Traditional Image Segmentation Research Articles

A multiangle polarised imaging-based method for thin sectionsegmentation.

The most crucial task of petroleum geology is to explore oil and gas reservoirs in the deep underground. As one of the analysis techniques in petroleum geological research, rock thin sectionidentification method includes particle segmentation, which is one of the key steps. A conventional sandstone thin sectionimage typically contains hundreds of mineral particles with blurred boundaries and complex microstructures inside the particles. Moreover, the complex lithology and low porosity of tight sandstone make traditional image segmentation methods unsuitable for solving the complex thin sectionsegmentation problems. This paper combines petrology and image processing technologies. First, polarised sequence images are aligned, and then the images are transformed to the HSV colour space to extract pores. Second, particles are extracted according to their extinction characteristics. Last, a concavity and corner detection matching method is used to process the extracted particles, thereby completing the segmentation of sandstone thin sectionimages. The experimental results show that our proposed method can more accurately fit the boundaries of mineral particles in sandstone images than existing image segmentation methods. Additionally, when applied in actual production scenarios, our method exhibits excellent performance, greatly improving thin sectionidentification efficiency and significantly assisting experts inidentification.

Attention‐based prohibited item detection in X‐ray images during security checking

AbstractThis paper focuses on the intelligent detection of prohibited items in X‐ray images during the security checking process. An intelligent semantic segmentation model of prohibited items in X‐ray images is proposed based on the attention‐based object localization method. Based on the pre‐trained CNN classification framework, the attention mechanism can map the high‐layer semantic information of objects into the input space, while generating energy saliency maps to locate the prohibited items. In order to make the obtained attention maps discriminative, the lateral and contrastive inhibition strategies are introduced and combined together which can highlight the responses of activated neurons. Under the guidance of attention responses, two traditional image segmentation algorithms are employed to achieve the semantic segmentation results for the prohibited items detection in X‐ray images. The proposed semantic segmentation model relies on weakly supervised learning mechanism, and only depends on the category labels of prohibited items, which greatly avoids the work cost of data semantic annotation. The experimental results based on the public SIXray baseline and the self‐built X‐ray image database demonstrate the proposed method can achieve about 65% IoU localization precise averagely. In addition, comparison experiments were carried out with the state‐of‐the‐arts and ablation experiments to verify the effectiveness of the proposed model.

Design of image segmentation model based on residual connection and feature fusion.

With the development of deep learning technology, convolutional neural networks have made great progress in the field of image segmentation. However, for complex scenes and multi-scale target images, the existing technologies are still unable to achieve effective image segmentation. In view of this, an image segmentation model based on residual connection and feature fusion is proposed. The model makes comprehensive use of the deep feature extraction ability of residual connections and the multi-scale feature integration ability of feature fusion. In order to solve the problem of background complexity and information loss in traditional image segmentation, experiments were carried out on two publicly available data sets. The results showed that in the ISPRS Vaihingen dataset and the Caltech UCSD Birds200 dataset, when the model completed the 56th and 84th iterations, respectively, the average accuracy of FRes-MFDNN was the highest, which was 97.89% and 98.24%, respectively. In the ISPRS Vaihingen dataset and the Caltech UCSD Birds200 dataset, when the system model ran to 0.20s and 0.26s, the F1 value of the FRes-MFDNN method was the largest, and the F1 value approached 100% infinitely. The FRes-MFDNN segmented four images in the ISPRS Vaihingen dataset, and the segmentation accuracy of images 1, 2, 3 and 4 were 91.44%, 92.12%, 94.02% and 91.41%, respectively. In practical applications, the MSRF-Net method, LBN-AA-SPN method, ARG-Otsu method, and FRes-MFDNN were used to segment unlabeled bird images. The results showed that the FRes-MFDNN was more complete in details, and the overall effect was significantly better than the other three models. Meanwhile, in ordinary scene images, although there was a certain degree of noise and occlusion, the model still accurately recognized and segmented the main bird images. The results show that compared with the traditional model, after FRes-MFDNN segmentation, the completeness, detail, and spatial continuity of pixels have been significantly improved, making it more suitable for complex scenes.

