An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images

Shortest path computation using pulse-coupled neural networks with restricted autowave

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.

In this paper, a novel multimodality Medical Image Fusion (MIF) method, based on Ripplet Transform Type-I (RT) using Pulse-Coupled Neural Network (PCNN) is presented. The proposed MIF scheme exploits the advantages of both RT and PCNN to obtain better results. The source medical images are first decomposed by discrete RT (DRT). The low-frequency subbands (LFSs) are fused using the ‘max selection’ rule. For the fusion of high-frequency subbands (HFSs) a PCNN model is utilized. Modified Spatial Frequency (MSF) in DRT domain is input to motivate the PCNN and coefficients in DRT domain with large firing times are selected as coefficients of the fused image. Then inverse DRT (IDRT) is applied to the fused coefficients to get the fused image. The performance of the proposed scheme is evaluated by various quantitative measures like Mutual Information (MI), Spatial Frequency (SF), and Entropy (EN) etc. Visual and quantitative analysis and comparisons show the effectiveness of the proposed scheme in fusing multimodality medical images.

To calculate the similarity or dissimilarity of DNA sequences, a new method is proposed based on pulse coupled neural network (PCNN) model. First, according to the characteristics of PCNN model, we encode DNA primary sequences using a simple coding method. Then we use PCNN model to extract the entropy sequence (ES) of the encoded DNA sequence; the ES expresses the features of the DNA sequences. At last, we calculate the similarity of the ES by Euclidean distance to get the similarity of DNA sequences. We take several sets of data to test our method. The experimental results demonstrate that our method is effective.

This paper describes a machine vision system for the detection of weft‐knitted fabric defects based on an adaptive pulse‐coupled neural network (PCNN) and Ridgelet transform. In order to classify defects according to their different texture features, two methods are implemented: an improved PCNN method to segment the defects such as hole and dropped stitch from background image and a Ridgelet transform method based on wavelet analysis to identify the defect such as course mark. In implementing the PCNN model, necessary parameters of PCNN model such as linking coefficient, connection weight, and iteration number are automatically calculated in accordance with the spatial distance of neurons, mean, and variance value of whole image, and the cross‐entropy criterion. The function of Ridgelet transform is to identify the straight line marks and fit the regression equation for simulating the course mark in the image. The Ridgelet transform model can be simplified as the combination of Radon and wavelet transforms. The parameters of detected line are acquired by wavelet analysis in Fourier semicircle region. The experiment materials were several plain and interlocked weft‐knitted fabrics with hole, dropped stitch, and course mark defects. The fabric images were captured by an area‐scan camera with a resolution of 600 × 800 pixels, and signal processing was controlled by a digital signal processing multiprocessor on the inspection machine. The validation tests proved that the system performed well.

It is the key step of automatic apple picking and sorting to segment of the apple image in natural environment using image information processing technology. In order to improve the intellectualization of apple picking process and effectively extract the target of image segmentation, a novel image segmentation method of Huaniu apple based on pulse coupled neural network (PCNN) and watershed algorithm is proposed. Firstly, the PCNN model is improved and optimized on the basis of image RGB to L*A*B space conversion. Then, the improved PCNN combined with watershed algorithm was used to segment the apple image on considering the image texture and color feature information. The theoretical analysis and experimental results show that the novel method is effectively able to segment target image, and can preserve the image details and the edges as well. The proposed algorithm has better subjective vision effect and objective quality than the traditional watershed and other related algorithms.

