adaptive Pulse Coupled Neural Network Parameters for Image Segmentation

Adaptive Pulse Coupled Neural Network Parameters for Image Segmentation

Image fusion based on wavelet transform and adaptive PCNN

Concerning the fusion problem of multi-focus image with the same scene,an algorithm of image fusion based on wavelet transform and adaptive Pulse Coupled Neural Network(PCNN) was proposed.Firstly,original images were decomposed by wavelet transform,and the sub-band images at different scales were obtained.Secondly,a fusion rule was given through making use of synchronous pulse bursts.This method used Energy of Laplacian(EOL) of wavelet coefficients at different scales as the linking strength of the corresponding neuron.After the processing of PCNN with the adaptive strength,new fire mapping images in wavelet domain were obtained.According to the fire mapping images,the fusion coefficients were decided by the compare-select operator,and then the region consistency test was used on the fusion coefficients to obtain the final fusion coefficients.Finally,fusion images were obtained by wavelet inverse transform.The experimental results illustrate that this algorithm is efficient to extract feature information from the original images and improves fusion images.It outperforms the conventional methods in subjective vision effect and objective performance index.

Noise and Speckle Reduction in Doppler Blood Flow Spectrograms Using an Adaptive Pulse-Coupled Neural Network

A novel method, called adaptive pulse coupled neural network (AD-PCNN) using a two-stage denoising strategy, is proposed to reduce noise and speckle in the spectrograms of Doppler blood flow signals. AD-PCNN contains an adaptive thresholding PCNN and a threshold decaying PCNN. Firstly, PCNN pulses based on the adaptive threshold filter a part of background noise in the spectrogram while isolating the remained noise and speckles. Subsequently, the speckles and noise of the denoised spectrogram are detected by the pulses generated through the threshold decaying PCNN and then are iteratively removed by the intensity variation to speckle or noise neurons. The relative root mean square (RRMS) error of the maximum frequency extracted from the AD-PCNN spectrogram of the simulated Doppler blood flow signals is decreased 25.2% on average compared to that extracted from the MPWD (matching pursuit with Wigner Distribution) spectrogram, and the RRMS error of the AD-PCNN spectrogram is decreased 10.8% on average compared to MPWD spectrogram. Experimental results of synthetic and clinical signals show that the proposed method is better than the MPWD in improving the accuracy of the spectrograms and their maximum frequency curves.

A Novel Image Fusion Algorithm Based on Local Contrast and Adaptive PCNN

This paper proposes a new fusion algorithm based on the improved pulse coupled neural network(PCNN)model,the fundamental characteristics of images and the properties of human vision system.Compared with the traditional algorithm where the linking strength of each neuron has the same value and its value is chosen through experimentation,this algorithm uses the local contrast of each pixel as its value,so that the linking strength of each pixel can be chosen adaptively.After the processing of PCNN with the adaptive linking strength,new fire mapping images are obtained for each image taking part in the fusion.The clear objects of each original image are decided by the compare-selection operator with the fire mapping images pixel by pixel and then all of them are merged into a new clear image.Furthermore,by this algorithm,other parameters,for example,Δ,the threshold adjusting constant,only have a slight effect on the new fused image.It therefore overcomes the difficulty in adjusting parameters in PCNN.Experimental results indicate that the method outperforms the traditional approaches in preserving edge information while improving texture information.