Simplified Pulse Coupled Neural Network Research Articles

LI-DWT- and PD-FC-MSPCNN-Based Small-Target Localization Method for Floating Garbage on Water Surfaces

Typically, the process of visual tracking and position prediction of floating garbage on water surfaces is significantly affected by illumination, water waves, or complex backgrounds, consequently lowering the localization accuracy of small targets. Herein, we propose a small-target localization method based on the neurobiological phenomenon of lateral inhibition (LI), discrete wavelet transform (DWT), and a parameter-designed fire-controlled modified simplified pulse-coupled neural network (PD-FC-MSPCNN) to track water-floating garbage floating. First, a network simulating LI is fused with the DWT to derive a denoising preprocessing algorithm that effectively reduces the interference of image noise and enhances target edge features. Subsequently, a new PD-FC-MSPCNN network is developed to improve the image segmentation accuracy, and an adaptive fine-tuned dynamic threshold magnitude parameter V and auxiliary parameter P are newly designed, while eliminating the link strength parameter. Finally, a multiscale morphological filtering postprocessing algorithm is developed to connect the edge contour breakpoints of segmented targets, smoothen the segmentation results, and improve the localization accuracy. An effective computer vision technology approach is adopted for the accurate localization and intelligent monitoring of water-floating garbage. The experimental results demonstrate that the proposed method outperforms other methods in terms of the overall comprehensive evaluation indexes, suggesting higher accuracy and reliability.

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%.

Classification of brain MR images using Modified version of Simplified Pulse-Coupled Neural Network and Linear Programming Twin Support Vector Machines

Classification of brain MR images using Modified version of Simplified Pulse-Coupled Neural Network and Linear Programming Twin Support Vector Machines

An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

COVID-19 remains to proliferate precipitously in the world. It has significantly influenced public health, the world economy, and the persons’ lives. Hence, there is a need to speed up the diagnosis and precautions to deal with COVID-19 patients. With this explosion of this pandemic, there is a need for automated diagnosis tools to help specialists based on medical images. This paper presents a hybrid Convolutional Neural Network (CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography (CT) images. The proposed approach is employed to classify and segment the COVID-19, pneumonia, and normal CT images. The classification stage is firstly applied to detect and classify the input medical CT images. Then, the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT images. The classification stage is implemented based on a simple and efficient CNN deep learning model. This model comprises four Rectified Linear Units (ReLUs), four batch normalization layers, and four convolutional (Conv) layers. The Conv layer depends on filters with sizes of 64, 32, 16, and 8. A 2 × 2 window and a stride of 2 are employed in the utilized four max-pooling layers. A soft-max activation function and a Fully-Connected (FC) layer are utilized in the classification stage to perform the detection process. For the segmentation process, the Simplified Pulse Coupled Neural Network (SPCNN) is utilized in the proposed hybrid approach. The proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region, accurately. To summarize the contributions of the paper, we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis system. Once the images are accepted by the system, it is possible to perform further processing through a segmentation process to isolate the regions of interest in the images. The region of interest can be assesses both automatically and through experts. This strategy helps so much in saving the time and efforts of specialists with the explosion of COVID-19 pandemic in the world. The proposed classification approach is applied for different scenarios of 80%, 70%, or 60% of the data for training and 20%, 30, or 40% of the data for testing, respectively. In these scenarios, the proposed approach achieves classification accuracies of 100%, 99.45%, and 98.55%, respectively. Thus, the obtained results demonstrate and prove the efficacy of the proposed approach for assisting the specialists in automated medical diagnosis services.

Brain Tumor Detection Using Selective Search and Pulse-Coupled Neural Network Feature Extraction

The identification of tumorous tissues in the brain based on Magnetic Resonance Images (MRI) analysis is a challenging and time consuming task that highly depends on radiologists expertise. As prompt diagnosis of tumors can often be inherent to the patient's survival, it is however crucial to decrease the amount of time spent on the manual analysis of MRI while increasing the accuracy of the detection process. To tackle these issues, many research works have already investigated efficient computer vision systems. They offer new opportunities to assist health care providers in the establishment of fast and more accurate tumor detection, classification and segmentation. However, often based on deep learning methods, the development and tuning of these solutions remains time and energy consuming while inducing a lack of explainability in the decision making system. In this study, we respond to these issues by solving a brain tumor detection task using the Selective Search (SS) algorithm coupled with a simplified Pulse-Coupled Neural Network (PCNN) for visual feature extraction and detection validation. The performed experiments showed promising results in terms of computational cost and detection accuracy. This leads to the development of a light-weight brain tumor detection system.

