Simplified PCNN Model Research Articles

Value of Artificial Neural Network Ultrasound in Improving Breast Cancer Diagnosis.

Ultrasound-guided needle biopsy based on artificial neural network, as a safe, effective, and simple preoperative pathological diagnosis technique, has been widely used in clinical practice. Ultrasound-guided needle biopsy based on artificial neural networks for suspicious breast lesions found in conventional ultrasound examinations is an effective method for preoperative diagnosis. The purpose of this article is to study the value of artificial neural network ultrasound in improving breast cancer diagnosis. This article summarizes the neuron model of PCNN by observing and studying its impulse synchronization phenomenon. Aiming at gray-scale images disturbed by mixed noise (impulse noise and the Gaussian noise), a comprehensive filtering algorithm based on the simplified PCNN model is proposed. In this paper, the benign and malignant breast masses were evaluated based on the two-dimensional and three-dimensional ultrasound imaging signs of the mass, and compared with the postoperative pathological results, a logistic regression model was established to analyze the shape, boundary, microcalcification, and posterior echo attenuation of the mass, values for keratinization or burrs, convergent signs, and blood flow classification in the differential diagnosis of benign and malignant. In this paper, a color ultrasound diagnostic device is used, Sonobi is used as a contrast medium, and the injection volume is 2.4 ml/dose. During the imaging process, the sound image performance of the lesion is dynamically observed, the original dynamic data are stored throughout the whole process, and the playback analysis is performed after the imaging is completed. Studies have shown that CDUS elastography (UE) combined with MRI can increase the sensitivity of breast cancer diagnosis, with a diagnostic accuracy rate of 92.4%.

An improved simplified PCNN model for salient region detection

As PCNN is modulated by using the pulse-coupled synaptic mechanisms, it has a great potential for image processing in a complex real-world environment, especially in images. A new simplified pulse coupled neural network (SPCNN) is proposed. This new model uses the pixel intensity with the actual physical meanings as the input parameters instead of the abstract network parameters in the original SPCNN. In order to achieve this goal, we try to derive the general formulae of dynamic threshold and internal activity of the SPCNN according to the dynamic properties of neurons and then deduce the relationship between the pixel intensity and the abstract parameters. Then, the relationship is transformed into an objective optimization problem to obtain the appropriate abstract parameters. Finally, extensive experiments are conducted on seven widely used datasets to demonstrate the effectiveness of the proposed method and shown improvement on the salient region detection.

Multi-Modality Image Fusion in Adaptive-Parameters SPCNN Based on Inherent Characteristics of Image

Due to the characteristics of the diversity and complement of multi-modality images, the fused method in SPCNN of adaptive parameters based on inherent characteristics of image is proposed in this paper, in order to extract the optimal feature information from the different images. Based on the analysis of traditional pulse coupled neural network, the simplified PCNN model based on SCM is designed. In order to highlight the different feature of different modality images, the adaptive parameters of SPCNN are set on the basis of the inherent characteristics of images, and the fused method in NSST is proposed. Firstly, the source image is decomposed by NSST with multiple scales and multiple directions. In the high-frequency coefficients, the SPCNN with adaptive parameters is adopted to calculate the firing time of each coefficient, the high-frequency coefficients are fused by comparing the firing times. In order to protect the details and edge information of the low-frequency coefficients, the combining of regional energy and energy of gradient is regarded as the active level, the low-frequency coefficients is fused by the weighted fusion rule. Finally, the fused image is reconstructed by inverse NSST transform. The experimental results show that the proposed method is superior to the other five classical methods, the fused results of the proposed method are consistence in the human visual perception system. The contrast and detail of fused image are perfect.

A new image segmentation method based on the ICSO-ISPCNN model

To address the issue of parameter settings in a pulse coupled neural network (PCNN), we propose a new image segmentation method based on the improved chicken swarm optimization algorithm and improved simplified PCNN (ICSO-ISPCNN) model. First, we improved a simplified PCNN model by modifying the dynamic threshold function and meanwhile improved the chicken swarm optimization (CSO) algorithm by introducing the survival of the fittest mechanism. Then, a product cross entropy is utilized as the fitness function of the ICSO algorithm, and the parameter values of the ISPCNN model are determined through the effective teamwork of roosters, hens, and chicks in the chicken swarm. Finally, we can achieve the automatic image segmentation via the ISPCNN model, which has the best parameter values. The detailed experiments indicate that our method has more superior performance in terms of convergence and segmentation accuracy than methods based on the genetic algorithm and ant colony optimization algorithm.

