Neural Network Based Image Segmentation Research Articles

A fish image segmentation methodology in aquaculture environment based on multi-feature fusion model

Underwater fish image processing is one of the key technologies to realize intelligent aquaculture. However, due to the complexity of marine environments, underwater fish images usually have the characteristics of color cast, unbalanced contrast, and blur. Current underwater fish image segmentation methods lack adaptive models and have low segmentation accuracy. Hence, this paper constructs a convolutional neural network-based image segmentation model. This paper first proposes a fish image preprocessing method based on pixel threshold segmentation. To enhance the important features in a fish image, this method uses the minimum Euclidean distance between the original image peaks to redefine the threshold and fuses the thresholded image with the original image. Second, to strengthen the extraction of high-level semantic features of images, the multiscale attentional feature extraction module (MAFEM), which fuses the adaptive channel attention mechanism with the hybrid dilated convolutional pyramid pooling module, is proposed. In this paper, a data set in voc format is produced based on underwater fish images, and this data set is used to verify the model in this paper. The mean intersection over union (MIoU) reaches 92.6%. Compared with other traditional segmentation models, the MIoU, mean pixel accuracy (MPA), and balanced F score (F1-score) of the segmentation results of the model in this paper are increased by averages of 1.84%, 0.785%, and 1.18%, respectively. The experimental results show that this model has a better segmentation effect than other models and provides a theoretical basis for intelligent monitoring of underwater fish body length measurement, weight estimation, and discrimination of growth and health statuses.

Automatic detection and measurement of ground crack propagation using deep learning networks and an image processing technique

We present a novel approach for automatic identification of ground crack propagation and measurement of their dimensions using deep learning and an image processing technique. The different convolutional neural networks (U-Net, LinkNet, Feature Pyramid Network (FPN), and Deeplabv3) and a traditional image-processing technique based on the Otsu method were employed to identify ground cracks and calculate their lengths and widths on camera views. The deep learning models were trained and tested on an image dataset obtained from laboratory slope experiments and field crack images. Ground crack identified by the convolutional neural network-based image segmentation models afforded average F1 score of 0.877 to 0.896 and processing speed of 7.48 to 8.01 frame per second (FPS), thus better than those of traditional image processing (F1 score of 0.65 and speed of 0.01 FPS). In addition, the accuracies of the calculated crack dimensions on both images and videos ranged from 86.7 to 99.9%.

A Shine Muscat Grape Berry Detection and Grape Cluster Compactness Estimation for Assessment of Grape Quality Based on Instance Segmentation Methods

Highlights Mask R-CNN ResNet-101 was analyzed to have the highest detection accuracy for grape berries. Grape cluster compactness is one of the important factors that determines the quality of grapes. Indicator DGC was used to intuitively calculate the compactness of grape cluster. An average error of 1.6 mm occurred with the verification of diameter estimation algorithm. Abstract. Consumption and cultivation of grapes are constantly increasing because they are known as alkaline foods, which relieve fatigue by accelerating carbohydrate metabolism in the human body. Among these grapes, a Shine Muscat doesn’t have the bitter taste of ordinary grapes, has low acidity, and has a crunchy texture, which results in growing interest in modern society. As the consumption of Shine Muscat increases, an automated process to save labor and time becomes essential. Particularly, one challenge that needs to be solved is the determination of grape quality based solely on visual evaluations by experts, which can result in inconsistent pricing for consumers. In this study, an algorithm was adopted to detect the grape berries from a bunch of single grapes by acquiring RGB images of the harvested Shine Muscat. Mask R-CNN, a convolutional neural network-based image segmentation technique, was used with various backbones to compare and evaluate the performance of each model. Results showed that Mask R-CNN ResNet 101 had the highest AP (Average Precision) value of 0.961 among all models. In addition, indexes such as the size (diameter) of each grape berry, the area of the grape on the image, and the area of the empty space that are required to find the compactness of the grape cluster are obtained. In particular, it was analyzed that the average error value (mm) and percent error (%) of the diameter estimation algorithm developed in this study were 1.60 mm and 4.79%, respectively. Keywords: Keywords., Density of Grape Cluster (DGC), Diameter estimation, Grape berry, Grape cluster compactness, Mask R-CNN, Object detection.

Structured cerebellar connectivity supports resilient pattern separation.

The cerebellum is thought to helpdetect and correct errors between intended and executed commands1,2 and is critical for social behaviours, cognition and emotion3-6. Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise7. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network's first layer8-13. However, maximizing encoding capacity reduces the resilience to noise7. To understand how neuronal circuits address this fundamental trade-off, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest that these redundant, non-random connectivity motifs increase the resilience to noise at a negligible cost to the overall encoding capacity. This work reveals how neuronal network structure can support a trade-off between encoding capacity and redundancy, unveiling principles of biological network architecture with implications for the design of artificial neural networks.

Application of medical clinic system using improved neural network-based image segmentation technique

Application of medical clinic system using improved neural network-based image segmentation technique

P‐3.1: Convolutional Neural Network based Image Segmentation Algorithm for Dual‐Layer LCDs

Dual-layer LCDs have the advantage of high contrast ratio, but due to the physical distance between the two LC panels, parallax error may appear when viewed off-axis, affecting the display quality. In this paper, we propose a convolutional neural network-based algorithm to mitigate parallax error and improve the display quality. First, a suitable training data set is selected and the input image is processed by a convolutional neural network to output a front image and a rear image. These two images are staggered and multiplied to obtain reproduced images at different angles. The reproduced image is compared with the original image by means of a loss function, and the parameters in the convolution are continuously updated to achieve the best display quality. The algorithm proposed in this paper can effectively mitigate the parallax error phenomenon of a dual-layer LCD and improve the display quality, and the computational efficiency is also higher than the traditional algorithms.

Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging.

Multispectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain. It can be greatly augmented by magnetic resonance imaging (MRI) which offers excellent soft-tissue contrast and high-resolution brain anatomy. Nevertheless, registration of MSOT-MRI images remains challenging, chiefly due to the entirely different image contrast rendered by these two modalities. Previously reported registration algorithms mostly relied on manual user-dependent brain segmentation, which compromised data interpretation and quantification. Here we propose a fully automated registration method for MSOT-MRI multimodal imaging empowered by deep learning. The automated workflow includes neural network-based image segmentation to generate suitable masks, which are subsequently registered using an additional neural network. The performance of the algorithm is showcased with datasets acquired by cross-sectional MSOT and high-field MRI preclinical scanners. The automated registration method is further validated with manual and half-automated registration, demonstrating its robustness and accuracy.

Rule and Neural Network-Based Image Segmentation of Mice Vertebrae Images.

BackgroundImage segmentation is a fundamental technique that allows researchers to process images from various sources into individual components for certain applications, such as visual or numerical evaluations. Image segmentation is beneficial when studying medical images for healthcare purposes. However, existing semantic image segmentation models like the U-net are computationally intensive. This work aimed to develop less complicated models that could still accurately segment images.MethodologyRule-based and linear layer neural network models were developed in Mathematica and trained on mouse vertebrae micro-computed tomography scans. These models were tasked with segmenting the cortical shell from the whole bone image. A U-net model was also set up for comparison.ResultsIt was found that the linear layer neural network had comparable accuracy to the U-net model in segmenting the mice vertebrae scans.ConclusionsThis work provides two separate models that allow for automated segmentation of mouse vertebral scans, which could be potentially valuable in applications such as pre-processing the murine vertebral scans for further evaluations of the effect of drug treatment on bone micro-architecture.

Neural network based image segmentation for spatter extraction during laser-based powder bed fusion processing

In situ monitoring of spatter signatures is often employed to improve product quality during laser-based powder bed fusion (LPBF). This paper describes a novel neural network (NN) based image segmentation method for spatter extraction with a simple labeling process and high accuracy results. Use of a 290–1100 nm waveband high-speed camera allowed capturing images with more complete spatter signatures and a more complex background compared with previous LPBF studies. Conventional image segmentation approaches are inadequate to perform spatter extraction because of the complex background. The proposed NN-based image segmentation method split images into a block grid and segmented each block using a parallel convolutional neural network (CNN) and a thresholding neural network (TNN), which permitted extracting 80.48% of spatters in only 70 ms. Furthermore, the ability to extract spatters connected to a molten pool distinguishes the proposed NN-based image segmentation method from conventional image segmentation approaches.

TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors.

In this paper, we introduce and compare different approaches for incorporating shape prior information into neural network-based image segmentation. Specifically, we introduce the concept of template transformer networks, where a shape template is deformed to match the underlying structure of interest through an end-to-end trained spatial transformer network. This has the advantage of explicitly enforcing shape priors, and this is free of discretization artifacts by providing a soft partial volume segmentation. We also introduce a simple yet effective way of incorporating priors in the state-of-the-art pixel-wise binary classification methods such as fully convolutional networks and U-net. Here, the template shape is given as an additional input channel, incorporating this information significantly reduces false positives. We report results on synthetic data and sub-voxel segmentation of coronary lumen structures in cardiac computed tomography showing the benefit of incorporating priors in neural network-based image segmentation.

Review on 2D and 3D MRI Image Segmentation Techniques

Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics. Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation. This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.

Multilevel image segmentation with adaptive image context based thresholding

Neural network based image segmentation techniques primarily focus on the selection of appropriate thresholding points in the image feature space. Research initiatives in this direction aim at addressing this problem of effective threshold selection for activation functions. Multilevel activation functions resort to fixed and uniform thresholding mechanisms. These functions assume homogeneity of the image information content. In this paper, we propose a collection of adaptive thresholding approaches to multilevel activation functions. The proposed thresholding mechanisms incorporate the image context information in the thresholding process. Applications of these mechanisms are demonstrated on the segmentation of real life multilevel intensity images using a self-supervised multilayer self-organizing neural network (MLSONN) and a supervised pyramidal neural network (PyraNet). We also present a bi-directional self-organizing neural network (BDSONN) architecture suitable for multilevel image segmentation. The architecture uses an embedded adaptive thresholding mechanism to a characteristic multilevel activation function. The segmentation efficiencies of the thresholding mechanisms evaluated using four unsupervised measures of merit, are reported for the three neural network architectures considered.

Analysis of dental plaque by using cellular neural network-based image segmentation

To establish and evaluate a new method for measurement of dental plaque by using cellular neural network-based image segmentation. A total of 195 subjects were selected from community population. After dental plaque staining, oral digital picture of anterior teeth area was taken by an Olympus digital camera (C-7070 Wide Zoom). At the same time, the Turesky dental plaque indices of anterior teeth were evaluated. The image analysis was conducted by cellular neural network-based image segmentation. The image cutting errors between two operators were very small. The Kappa value is 0.935. Pearson's correlation coefficient is 0.988 (P < 0.001). There was high correlative consistency between traditional dental plaque index and plaque percentage obtained by using image analysis. Pearson's correlation coefficient was 0.853 (P < 0.001). Cellular neural network-based image segmentation is a new method feasible for evaluating dental plaque.