Correction to: Automated diagnosis system for detection of the pathological brain using fast version of simplified pulse-coupled neural network and twin support vector machine

Correction to: Automated diagnosis system for detection of the pathological brain using fast version of simplified pulse-coupled neural network and twin support vector machine

Automated Diagnosis system for detection of the pathological brain using Fast version of Simplified Pulse-Coupled Neural Network and Twin Support Vector Machine

Brain abnormalities are neurological disorders of the human nervous system that contain biochemical, electrical, and structural changes in the brain and spinal cord. However, such changes produce diverse symptoms like paralysis, amnesia, and muscle weakness. The diagnosis of these abnormalities is crucial for treatment planning in the early stage to limit the progression of diseases. The brain Magnetic Resonance (MR) images are extensively used for treatment planning, but manually diagnosis of MR images is a time-consuming, expensive, and cumbersome task. Hence, in this paper, we have proposed the automated Computer-Aided Diagnosis (CAD) system for classification of brain MR images. These images are skill-stripped for removing the irrelevant tissues that improve the quality of images. We have developed the Fast version of Simplified Pulse-Coupled Neural Network (F-SPCNN) to segment the region of interest. Further, the features are extracted from the segmented images by using the Ripplet Transform (RT). Subsequently, Probabilistic Principal Component Analysis (PPCA) is employed for reducing the dimensionality of features. Finally, Twin Support Vector Machine (TWSVM) is applied for classification of brain MR images. The extensive simulation results on three standard datasets, e.g., DS-66, DS-160, and DS-255, demonstrate that the proposed method achieves better performance than the state-of-the-art methods.

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.

Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation

Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation because of the complexity of parameter setting and the need for manual setting. This paper presents a method of searching simplified PCNN parameters by using Harris Hawks optimization (HHO) algorithm. For one thing the number of parameters of PCNN is reduced without affecting the segmentation effect, for another the corresponding parameters of PCNN are searched quickly and accurately by intelligent optimization algorithm. Then, image entropy (H) and mutual information entropy (MI) are introduced as fitness functions. The performance of HHO-PCNN is compared with WOA-PCNN, SCA-PCNN, SSA-PCNN, PSO-PCNN, GWO-PCNN, MVO-PCNN, Otsu and K-means by performance indicators (UM, CM, Precision, Recall, and Dice). The experimental results verify the superiority of this method in image segmentation.

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.

Image Feature Extraction and Retrieval of the Euler Number to Chinese Herbal Medicine based on PCNN

In order to extract microscopic image feature information of traditional Chinese medicine effectively and simply and improve the precision of image retrieval, an image Euler number feature extraction and retrieval algorithm based on pulse coupled neural network (PCNN) was proposed. First of all, the PCNN model is introduced from the perspective of biological tissue image processing. Secondly, the CHM image is segmented into a series of binary correlation images using simplified PCNN. The Euler number of the edge information of each binary image in the generated binary image sequence is calculated, and a one-dimensional feature vector is constructed. Finally, the Euclidean distance is used to measure the similarity of the feature vector in the retrieval of CHM microscopic images. Euler number feature extraction and retrieval method is characterized by simple calculation and small amount of data. Compared with other methods, this algorithm has stronger robustness and retrieval accuracy, and certain feasibility.

Automatic adjustment of the pulse-coupled neural network hyperparameters based on differential evolution and cluster validity index for image segmentation

The pulse-coupled neural network (PCNN) is a cortical model that can be used in image segmentation applications. The performance of the PCNN depends on adjusting its hyperparameters, where population-based metaheuristics, such as evolutionary algorithms, have been used to perform this task by optimizing a fitness function. In this regard, the entropy criterion is a common fitness function used to evaluate the quality of potential PCNN solutions. However, maximizing the entropy is related to maximize the inter-group separation, but the intra-group cohesion is unconsidered. In this regard, a cluster validity index (CVI) can be used as a fitness function, which defines a relationship between inter-group separation and intra-group cohesion. Therefore, we propose using a CVI to quantify the segmentation quality generated by the PCNN given a set of hyperparameters adjusted by the differential evolution algorithm. The proposed approach is tested on a dataset of natural images, where every image has three reference segmentations; thus, the Jaccard index is used to measure the segmentation performance. The experimental results reveal that a simplified PCNN, when used jointly with the Silhouette index, obtains the best performance with a mean Jaccard value of 0.77, whereas the entropy criterion attains 0.41. Additionally, the proposed approach is tested on two modalities of medical images to show its applicability in other kinds of images. The results suggest that using a CVI instead of the entropy criterion can improve the segmentation performance of the PCNN.