The process of image fusion is the process of enriching an image and improving the image's quality, so as to facilitate the subsequent image processing and analysis. With the increasing importance of image fusion technology, the fusion of infrared and visible images has received extensive attention. In today's deep learning environment, deep learning is widely used in the field of image fusion. However, in some applications, it is not possible to obtain a large amount of training data. Because some special organs of snakes can receive and process infrared information and visible information, the fusion method of infrared and visible light to simulate the visual mechanism of snakes came into being. Therefore, this paper takes into account the perspective of visual bionics to achieve image fusion; such methods do not need to obtain a significant amount of training data. However, most of the fusion methods for simulating snakes face the problem of unclear details, so this paper combines this method with a pulse coupled neural network (PCNN). By studying two receptive field models of retinal nerve cells, six dual-mode cell imaging mechanisms of rattlesnakes and their mathematical models and the PCNN model, an improved fusion method of infrared and visible images was proposed. For the proposed fusion method, eleven groups of source images were used, and three non-reference image quality evaluation indexes were compared with seven other fusion methods. The experimental results show that the improved algorithm proposed in this paper is better overall than the comparison method for the three evaluation indexes.

Face recognition is an important identification technology in daily life and one of the research hotspots in the field of computer vision and intelligent image processing. However, many algorithms based on Pulse Coupled Neural Network (PCNN) do not adequately describe features, same parameters are used to process the entire face image, ignoring the difference between the local recognition rate and gray distribution of each part of the face image. This paper proposes a block face recognition algorithm based on the weighted intensity PCNN model, we have refined the description of the features through parameter adaptation, partitioned the face according to the degree of contribution in the recognition process, and set the corresponding weight parameters through the improved grid search algorithm, which can effectively improve the PCNN model’s ability to recognize faces. Experiments show that in different face databases, the performance of this model is greatly improved compared with common methods such as PCA and ICA.

Pulse Coupled Neural Network (PCNN) is widely used in image processing for its basic characteristics of coupling mechanism and achieved some results. PCNN model has been improved as follows: Firstly, correlation coefficient is used to control the bonding strength. Secondly, the threshold setting is adjusted by the least error. Thirdly, A Trous transform is combined with PCNN model to form the combination forecasting model. The improved combination model was implemented in annual rainfall forecasting to check its feasibility.

It needs set parameters on image segmentation based on PCNN (Pulse Coupled Neural Network) now. This paper points out the new method for medical image segmentation based on improved PCNN and Tsallis entropy. The new methods can automatically segment the medical images without selecting the PCNN parameters. It gets the best results with combining with the Tsallis entropy. The new method is very useful for PCNN application in the medical images segmentation.

The quality of dental pulp cone beam computed tomography (CBCT) images significantly impacts the accuracy of disease diagnosis. However, existing CBCT images often suffer from deficiencies such as artifacts, noise, resolution limitations and data processing issues, which frequently lead to misdiagnoses and missed diagnoses. This study aims to construct a highly accurate dental pulp CBCT image segmentation model based on the pulse-coupled neural network (PCNN) approach. By optimizing the PCNN model, this work sought to enhance the segmentation accuracy of dental pulp CBCT images and to evaluate the clinical efficacy of dental pulp revascularization surgery through quantitative analysis of the CBCT images, thereby improving the diagnostic accuracy of imaging data and the reliability of treatment outcome assessments. To achieve this, a cross-entropy thresholding method was introduced to optimize the PCNN model, establishing the Otsu-PCNN model for CBCT pulp segmentation. The model performance was evaluated using accuracy, sensitivity, positive predictive value (PPV) and the area under the receiver operating characteristic curve (ROC). The study involved 43 patients with pulp diseases who underwent dental pulp revascularization, with follow-ups conducted preoperatively and at 3, 6, 9, 12 and 18 months postoperatively. Changes in root image area (RIA), tooth root length (TRL) and apical diameter (AHd) were recorded, along with the classification of root development types. The results showed that the sensitivity and PPV of the Otsu-PCNN model were significantly higher than those of the optimized PCNN model ([Formula: see text]). The detection time for the Otsu-PCNN model was 13.80 s, with an ROC area of 0.93. The overall effective treatment rate for all patients was 100%. RIA, TRL and AHd increased from 30.55%, 2.58% and 19.95% at 3 months postoperatively to 84.85%, 25.18% and 51.62% at 18 months, respectively. Of the roots examined, 22 (48.84%) exhibited Type I root development. This study demonstrates that the optimization of the PCNN model significantly enhances the segmentation accuracy of dental pulp CBCT images, and that dental pulp revascularization surgery can effectively improve root development, yielding significant results in the treatment of pulp diseases.