Time-Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modeling.

The rate of soybean canopy establishment largely determines photoperiodic sensitivity, subsequently influencing yield potential. However, assessing the rate of soybean canopy development in large-scale field breeding trials is both laborious and time-consuming. High-throughput phenotyping methods based on unmanned aerial vehicle (UAV) systems can be used to monitor and quantitatively describe the development of soybean canopies for different genotypes. In this study, high-resolution and time-series raw data from field soybean populations were collected using UAVs. The RGB (red, green, and blue) and infrared images are used as inputs to construct the multimodal image segmentation model-the RGB & Infrared Feature Fusion Segmentation Network (RIFSeg-Net). Subsequently, the segment anything model was employed to extract complete individual leaves from the segmentation results obtained from RIFSeg-Net. These leaf aspect ratios facilitated the accurate categorization of soybean populations into 2 distinct varieties: oval leaf type variety and lanceolate leaf type variety. Finally, dynamic modeling was conducted to identify 5 phenotypic traits associated with the canopy development rate that differed substantially among the classified soybean varieties. The results showed that the developed multimodal image segmentation model RIFSeg-Net for extracting soybean canopy cover from UAV images outperformed traditional deep learning image segmentation networks (precision = 0.94, recall = 0.93, F1-score = 0.93). The proposed method has high practical value in the field of germplasm resource identification. This approach could lead to the use of a practical tool for further genotypic differentiation analysis and the selection of target genes.

MIS-Net: A deep learning-based multi-class segmentation model for CT images.

The accuracy of traditional CT image segmentation algorithms is hindered by issues such as low contrast and high noise in the images. While numerous scholars have introduced deep learning-based CT image segmentation algorithms, they still face challenges, particularly in achieving high edge accuracy and addressing pixel classification errors. To tackle these issues, this study proposes the MIS-Net (Medical Images Segment Net) model, a deep learning-based approach. The MIS-Net model incorporates multi-scale atrous convolution into the encoding and decoding structure with symmetry, enabling the comprehensive extraction of multi-scale features from CT images. This enhancement aims to improve the accuracy of lung and liver edge segmentation. In the evaluation using the COVID-19 CT Lung and Infection Segmentation dataset, the left and right lung segmentation results demonstrate that MIS-Net achieves a Dice Similarity Coefficient (DSC) of 97.61. Similarly, in the Liver Tumor Segmentation Challenge 2017 public dataset, the DSC of MIS-Net reaches 98.78.

Pancreatic cancer pathology image segmentation with channel and spatial long-range dependencies

Based on deep learning, pancreatic cancer pathology image segmentation technology effectively assists pathologists in achieving improved treatment outcomes. However, compared to traditional image segmentation tasks, the large size of tissues in pathology images requires a larger receptive field. While methods based on dilated convolutions or attention mechanisms can enhance the receptive field, they cannot capture long-range feature dependencies. Directly applying self-attention mechanisms to capture long-range dependencies results in intolerable computational complexity. To address these challenges, we introduce a channel and spatial self-attention (CS) Module designed for efficiently capturing both channel and spatial long-range feature dependencies in pancreatic cancer pathological images. Specifically, the channel and spatial self-attention module consists of an adaptive channel self-attention module and a window-shift spatial self-attention module. The adaptive channel self-attention module adaptively pools features to a fixed size to capture long-range feature dependencies. While the window-shift spatial self-attention module captures spatial long-range dependencies in a window-based manner. Additionally, we propose a re-weighted cross-entropy loss to mitigate the impact of long-tail distribution on performance. Our proposed method surpasses state-of-the-art on both our Pancreatic Cancer Pathology Image (PCPI) dataset and the GlaS challenge dataset. The mDice and mIoU have achieved 73.93% and 59.42% in our PCPI dataset.