A Novel Method for Image Segmentation Based on Simplified Pulse Coupled Neural Network and Gbest Led Gravitational Search Algorithm

This paper proposed a novel image segmentation method based on simplified pulse coupled neural network (SPCNN), which was optimized by gbest led gravitational search algorithm (GLGSA) that combined gravitational search algorithm (GSA) with gbest agent memory ability. To evaluate the performance of GLGSA, we applied it to 23 standard benchmark functions and compared with GSA and GGSA. The results showed that the GLGSA had better performance in term of convergence and avoidance of local minima. Besides, in order to improve the accuracy of segmentation, the fitness function consisted of cross entropy parameter, edge matching, and noise control. To verify the efficiency of our method, we compared it with the state-of-the-art algorithms, such as Otsu, GA Renyi, and PSO-PCNN, using the gray nature images from the Berkeley segmentation dataset. Finally, the subjective visual analysis and quantitative analysis that included the uniformity measure, region contrast measure, structural similarity, and comprehensive evaluation were used to evaluate the segmented images. The comparison results demonstrated that our proposed method could get better segmentation results.

SAR Image Invariant Feature Extraction by Anisotropic Diffusion and Multi-Gray Level Simplified PCNN

Besides the target in synthetic aperture radar (SAR) image, the structural information remained in speckle and shadow is also very important when we extract the invariant feature of SAR image. However, the output of the classical pulse coupled neural network (PCNN) is a binary value. All the information remained in speckle and shadow is lost, when the classical PCNN is used to process SAR image directly. To overcome this problem, a multi-gray level simplified PCNN (MSPCNN) is proposed in this paper. All the useful information in speckle, shadow, and target can be considered when we use MSPCNN to compute the invariant feature of the SAR target. In order to suppress the negative influence of speckle noise and keep the useful structured information, the improved speckle reducing algorithm (SRAD) is used first. By an adaptive threshold delt0, we can keep the remained speckle in different SAR images to an ideal same level. The negative influence of the remained speckle to different targets' signature becomes basically the same and can be ignored. Combining with SRAD and MSPCNN, the SAR image invariant feature extraction scheme AD-MSPCNN is put forward. After analyzing the performance of different signature computing methods, we splice the time signature and the entropy signature together and use the new spliced vector as the invariant feature of the SAR target. The validity and robustness of AD-MSPCNN are proved by the experimental results on MSTAR database.

An image segmentation method of a modified SPCNN based on human visual system in medical images

An image segmentation method of a modified simplified pulse-coupled neural network (MSPCNN) based on human visual system (HVS) is proposed for medical images. The method successfully determines the stimulus input of the MSPCNN according to the characteristics of PCNN and HVS. In order to accomplish the goal, we attempt to deduce the sub-intensity range of central neurons firing by introducing neighboring firing matrix Q and calculating intensity distribution range based on a new MSPCNN(NMSPCNN), and then reveal the way how sub-intensity range parameter Sint generates the stimulus input Sioij closer to HVS. Besides, we try to substitute the above stimulus input into the MSPCNN to extract more suitable lesions for medical images. In contrast to prevalent PCNN models, the MSPCNN has higher segmentation accuracy rates and lower computational complexity because of the parameter setting method. Finally, a good proposed method comparing with the state-of-the-art methods has a better performance, presenting the overall metric OEM with MIAS of 0.8784, DDSM of 0.8606 and gallstones of 0.8585.

Image segmentation for whole tomato plant recognition at night

A simplified pulse-coupled neural network was developed based on comentropy gradient for segmentation of tomato plant images captured at night. In this network, the connection matrix was determined using differences in the value and distance between the center and other pixels in a neighborhood. To minimize the number of iterations, the model used linear threshold attenuation function on a limited range. A new evaluation method based on comentropy gradient was also used to determine the best image segmentation result during iteration. Evaluation of 385 tomato plant images captured at night showed that the simplified pulse-coupled neural network segmented the images of tomato plants captured at night. The method improved the image segmentation performance, with best and false rates of 67.79% and 9.61%, respectively, for all segmentation results. In addition, the best and false rates are 59.22% and 13.77%, respectively, for the method based on the maximum comentropy. The proposed algorithm exhibited the optimal segmentation performance, with the best rate of 91.67%, the second best rate of 8.33%, and the false rate of 0.00% for images with 640 × 480 pixel captured at distances from 300 mm to 500 mm under various lighting conditions by using the lighting system composed of two 25 W incandescent light bulbs in the opposite layout at night. The linear threshold attenuation function improved the real-time performance of the method. For 640 × 480 and 320 × 240 pixel images, the average running times were 2.69 and 0.61 s, respectively, compared with the original times of 12.02 and 2.90 s.

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.