Pulse-coupled neural network (PCNN) model is widely used in digital image processing, but it is always a difficult problem to set network parameters and determine the optimal segmentation. By analyzing the firing characteristics and network parameters setting for the non-coupled linking PCNN, we propose an improved non-coupled linking PCNN for image segmentation. The model introduce the coupling effect of neighboring neurons into the dynamic threshold subsystem, and using a combination of DNN network, manual adjusting on step length for setting the dynamic threshold initial value. When the dynamic threshold initial value is adjusted properly, the optimal segmentation for the image can be obtained. Using the proposed algorithm in image segmentation of Lena and mammographic images, the segmentation effect similar to that of the traditional model can be obtained by less iteration, and it shows faster speed and better robustness.

The evaluation of flood disaster has great guidance to the systematic management of flood disaster. It is a new attempt that pulse coupled neural network model is used in the evaluation of flood disaster. This method adjusted threshold value through the output of impulse, and using dynamic threshold decided the grade of flood disaster. This model simplified the training of the weight compared to BP model, which was simpler, convenient and intuitionistic. Compared the results of pulse coupled neural network model with grey association, grey clustering and synthetic grey-fuzzy, it shows that pulse coupled neural network model is scientific and practicable to evaluation of flood disaster loss.

In this paper, a novel algorithm for microscopic color cell image edge detection based on Pulse Coupled Neural Networks (PCNN) is presented. Microscopic color image edge detection has proven to be a difficult task due to uneven brightness, cross-color existing between cell boundary and background caused by dyeing and noise. To solve these problems, this paper originally employs an improved PCNN model called multi-dimensional PCNN to implement edge detection of microscopic color cell image. PCNN is an advanced approach which aims at processing color images parellelly rather than separately dispose signal to every channel because it is characterized by synchronous neuronal burst and multi-dimensional convolution. To test the feasibility and effectiveness of multi-dimensional PCNN on edge detection of microscopic color cell images, experiments of several microscopic cell images are carried out. Empirical results show that multi-dimensional PCNN outperforms classical methods in terms of anti-noise and the accuracy of weak edge detection.

The visualization of medical images with advanced techniques, such as augmented reality and virtual reality, represent a breakthrough for medical professionals. In contrast to more traditional visualization tools lacking 3D capabilities, these systems use the three available dimensions. To visualize medical images in 3D, the anatomical areas of interest must be segmented. Currently, manual segmentation, which is the most commonly used technique, and semi-automatic approaches can be time consuming because a doctor is required, making segmentation for each individual case unfeasible. Using new technologies, such as computer vision and artificial intelligence for segmentation algorithms and augmented and virtual reality for visualization techniques implementation, we designed a complete platform to solve this problem and allow medical professionals to work more frequently with anatomical 3D models obtained from medical imaging. As a result, the Nextmed project, due to the different implemented software applications, permits the importation of digital imaging and communication on medicine (dicom) images on a secure cloud platform and the automatic segmentation of certain anatomical structures with new algorithms that improve upon the current research results. A 3D mesh of the segmented structure is then automatically generated that can be printed in 3D or visualized using both augmented and virtual reality, with the designed software systems. The Nextmed project is unique, as it covers the whole process from uploading dicom images to automatic segmentation, 3D reconstruction, 3D visualization, and manipulation using augmented and virtual reality. There are many researches about application of augmented and virtual reality for medical image 3D visualization; however, they are not automated platforms. Although some other anatomical structures can be studied, we focused on one case: a lung study. Analyzing the application of the platform to more than 1000 dicom images and studying the results with medical specialists, we concluded that the installation of this system in hospitals would provide a considerable improvement as a tool for medical image visualization.