Research on fundus image registration and fusion method based on nonsubsampled contourlet and adaptive pulse coupled neural network

We present a registration and fusion method of fluorescein fundus angiography image and color fundus image which combines Nonsubsampled Contourlet (NSCT) and adaptive Pulse Coupled Neural Network (PCNN). Firstly, we register two images by Speeded Up Robust Features (SURF) feature points, the nearest neighbor and the next nearest neighbor distance ratio method to eliminate the spatial difference between the source images. Secondly, we use Random Sample Consensus (RANSAC) algorithm to achieve precise matching of feature points. Then, according to the transformation parameters obtained by RANSAC algorithm, we perform spatial transformation on the floating image to complete the registration. Finally, we obtain the low-frequency sub-band and high-frequency sub-band of the image to be fused by NSCT decomposition. The low-frequency sub-band is fused by the regional energy. The high-frequency sub-bands are studied using a simplified-PCNN model and the Particle Swarm Optimization algorithm. The link strength of the simplified-PCNN is an improved Laplacian energy and the images are fused based on the number of times the pixels are ignited. The proposed method has higher average gradient (AG) value and information entropy (IE) value and lower relative global dimensional synthesis error (ERGAS) than the existing fusion methods of the fundus image. The fusion image can accurately synthesize the image information, clarify the performance of the details, and has better spectral quality in the spectral range. The image of fused provides an effective reference for the clinical diagnosis of fundus diseases.

Non‐subsampled shearlet transform based MRI and PET brain image fusion using simplified pulse coupled neural network and weight local features in YIQ colour space

Magnetic resonance imaging (MRI) and positron emission tomography (PET) image fusion is a recent hybrid modality used in several oncology applications. The MRI image shows the brain tissue anatomy and does not contain any functional information, while the PET image indicates the brain function and has a low spatial resolution. A perfect MRI–PET fusion method preserves the functional information of the PET image and adds spatial characteristics of the MRI image with the less possible spatial distortion. In this context, the authors propose an efficient MRI–PET image fusion approach based on non-subsampled shearlet transform (NSST) and simplified pulse-coupled neural network model (S-PCNN). First, the PET image is transformed to YIQ independent components. Then, the source registered MRI image and the Y -component of PET image are decomposed into low-frequency (LF) and high-frequency (HF) subbands using NSST. LF coefficients are fused using weight region standard deviation (SD) and local energy, while HF coefficients are combined based on S-PCCN which is motivated by an adaptive-linking strength coefficient. Finally, inverse NSST and inverse YIQ are applied to get the fused image. Experimental results demonstrate that the proposed method has a better performance than other current approaches in terms of fusion mutual information, entropy, SD, fusion quality, and spatial frequency.

Catenary image segmentation using the simplified PCNN with adaptive parameters

The catenary fault detection method based on image processing technique plays an important role in the railway transportation safety system, and image segmentation is the hot and critical stage of this detection method. Conventional pulse coupled neural network (PCNN) model has too many parameters which should be set with values in advance for image segmentation. However, the setting of parameters is critical but complex task, and the segmentation effect and efficiency significantly rely on the network parameters setting. To overcome the above mentioned disadvantages, the simplified PCNN (SPCNN) model based on the conventional PCNN is introduced. First, simplify the input domain, the external input signal is directly used as the input neuron. Then the linking input and the dynamic threshold of the neuron are simplified. Furthermore, the linking coefficients of the modulation field are adaptively determined by normalized mean square error, and the iteration number is adaptively determined in accordance with the minimum cross entropy. In this simplified model, some parameters are reduced but the important mechanisms of PCNN are still remained. Finally, several sets of real-time collected catenary images are segmented by the SPCNN model with adaptive parameters. Experiments results show that the proposed method not only significantly improves image segmentation performance than the conventional image segmentation methods but also shows the continuity and integrity of the segmented images, especially for the pull rods, posts, insulators and other parts of catenary. Furthermore, it is superior to the conventional image segmentation in terms of parameters setting, visual appearance and objective evaluation criteria of VOIand PRIvalues.

Saliency motivated improved simplified PCNN model for object segmentation

Abstract Based on the fact that PCNN and saliency detection method all can achieve the better simulation of HVS to locate the objects that have the most interests in an image, a novel approach for object segmentation, termed as saliency motivated improved simplified pulse coupled neural network (SM-ISPCNN) algorithm, is proposed in this paper. Instead of adopting pure gray-scale to activate the ISPCNN neurons, it is better to introduce the saliency feature value to motivate this model. The introduced saliency stimulus applies a sliding window to precisely exploit the distributions of the objects and surroundings, weakens the influence of background while retaining the region of interest; the highlight of this ASLFC saliency feature lies on: (1) the saliency estimation is based on semi-local areas instead of pixel level; (2) the estimations of the conditional distributions is manipulated via integral histogram approach; (3) the adaptive prior probability setting method is employed to achieve more promising saliency map. For improvement of convergence speed of SM-ISPCNN model for object segmentation, at each iteration, we regard top 5 regions as feedback input for next iteration, which can raise the robustness of SM-ISPCNN model against noise and other interferences. We demonstrate the proposed model based on the mammograms from open and common database of MIAS, gray images from Weizmann segmentation evaluation database and color images from public database with ground truth annotations. Compared with five competitive methods, our model has the obvious superiority for segmentation capacity and algorithm robustness, furthermore, it does not requires any training and can be used in various occasions of object segmentation. In addition, this method is verified on the mammograms from Gansu Provincial Cancer Hospital, the detection results reveal that this model has great potential in clinical application.