Multidimensional characterization of particle morphology and mineralogical composition using CT data and R-vine copulas

Computed tomography (CT) can capture volumes large enough to measure a statistically meaningful number of micron-sized particles with a sufficiently good resolution to allow for the analysis of individual particles. However, the development of methods to efficiently investigate such image data and interpretably model the observed particle features is still an active field of research. When image data of particles exhibiting a wide range of shapes and sizes is considered, traditional image segmentation methods, such as the classic watershed algorithm, struggle to recognize particles with satisfying accuracy. Thus, more advanced methods of machine learning must be utilized for image segmentation to improve the validity of subsequent analyses. Moreover, CT data does not include information about the mineralogical composition of particles and, therefore, additional SEM-EDS image data has to be acquired. In this paper, micro-CT image data of a particle system mostly consisting of zinnwaldite–quartz composites is considered. First, an image segmentation method is applied which uses deep convolutional neural networks, in particular an adaptation of the U-net architecture. This has the advantage of requiring less hand-labeling than other machine learning methods, while also being more flexible with the possibility of transfer learning. Then, fully parameterized models based on vine copulas are designed to determine multivariate probability distributions of descriptor vectors for the size, shape, texture and composition of particles—allowing for the estimation and interpretable characterization of interdependencies between particle descriptors. For model fitting, the segmented three-dimensional CT data and co-registered two-dimensional SEM-EDS data are used. The models are applied to predict the mineralogical composition of particles, solely on the basis of particle descriptors observed in CT data.

Automatic road crack detection and analysis system based on deep feature fusion and edge structure extraction

ABSTRACT In order to solve the problem that the traditional crack image segmentation method cannot accurately extract the edge structure of road cracks, a flexible crack detection and identification system are provided. Firstly, a road crack dataset is built by processing the acquired images using the sliding window technique. A model called R2-AENet is proposed for training and testing. The model structure uses the recurrent residual convolutional neural network (RRCNN) to improve the convolutional structure and drive the accumulation of fracture features. The model decoder combines the attention-guided filtering module and the edge residuals module (ERB) better to extract the edge features of the feature map. As a result, it improves the crack segmentation performance. Finally, the research results are validated on the upgraded software platform. The experimental results demonstrate that the R2-AENet model achieves better segmentation results compared to the popular deep learning algorithm models. Evidently, the model achieves accuracy and recall of 0.982 and 0.849 on the collected dataset. Besides, the area under the curve (AUC) metric value can reach 0.992. In addition, the detection results in different data sets perform well, verifying that the model has exceptional robustness and can be applied to various environmental road inspection and maintenance projects.

Clustering and Segmentation of Adhesive Pests in Apple Orchards Based on GMM-DC

The segmentation of individual pests is a prerequisite for pest feature extraction and identification. To address the issue of pest adhesion in the apple orchard pest identification process, this research proposed a pest adhesion image segmentation method based on Gaussian Mixture Model with Density and Curvature Weighting (GMM-DC). First, in the HSV color space, an image was desaturated by adjusting the hue and inverting to mitigate threshold crossing points. Subsequently, threshold segmentation and contour selection methods were used to separate the image background. Next, a shape factor was introduced to determine the regions and quantities of adhering pests, thereby determining the number of model clustering clusters. Then, point cloud reconstruction was performed based on the color and spatial distribution features of the pests. To construct the GMM-DC segmentation model, a spatial density (SD) and spatial curvature (SC) information function were designed and embedded in the GMM. Finally, experimental analysis was conducted on the collected apple orchard pest images. The results showed that GMM-DC achieved an average accurate segmentation rate of 95.75%, an average over-segmentation rate of 2.83%, and an average under-segmentation rate of 1.42%. These results significantly outperformed traditional image segmentation methods. In addition, the original and improved Mask R-CNN models were used as recognition models, and the mean Average Precision was used as the evaluation metric. Recognition experiments were conducted on pest images with and without the proposed method. The results show the mean Average Precision for pest images segmented with the proposed method as 92.43% and 96.75%. This indicates an improvement of 13.01% and 12.18% in average recognition accuracy, respectively. The experimental results demonstrate that this method provides a theoretical and methodological foundation for accurate pest identification in orchards.

