Image Segmentation Technology Research Articles

Adaptive neighborhood triplet loss: enhanced segmentation of dermoscopy datasets by mining pixel information.

The integration of deep learning in image segmentation technology markedly improves the automation capabilities of medical diagnostic systems, reducing the dependence on the clinical expertise of medical professionals. However, the accuracy of image segmentation is still impacted by various interference factors encountered during image acquisition. To address this challenge, this paper proposes a loss function designed to mine specific pixel information which dynamically changes during training process. Based on the triplet concept, this dynamic change is leveraged to drive the predicted boundaries of images closer to the real boundaries. Extensive experiments on the PH2 and ISIC2017 dermoscopy datasets validate that our proposed loss function overcomes the limitations of traditional triplet loss methods in image segmentation applications. This loss function not only enhances Jaccard indices of neural networks by 2.42 and 2.21 for PH2 and ISIC2017, respectively, but also neural networks utilizing this loss function generally surpass those that do not in terms of segmentation performance. This work proposed a loss function that mined the information of specific pixels deeply without incurring additional training costs, significantly improving the automation of neural networks in image segmentation tasks. This loss function adapts to dermoscopic images of varying qualities and demonstrates higher effectiveness and robustness compared to other boundary loss functions, making it suitable for image segmentation tasks across various neural networks.

Lightweight multi-scale dynamic selection network for medical image segmentation

Medical image segmentation technology can assist doctors in accurately diagnosing and treating diseases. However, the diversity of segmented targets in scale and shape, as well as the complexity of the background environment, pose challenges for existing segmentation methods. In this context, we propose two effective deep neural networks, the multi-scale dynamic selection network (MDSNet) and its lightweight version MDSNet-Light for medical image segmentation. In MDSNet, we design a multi-scale dynamic selection module to capture multi-scale contextual information and dynamically adjust the acceptance domain of the feature map to achieve feature fusion at the optimal scale. We introduce PixelShuffle and PixelUnshuffle as sampling methods to effectively alleviate information loss issues for small-scale objects caused by pooling. Experimental results show that MDSNet outperforms eight existing superior neural networks in the test data of the publicly available dataset LIDC-IDRI on four evaluation indicators. And MDSNet-Light achieves a good balance between network performance and computational complexity, providing an option for applications in limited computing resource scenarios.

Review of Semi-Supervised Medical Image Segmentation based on the U-Net

Medical image segmentation is a core task in the field of medical image processing. It aims to separate areas of interest such as organs, tissues, and lesions from the background in medical images to facilitate further analysis, diagnosis, and treatment planning. Accurate image segmentation is crucial for improving the accuracy of disease diagnosis, assessing disease progression, and developing personalized treatment plans. However, fully supervised segmentation methods face the challenge of high annotation costs. With the emergence of the U-Net architecture, semi-supervised medical image segmentation based on U-Net is receiving increasing attention. This article reviews semi-supervised segmentation methods, the design concept and structure of the U-Net network, how it has been extended, and its application in semi-supervised medical image segmentation. The article also identifies the challenges faced by semi-supervised medical image segmentation techniques based on U-Net and speculates on possible future research directions. In conclusion, the article summarizes the potential of semi-supervised medical image segmentation technology based on U-Net as an accurate and efficient tool for medical diagnosis and treatment.

Image Segmentation Technology Based on Ant Colony Algorithm

Image segmentation is a key task in computer vision, with applications ranging from medical diagnosis to autonomous driving. The Ant Colony Algorithm (ACO), modeled after ant foraging behavior, has emerged as a viable segmentation methodology. However, ACO-based segmentation algorithms frequently generate segmented outputs with jagged or uneven boundaries, which reduces their interpretability and usability. To alleviate this problem, they study the use of boundary-smoothing approaches in ACO-based segmentation. In this paper, they investigate image segmentation technology based on the Ant Colony Algorithm, with a focus on border smoothing. They examine the fundamentals of ACO and its application to image segmentation, emphasizing its strengths and limits. They also look at several boundary smoothing strategies, such as morphological operations, edge-preserving filters, and active contours (snakes), and how they affect segmentation performance. Through experimental validation and comparative analysis, they show that boundary smoothing improves the accuracy and visual quality of segmented images produced by ACO-based segmentation algorithms. These results help to design more robust and visually appealing segmentation algorithms, which have potential applications in medical imaging, remote sensing, and industrial automation.