An automatic segmentation method of a parameter-adaptive PCNN for medical images.

Since pre-processing and initial segmentation steps in medical images directly affect the final segmentation results of the regions of interesting, an automatic segmentation method of a parameter-adaptive pulse-coupled neural network is proposed to integrate the above-mentioned two segmentation steps into one. This method has a low computational complexity for different kinds of medical images and has a high segmentation precision. The method comprises four steps. Firstly, an optimal histogram threshold is used to determine the parameter [Formula: see text] for different kinds of images. Secondly, we acquire the parameter [Formula: see text] according to a simplified pulse-coupled neural network (SPCNN). Thirdly, we redefine the parameter V of the SPCNN model by sub-intensity distribution range of firing pixels. Fourthly, we add an offset [Formula: see text] to improve initial segmentation precision. Compared with the state-of-the-art algorithms, the new method achieves a comparable performance by the experimental results from ultrasound images of the gallbladder and gallstones, magnetic resonance images of the left ventricle, and mammogram images of the left and the right breast, presenting the overall metric UM of 0.9845, CM of 0.8142, TM of 0.0726. The algorithm has a great potential to achieve the pre-processing and initial segmentation steps in various medical images. This is a premise for assisting physicians to detect and diagnose clinical cases.

Object detection system based on multimodel saliency maps

Detection of visually salient image regions is extensively applied in computer vision and computer graphics, such as object detection, adaptive compression, and object recognition, but any single model always has its limitations to various images, so in our work, we establish a method based on multimodel saliency maps to detect the object, which intelligently absorbs the merits of various individual saliency detection models to achieve promising results. The method can be roughly divided into three steps: in the first step, we propose a decision-making system to evaluate saliency maps obtained by seven competitive methods and merely select the three most valuable saliency maps; in the second step, we introduce heterogeneous PCNN algorithm to obtain three prime foregrounds; and then a self-designed nonlinear fusion method is proposed to merge these saliency maps; at last, the adaptive improved and simplified PCNN model is used to detect the object. Our proposed method can constitute an object detection system for different occasions, which requires no training, is simple, and highly efficient. The proposed saliency fusion technique shows better performance over a broad range of images and enriches the applicability range by fusing different individual saliency models, this proposed system is worthy enough to be called a strong model. Moreover, the proposed adaptive improved SPCNN model is stemmed from the Eckhorn’s neuron model, which is skilled in image segmentation because of its biological background, and in which all the parameters are adaptive to image information. We extensively appraise our algorithm on classical salient object detection database, and the experimental results demonstrate that the aggregation of saliency maps outperforms the best saliency model in all cases, yielding highest precision of 89.90%, better recall rates of 98.20%, greatest F -measure of 91.20%, and lowest mean absolute error value of 0.057, the value of proposed saliency evaluation E H A reaches to 215.287. We deem our method can be wielded to diverse applications in the future.

An Image Fusion Method Based on NSCT and Dual-channel PCNN Model

NSCT is one of useful multiscale geometric analysis tools, which takes full advantage of geometric regularity of image intrinsic structures. The dual-channel PCNN is a simplified PCNN model, which can process multiple images by a single PCNN. This saves time in the process of image fusion and cuts down computational complexity. In this paper, we present a new image fusion scheme based on NSCT and dual-channel PCNN. Firstly, the fusion rules of subband coefficients of NSCT are discussed. For the fusion rule of low frequency coefficients, the maximum selection rule (MSR) is used. Then, for the fusion rule of high frequency coefficients, spatial frequency (SF) of each high frequency subband is considered as the gradient features of images to motivate dual-channel PCNN networks and generate pulse of neurons. At last, fused image is obtained by using the inverse NSCT transform. In order to show that the proposed method can deal with image fusion, we used two pairs of images as our experimental subjects. The proposed method is compared with other five methods. The performance of various methods is mathematically evaluated by using four image quality evaluation criteria. Experimental comparisons conducted on different fusion methods prove the effectiveness of the proposed fusion method

Insulator Image Denoising Based on the Simplified PCNN Model

A new denoising method was proposed in the paper according to the characteristics of insulator infrared image with impulse noise. First, based on the pulse coupled neural network (PCNN) to detect the location of the impulse noise pixels, while maintaining the same non-noise pixels. and then according to the characteristics of the impulse noise, the window size of the filter was adaptively determined by calculating the noise intensity of the image. The pixels with maximum and minimum gray value in filtering window are excluded, using the left pixels similarity calculation out weights. A new weighted filtering algorithm is used to filter noise pixels. The experiments show that the method is better than the median filter in peak signal-to-noise ratio (PSNR), and has better image edge details protection ability.