Deep Learning-Based Segmentation of Intertwined Fruit Trees for Agricultural Tasks

Fruit trees in orchards are typically placed at equal distances in rows; therefore, their branches are intertwined. The precise segmentation of a target tree in this situation is very important for many agricultural tasks, such as yield estimation, phenotyping, spraying, and pruning. However, our survey on tree segmentation revealed that no study has explicitly addressed this intertwining situation. This paper presents a novel dataset in which a precise tree region is labeled carefully by a human annotator by delineating the branches and trunk of a target apple tree. Because traditional rule-based image segmentation methods neglect semantic considerations, we employed cutting-edge deep learning models. Five recently pre-trained deep learning models for segmentation were modified to suit tree segmentation and were fine-tuned using our dataset. The experimental results show that YOLOv8 produces the best average precision (AP), 93.7 box AP@0.5:0.95 and 84.2 mask AP@0.5:0.95. We believe that our model can be successfully applied to various agricultural tasks.

Research on Traditional Image Segmentation Method Based on Oil Drilling Pipe Defects

This study explores the feasibility and efficacy of conventional image seg-mentation technology in diagnosing failures in oil drilling pipe images. Simultaneously, it envisions an intelligent approach to diagnose defects in oil drilling pipes. The present paper examines and scrutinizes traditional image segmentation methods in light of the characteristics of oil drilling pipe defect images. It devises experiments tailored for these defect images and employs various traditional image segmentation methods to facilitate comparison and evaluation. The experimental findings illustrate that the traditional image segmentation methods possess a discernible impact on detecting defects in oil drilling pipes, with the image segmentation effect based on the Canny operator method of edge detection proving to be the most effective. The experiments are specifically devised for defective images of oil drilling pipes, utilizing diverse traditional image segmentation methods for comparison and evaluation. The experimental results demonstrate that the traditional image segmentation methods exhibit a certain degree of efficacy in detecting defects in oil drilling pipes, with the image segmentation effect based on the Canny operator method of edge detection being the most optimal.

A simplified U-net model for Covid-19 CT image segmentation

The ongoing COVID-19 pandemic has highlighted the importance of accurate and efficient medical image analysis to aid in the diagnosis and treatment of patients. In particular, the segmentation of COVID-19 medical images has become a critical task to identify regions of interest, such as the infected lung areas, and to track disease progression. Traditional image segmentation methods have been widely used in medical image analysis. However, these methods are often challenged by the complex and diverse nature of COVID-19 images, as well as the limited availability of data. In this paper, we propose a simplified version of the U-Net that eliminates redundant crop operations. This simplification reduces computational complexity and memory usage, and enables the model to learn from larger input images, resulting in better performance. We evaluate the performance of our simplified U-Net model on a public COVID-19 dataset and demonstrate that our model achieves state-of-the-art results while using fewer computational resources.

Metal Abrasive Image Segmentation Algorithm Based on K-means Clustering

Metal abrasive image segmentation is one of the important image processing tasks in the industrial field. However, due to the complex color and texture characteristics of metal abrasive images, as well as difficult factors such as noise and lighting changes, traditional image segmentation methods often fail to achieve high accuracy and stability. In order to solve this problem, a metal abrasive image segmentation algorithm based on K-means clustering is proposed. The algorithm applies the K-means clustering algorithm to the image segmentation of metal abrasive particles, and realizes the separation of metal abrasive grains from the background by clustering the color features of image pixels. Experimental results show that our algorithm shows good accuracy and stability in the metal abrasive image segmentation task, and has high efficiency. Therefore, the algorithm provides an accurate and efficient method for metal abrasive image segmentation.

Study on segmentation of blasting fragment images from open-pit mine based on U-CARFnet.

Bench blasting is the primary means of production in open-pit metal mines. The size of the resulting rock mass after blasting has a significant impact on production cost. Currently, the ore fragment size is obtained mainly through manual measurement or estimation with the naked eye, which is inefficient and inaccurate. This study proposes the U-CARFnet and U-Net models for segmenting blasting fragment images from open-pit mines based on an attention mechanism, residual learning module, and focal loss function. It compares this technique with traditional image segmentation ones and a variety of deep learning models to verify the efficacy of the proposed model. Experimental results show that the accuracy of the U-CARFnet model proposed in this paper reaches 97.11% in the performance evaluation, which shows better performance than the traditional image segmentation method. In this study, the U-CARFnet model is used in the application, and a superior performance is obtained, with an average segmentation error of 5.46%. The proposed approach provides an effective technique for statistically analyzing images of mine rock.