Application of Image Segmentation Algorithms in Computer Vision

In the field of computer vision (CV), image segmentation technology, as a fundamental part, has a crucial impact on the accuracy of subsequent image processing tasks. Image segmentation is not only a crucial transitional step from image processing to image analysis, but also a hot and difficult research topic in the field of CV. Although significant progress has been made in the research of image segmentation algorithms, existing segmentation algorithms may still face challenges in certain specific scenarios due to the complexity and diversity of images, making it difficult to achieve ideal segmentation results. In recent years, the rapid development of deep learning (DL) technology has brought new breakthroughs to the field of image segmentation. DL models, especially Convolutional Neural Networks (CNNs), can capture semantic information of images more accurately by automatically learning feature representations in images, thereby achieving more precise image segmentation. This article delves into the research and application of image segmentation algorithms in CV, with a focus on the application of DL in the field of image segmentation. With the continuous development of advanced technologies such as DL, it is believed that image segmentation technology will play a greater role in more fields in the future.

Segmentation analysis of UAV images based on Unet deep learning algorithm

The continuous development of UAV technology provides us with more and higher quality data, in which the application of UAV image segmentation technology can help us better understand and process these data. Traditional image segmentation methods can no longer meet the needs of UAV image segmentation, so researchers have begun to explore the application of deep learning methods in UAV image segmentation.U-Net, as a classical deep learning model, is also widely used in UAV image segmentation.U-Net is characterized by two parts: encoder and decoder, which are used to extract the image features, and decoder is used to map the features back to the original image size. features to map back to the original image size. UAV image segmentation technology can be applied in agriculture, urban planning, environmental monitoring and other fields. In the field of agriculture, UAV image segmentation can help farmers better manage and monitor farmland to improve crop yield and quality. In the field of urban planning, UAV image segmentation can help urban planners better understand the development status of the city and provide a more scientific basis for urban planning. In the field of environmental monitoring, UAV image segmentation can help us better monitor the changes in the natural environment and provide more effective means for environmental protection. By setting the ratio of training set, validation set and test set as 6:2:2 and performing 100 rounds of training, the U-Net model shows good results in UAV image segmentation. The loss of the model gradually stabilizes at 0.2979 and the accuracy gradually converges to 90.34%. The test results show that the prediction results are very close to the true mask, indicating that the U-Net model can segment UAV images well. The application of UAV image segmentation technology can help us better understand and process the data acquired by UAV and provide more information and basis. In the future, with the continuous development of UAV technology, UAV image segmentation technology will also be more widely used. Through the application of UAV image segmentation technology, we will better understand and protect our environment, manage our farmland more efficiently, and plan our cities more scientifically.

Research on medical image segmentation technology based on deep learning

With economy expanding quickly, people's demand for medical services has become higher and higher, and medical image as an important basis for medical diagnosis has naturally received widespread attention. However, traditional image segmentation methods are easily affected by noise and unable to meet the complex and changing practical clinical applications. The increasing utilization of deep learning technology enables effective resolution of these problems. In this paper, we will first introduce the traditional image segmentation techniques, and describe the main methods to realize traditional image segmentation and its limitations. Immediately after that, it is proposed that deep learning methods can solve the challenges of medical image segmentation with traditional methods, and then the structure, algorithms and applications of several of the most commonly used deep learning methods are introduced. This paper proposes that medical image segmentation based on deep learning can segment the image more robustly and with high accuracy, and it can automatically obtain the most suitable features. The research in this paper will be of great value to the research and application of medical image segmentation technology based on deep learning.

