Unit-linking Pulse Coupled Neural Network Research Articles

Parameter adaptive unit-linking pulse coupled neural network based MRI–PET/SPECT image fusion

Medical image fusion has many applications to healthcare that is accomplished by extracting and then combining the complementary information from multiple medical images into a single image. The pulse coupled neural network (PCNN) is greatly applied to image fusion due to its efficient coupling of surrounding neurons, but suffers from network complexity and manual parameter settings. This paper proposes a novel parameter adaptive unit-linking PCNN (PAULPCNN) model, whose parameters are automatically obtained from the external stimulus and exhibits a simpler structure than the original PCNN. The multi-scale decomposition-based methods, particularly based on the non-subsampled shearlet transform (NSST), are widely employed by researchers to perform medical image fusion due to their efficient separation of spatial details at different scales. Motivated by the advantages of the PCNN and multi-scale decomposition-based methods, a hybrid medical image fusion method based on the PAULPCNN model is introduced in the NSST domain that merges the salient complementary details from a gray-scale and the corresponding pseudo-color medical image captured at different modalities to produce a more informative image that is more useful to medical experts in computer-aided diagnosis of diseases. The proposed method first employs NSST to transform the source images into a low-pass and several high-pass sub-bands, respectively. The high-pass sub-bands are combined using the firing times of the proposed PAULPCNN model, whereas a new distance-weighted regional energy-based rule is applied to construct the fused low-pass sub-band, which while estimating regional energy, assigns weight to different neighboring pixels depending on their distance from the central pixel. Finally, inverse NSST is applied on the fused sub-bands to construct the fused image. The effectiveness of the proposed technique is shown using the fusion results of eleven state-of-the-art methods on thirty gray-scale and pseudo-color brain medical image pairs comprising mild Alzheimer’s disease, Huntington’s disease, motor neuron disease, sagittal plane, coronal plane, transaxial plane, and glioma image pairs, where eight objective metrics are considered for the quantitative assessment. Experimental results demonstrate that the proposed method is competitive with the state-of-the-art methods, which even outperforms some of these methods by providing fused images with better visual quality and greater objective performance, which are more suitable for specialists to diagnose brain diseases.

Parameter adaptive unit-linking dual-channel PCNN based infrared and visible image fusion

The properties like pulse synchronization of neurons and global coupling greatly motivated researchers to apply pulse coupled neural network (PCNN) models in accomplishing image fusion. However, manual adjustment of its parameters negatively affects the fusion performance. Moreover, it can process one image at a time. A new parameter adaptive unit-linking dual-channel PCNN model that exhibits all the properties of PCNN and processes two images simultaneously is used in this work to implement a novel fusion algorithm in the non-subsampled contourlet transform (NSCT) domain for the integration of infrared and visible images. At the same time, all the parameters of the proposed model are automatically estimated from the source images. The infrared and visible images are first decomposed using NSCT to provide a sequence of band-pass directional sub-bands and a low-pass sub-band, respectively. The band-pass directional sub-bands are fused using fractal dimension-based linking strength, while the low-pass sub-bands are combined using a new linking strength based on the multi-scale morphological gradient of coefficients. Lastly, the fused image is constructed from the fused sub-bands by applying inverse NSCT. Fourteen state-of-the-art methods are adopted for comparing the performance of the proposed method. The qualitative comparison is done using the human visual system, whereas six objective metrics are considered for the quantitative evaluation. The proposed method is competitive and outperforms some of the existing methods, according to the results of the experiments.

A Computer Aided Breast Cancer Detection Using Unit-Linking Pulse Coupled Neural Network & Multiphase Level Set Method

Breast cancer is one of the lethal diseases with high mortality rates among women. An early detection and diagnosis of the disease can help increase the survival rate. Distinguishing a normal breast tissue from a cancerous one proves to be ambiguous for a Radiologist. A computer aided system can help a radiologist in better and efficient diagnosis. This paper aims at detection and classification of benign and malignant mammogram images with Unit-linking Pulse Coupled Neural Network combined with Multiphase level set Method. While Unit linking Pulse Coupled Neural Network (PCNN) helps in coarse feature extraction, Multi phase Level Set method helps in extracting minute details and hence, better classification. The proposed method is tested with images from MIAS open-source database. Performance of the proposed method is measured using sensitivity, accuracy, specificity and false positive rate. Experiments show that the proposed method gives satisfactory results when compared to the state-of-art methods. The sensitivity obtained by the proposed method is 95.16%, an accuracy of 96.76%, the False Positive Rate (FPR) is as less as 0.85% and specificity of 97.12%.