Levy Flight and Chaos Theory-Based Gravitational Search Algorithm for Image Segmentation

Image segmentation is one of the pivotal steps in image processing due to its enormous application potential in medical image analysis, data mining, and pattern recognition. In fact, image segmentation is the process of splitting an image into multiple parts in order to provide detailed information on different aspects of the image. Traditional image segmentation techniques suffer from local minima and premature convergence issues when exploring complex search spaces. Additionally, these techniques also take considerable runtime to find the optimal pixels as the threshold levels are increased. Therefore, in order to overcome the computational overhead and convergence problems of the multilevel thresholding process, a robust optimizer, namely the Levy flight and Chaos theory-based Gravitational Search Algorithm (LCGSA), is employed to perform the segmentation of the COVID-19 chest CT scan images. In LCGSA, exploration is carried out by Levy flight, while chaotic maps guarantee the exploitation of the search space. Meanwhile, Kapur’s entropy method is utilized for segmenting the image into various regions based on the pixel intensity values. To investigate the segmentation performance of ten chaotic versions of LCGSA, firstly, several benchmark images from the USC-SIPI database are considered for the numerical analysis. Secondly, the applicability of LCGSA for solving real-world image processing problems is examined by using various COVID-19 chest CT scan imaging datasets from the Kaggle database. Further, an ablation study is carried out on different chest CT scan images by considering ground truth images. Moreover, various qualitative and quantitative metrics are used for the performance evaluation. The overall analysis of the experimental results indicated the efficient performance of LCGSA over other peer algorithms in terms of taking less computational time and providing optimal values for image quality metrics.

Unsupervised leaf segmentation in complex backgrounds using mutual information minimization

Leaf segmentation is crucial for plant recognition, especially for tree species identification. In natural environments, leaf segmentation can be very challenging due to the lack of prior information about leaves and the variability of backgrounds. In typical applications, supervised algorithms often require pixel-level annotation of regions, which can be labour-intensive and limited to identifying plant species using pre-labelled samples. On the other hand, traditional unsupervised image segmentation algorithms require specialised parameter tuning for leaf images to achieve optimal results. Therefore, this paper proposes an unsupervised leaf segmentation method that combines mutual information with neural networks to better generalise to unknown samples and adapt to variations in leaf shape and appearance to distinguish and identify different tree species. First, a model combining a Variational Autoencoder (VAE) and a segmentation network is used as a pre-segmenter to obtain dynamic masks. Secondly, the dynamic masks are combined with the segmentation masks generated by the mask generator module to construct the initial mask. Then, the patcher module uses the Mutual Information Minimum (MIM) loss as an optimisation objective to reconstruct independent regions based on this initial mask. The process of obtaining dynamic masks through pre-segmentation is unsupervised, and the entire experimental process does not involve any label information. The experimental method was performed on tree leaf images with a naturally complex background using the publicly available Pl@ntLeaves dataset. The results of the experiment showed that compared to existing excellent methods on this dataset, the IoU (Intersection over Union) index increased by 3.9%.

Bone tumor examination based on FCNN-4s and CRF fine segmentation fusion algorithm

Background and objectiveBone tumor is a kind of harmful orthopedic disease, there are benign and malignant points. Aiming at the problem that the accuracy of the existing machine learning algorithm for bone tumor image segmentation is not high, a bone tumor image segmentation algorithm based on improved full convolutional neural network which consists fully convolutional neural network (FCNN-4s) and conditional random field (CRF). MethodologyThe improved fully convolutional neural network (FCNN-4s) was used to perform coarse segmentation on preprocessed images. Batch normalization layers were added after each convolutional layer to accelerate the convergence speed of network training and improve the accuracy of the trained model. Then, a fully connected conditional random field (CRF) was fused to refine the bone tumor boundary in the coarse segmentation results, achieving the fine segmentation effect. ResultsThe experimental results show that compared with the traditional convolutional neural network bone tumor image segmentation algorithm, the algorithm has a great improvement in segmentation accuracy and stability, the average Dice can reach 91.56%, the real-time performance is better. ConclusionCompared with the traditional convolutional neural network segmentation algorithm, the algorithm in this paper has a more refined structure, which can effectively solve the problem of over-segmentation and under-segmentation of bone tumors. The segmentation prediction has better real-time performance, strong stability, and can achieve higher segmentation accuracy.