Fire in Focus: Advancing Wildfire Image Segmentation by Focusing on Fire Edges

With the intensification of global climate change and the frequent occurrence of forest fires, the development of efficient and precise forest fire monitoring and image segmentation technologies has become increasingly important. In dealing with challenges such as the irregular shapes, sizes, and blurred boundaries of flames and smoke, traditional convolutional neural networks (CNNs) face limitations in forest fire image segmentation, including flame edge recognition, class imbalance issues, and adapting to complex scenarios. This study aims to enhance the accuracy and efficiency of flame recognition in forest fire images by introducing a backbone network based on the Swin Transformer and combined with an adaptive multi-scale attention mechanism and focal loss function. By utilizing a rich and diverse pre-training dataset, our model can more effectively capture and understand key features of forest fire images. Through experimentation, our model achieved an intersection over union (IoU) of 86.73% and a precision of 91.23%. This indicates that the performance of our proposed wildfire segmentation model has been effectively enhanced. A series of ablation experiments validate the importance of these technological improvements in enhancing model performance. The results show that our approach achieves significant performance improvements in forest fire image segmentation tasks compared to traditional models. The Swin Transformer provides more refined feature extraction capabilities, the adaptive multi-scale attention mechanism helps the model focus better on key areas, and the focal loss function effectively addresses the issue of class imbalance. These innovations make the model more precise and robust in handling forest fire image segmentation tasks, providing strong technical support for future forest fire monitoring and prevention.

SLTM Network: Efficient Application of Lightweight Image Segmentation Technology in Detecting Drivable Areas for Unmanned Line-Marking Machines.

SLTM Network: Efficient Application of Lightweight Image Segmentation Technology in Detecting Drivable Areas for Unmanned Line-Marking Machines.

Medical Image Segmentation Based on TransUnet and A2B

The objective of medical picture segmentation technology is to accurately delineate and partition regions affected by pathology, which is of great significance to medical diagnosis and treatment. Thanks to deep learning's quick development, prior efforts mainly utilize the convolutional neural networks equipped with powerful feature representation capability. In this paper, to make the segmentation performance a petter appearance, we introduce a new model in medical image segmentation technology, TransUNet, which combines Transformer and U-Net and uses labeled and unlabeled images for training. To enhance the feature quality and boost the accuracy and robustness of the segmentation results, we further introduce the A2B (Attention in Attention Block) attention mechanism in the TransUNet backbone network. At the same time, we use the CNN-Transformer hybrid model and conduct ablation experiments to verify the improvement effect of the performance indicators. The empirical findings show that TransUNet has accomplished success in tasks requiring medical picture segmentation and can help doctors and researchers more accurately identify and locate key structures.

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.

Application of deep learning in medical imaging segmentation

The increasing demand for segmentation of lesions in medical images necessitates research on automatic segmentation. Manual segmentation is inefficient due to training time and energy constraints. Deep learning-based image segmentation technology can improve efficiency and aid in diagnosing conditions. This technology provides accurate and detailed data support for clinical medicine, making it a crucial field in medical image processing. This essay introduces image segmentation and its classification, and explains the differences between two segmentation methods, semantic segmentation and instance segmentation and their respective application fields. Additionally, it introduces several well-known deep neural networks for segmenting medical images using deep learning. Regarding the models, this article introduces the structure and characteristics of each model as well as their respective advantages and disadvantages. This essay also introduces examples of deep learning using different deep neural network models to segment specific medical images, including image segmentation based on FCN for the heart and U-Net for the kidneys.

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 Rust Extraction and Evaluation Method for Navigation Buoys Based on Improved U-Net and Hue, Saturation, and Value.

Abnormalities of navigation buoys include tilting, rusting, breaking, etc. Realizing automatic extraction and evaluation of rust on buoys is of great significance for maritime supervision. Severe rust may cause damage to the buoy itself. Therefore, a lightweight method based on machine vision is proposed for extracting and evaluating the rust of the buoy. The method integrates image segmentation and processing. Firstly, image segmentation technology is used to extract the metal part of the buoy based on an improved U-Net. Secondly, the RGB image is converted into an HSV image by preprocessing, and the transformation law of HSV channel color value is analyzed to obtain the best segmentation threshold and then the pixels of the rusted and the metal parts can be extracted. Finally, the rust ratio of the buoy is calculated to evaluate the rust level of the buoy. Results show that both the segmentation precision and recall are above 0.95, and the accuracy is nearly 1.00. Compared with the rust evaluation algorithm directly using the image processing method, the accuracy and processing speed of rust grade evaluation are greatly improved.