基于改进型UL-PCNN的绝缘子图像分割

基于改进型UL-PCNN的绝缘子图像分割

A fusion algorithm for infrared and visible images based on adaptive dual-channel unit-linking PCNN in NSCT domain

In this paper, a novel fusion algorithm based on the adaptive dual-channel unit-linking pulse coupled neural network (PCNN) for infrared and visible images fusion in nonsubsampled contourlet transform (NSCT) domain is proposed. The flexible multi-resolution and directional expansion for images of NSCT are associated with global coupling and pulse synchronization characteristic of dual-PCNN. Compared with other dual-PCNN models, the proposed model possesses fewer parameters and is not difficult to implement adaptive, which is more suitable for image fusion. Firstly, the source images were multi-scale and multi-directional decomposed by NSCT. Then, to make dual-channel PCNN adaptive, the average gradient of each pixel was presented as the linking strength, and the time matrix was presented to determine the iteration number adaptively. In this fusion scheme, a novel sum modified-Laplacian of low-frequency subband and a modified spatial frequency of high-frequency subband were input to motivate the adaptive dual-channel unit-linking PCNN, respectively. Experimental results demonstrate that the proposed algorithm can significantly improve image fusion performance, accomplish notable target information and high contrast, simultaneously preserve rich details information, and excel other typical current methods in both objective evaluation criteria and visual effect.

Attention selection using global topological properties based on pulse coupled neural network

Topological properties are with invariance and take priority over other features, which play an important role in cognition. This paper introduces a new attention selection model called TPA (topological properties-based attention), which adopts topological properties and quaternion. In TPA, using Unit-linking PCNN (Pulse Coupled Neural Network) hole-filter expresses an important topological property (the connectivity) in visual attention selection. Meanwhile, using the quaternion Fourier transform based phase spectrum of an image or a frame in a video obtains the spatio-temporal saliency map, which shows the result of attention selection. Adjusting the weight of a topological channel can change its influence. The experimental results show that TPA reflects the real attention selection more accurately than PQFT (Phase spectrum of Quaternion Fourier Transform).

Image fusion technique based on non-subsampled contourlet transform and adaptive unit-fast-linking pulse-coupled neural network

A new image fusion technique based on non-subsampled contourlet transform (NSCT) and adaptive unit-fast-linking pulse-coupled neural network (PCNN) is presented. By using NSCT, multi-scale and multi-direction sparse decompositions of the source images are performed. Then, the basic PCNN model is improved to be an adaptive unit-fast-linking PCNN model, which synthesises the advantages of both unit-linking PCNN and fast-linking PCNN. The novel PCNN model utilises the clarity of each pixel in images as the linking strength β; moreover, the time matrix T of the sub-images can be obtained via the synchronous pulse burst property. Finally, the sub-images are fused by analysing the time matrix T and linking strength β. The experimental results show that the proposed approach is better than some current methods.

Feature Extraction using Unit-linking Pulse Coupled Neural Network and its Applications

In this paper, we use Unit-linking PCNN (Pulse Coupled Neural Network), the simplified model of PCNN consisting of spiking neurons, to code a 2-dimensional image into a 1-dimensional time sequence called global Unit-linking PCNN image icon or time signature, including features of the original image and having the translation, rotation, and scale invariance. Dividing an image into multiple parts can obtain local Unit-linking PCNN image icons corresponding to the image's local regions, which can reflect the local changes of the image. In the meantime, the global and the local Unit-linking PCNN image icons are used in navigation, object detection, and image authentication. In navigation, global Unit-linking PCNN image icon shows qualified performance especially in non-stationary-video navigation. Object detection using global Unit-linking PCNN image icon, is independent of variances of translation, rotation, and scale, and object segmentation is avoided. In image authentication, using local Unit-linking PCNN image icon can authenticate correctly some juggled images failed to authenticate by using local histogram or local mean intensity, and can locate the juggled positions in the juggled images with some accuracy.