RSLC-Deeplab: A Ground Object Classification Method for High-Resolution Remote Sensing Images

With the continuous advancement of remote sensing technology, the semantic segmentation of different ground objects in remote sensing images has become an active research topic. For complex and diverse remote sensing imagery, deep learning methods have the ability to automatically discern features from image data and capture intricate spatial dependencies, thus outperforming traditional image segmentation methods. To address the problems of low segmentation accuracy in remote sensing image semantic segmentation, this paper proposes a new remote sensing image semantic segmentation network, RSLC-Deeplab, based on DeeplabV3+. Firstly, ResNet-50 is used as the backbone feature extraction network, which can extract deep semantic information more effectively and improve the segmentation accuracy. Secondly, the coordinate attention (CA) mechanism is introduced into the model to improve the feature representation generated by the network by embedding position information into the channel attention mechanism, effectively capturing the relationship between position information and channels. Finally, a multi-level feature fusion (MFF) module based on asymmetric convolution is proposed, which captures and refines low-level spatial features using asymmetric convolution and then fuses them with high-level abstract features to mitigate the influence of background noise and restore the lost detailed information in deep features. The experimental results on the WHDLD dataset show that the mean intersection over union (mIoU) of RSLC-Deeplab reached 72.63%, the pixel accuracy (PA) reached 83.49%, and the mean pixel accuracy (mPA) reached 83.72%. Compared to the original DeeplabV3+, the proposed method achieved a 4.13% improvement in mIoU and outperformed the PSP-NET, U-NET, MACU-NET, and DeeplabV3+ networks.

Improved Sea Ice Image Segmentation Using U2-Net and Dataset Augmentation

Sea ice extraction and segmentation of remote sensing images is the basis for sea ice monitoring. Traditional image segmentation methods rely on manual sampling and require complex feature extraction. Deep-learning-based semantic segmentation methods have the advantages of high efficiency, intelligence, and automation. Sea ice segmentation using deep learning methods faces the following problems: in terms of datasets, the high cost of sea ice image label production leads to fewer datasets for sea ice segmentation; in terms of image quality, remote sensing image noise and severe weather conditions affect image quality, which affects the accuracy of sea ice extraction. To address the quantity and quality of the dataset, this study used multiple data augmentation methods for data expansion. To improve the semantic segmentation accuracy, the SC-U2-Net network was constructed using multiscale inflation convolution and a multilayer convolutional block attention module (CBAM) attention mechanism for the U2-Net network. The experiments showed that (1) data augmentation solved the problem of an insufficient number of training samples to a certain extent and improved the accuracy of image segmentation; (2) this study designed a multilevel Gaussian noise data augmentation scheme to improve the network’s ability to resist noise interference and achieve a more accurate segmentation of images with different degrees of noise pollution; (3) the inclusion of a multiscale inflation perceptron and multilayer CBAM attention mechanism improved the ability of U2-Net network feature extraction and enhanced the model accuracy and generalization ability.

Multi-Scale Orthogonal Model CNN–Transformer for Medical Image Segmentation

Because of the limitations of convolution kernel, the traditional image segmentation network is not sufficient to obtain the context information, but the image segmentation task is very dependent on the context information. Transformer’s linear input can just get enough context information. In this paper, we propose a transformer segmentation network hyperfusion transformer based on a pyramid structure. First, the model divides the single-scale coding form into several-different-scale coding forms, and then fuses the decoding results. Second, in order to ensure the specificity of the output characteristics of each branch, we orthogonalize the results of a variety of different scales. By orthogonalizing in pairs, we can ensure that the results obtained by different branches are not completely similar to a certain extent, and reduce the redundancy of branch information. On the two datasets, the method in this paper surpasses a variety of classical models under multiple evaluation indexes, confirming that it is an effective segmentation method.