Forming process prediction of a self-piercing riveted joint in carbon fibre reinforced composites and aluminium alloy based on deep learning

As a mechanical internal locking structure, the forming process of a self-piercing riveted (SPR) joint is currently mainly investigated through riveting interruption experiments and finite element simulation. To solve the problems of complex simulation modelling processes, high experimental costs, and low efficiency, an SPR process prediction method based on deep learning was proposed to predict the deformation process and damage of SPR joints in carbon fibre-reinforced composites and aluminium alloys. The original images of the model dataset were obtained based on the simulation results, and image segmentation technology was used to classify the cross-section and damage morphology of the joint. A deep learning model based on a convolutional neural network and conditional generation antagonism model architecture was established, and the section shape and damage morphology of the joint were predicted by inputting the percentage of punch displacement. The k-fold cross validation method was used for model training, and the untrained data were used as the test set to verify the predictive ability of the model by comparing the forming section parameters of the SPR joint. The results show that the deep learning model used can accurately predict the deformation state and damage evolution of riveted materials at different joining stages. The average prediction accuracies of the height of the riveted head, residual thickness, and rivet spread are 95.80 %, 95.68 %, and 92.40 %, respectively.

Simplified expression and recursive algorithm of multi-threshold Tsallis entropy

Image segmentation is an important step in obtaining image information, and it has always been an extremely critical link in the fields of computer vision and pattern recognition. Because of its simplicity and effectiveness, threshold-based image segmentation technology has received significant attention and research by researchers. Tsallis entropy and Renyi entropy are two important global threshold selection methods in image thresholding. They not only have a special correspondence in mathematical expression, but also have a certain relationship in the field of image segmentation. Considering the number of thresholds increases, the complexity of the expression of Tsallis entropy criterion function also increases sharply, which is difficult to apply in multiple thresholds. Given this, this paper proposes a simplified expression of Tsallis entropy with multiple thresholds, which is proved by mathematical induction. Simultaneously, it is concluded that the multiple-threshold segmentation of Tsallis entropy and Renyi entropy with the same parameters is equivalent. To overcome the inconvenience of high calculation cost under multi-threshold and to improve calculation efficiency, recursive algorithms based on simplified Tsallis entropy and Renyi entropy under multi-threshold are given. The calculation costs of the traditional Tsallis entropy and Renyi entropy threshold, simplified Tsallis entropy and Renyi entropy threshold, and the recursive simplified Tsallis entropy and Renyi entropy are compared through experiments. The results have revealed that the calculation time of simplified Tsallis entropy and Renyi entropy is shorter than the traditional methods, and the calculation time of the recursive simplified Tsallis entropy and Renyi entropy is much lower than that of the simplified expression. Finally, we use the three optimization algorithms including particle swarm, differential evolution and Harris hawks algorithm to solve the optimal solutions of the six expressions in the case of multilevel thresholds. Experiments also show that recursive simplified Tsallis entropy and Renyi entropy have the lowest calculation time cost under the same parameters.

Image Segmentation in Aerial Imagery: A Review

The problem of distinguishing objects has plagued researchers for many years because of low accuracy compared to human eyes’ capability. In the last decade, the use of Machine Learning in aerial imagery data processing has multiplied, with the technology behind it has also developed exponentially. One of those technologies is image-based object identification, which relies heavily upon data computation. To reduce the computational load, various data segmentation algorithm was developed. This study is focused on reviewing the various image segmentation technology in aerial imagery for image recognition. Literature from as far as 1981 from various journals and conferences worldwide was reviewed. This review examines specific research questions to analyze image segmentation research over time and the challenges researchers face with each method. Machine Learning has gained popularity among segmentation methods. However, Deep Learning has been aggressively put an essential role in it by overcoming many of its weaknesses. The advanced algorithm used in Deep Learning to process the segmentation may drive more efficient and accurate data processing.

Hepatobiliary surgery based on intelligent image segmentation technology.

Liver disease is an important disease that seriously threatens human health. It accounts for the highest proportion in various malignant tumors, and its incidence rate and mortality are on the rise, seriously affecting human health. Modern imaging has developed rapidly, but the application of image segmentation in liver tumor surgery is still rare. The application of image processing technology represented by artificial intelligence (AI) in surgery can greatly improve the efficiency of surgery, reduce surgical complications, and reduce the cost of surgery. Hepatocellular carcinoma is the most common malignant tumor in the world, and its mortality is second only to lung cancer. The resection rate of liver cancer surgery is high, and it is a multidisciplinary surgery, so it is necessary to explore the possibility of effective switching between different disciplines. Resection of hepatobiliary and pancreatic tumors is one of the most challenging and lethal surgical procedures. The operation requires a high level of doctors' experience and understanding of anatomical structures. The surgical segmentation is slow and there may be obvious complications. Therefore, the surgical system needs to make full use of the relevant functions of AI technology and computer vision analysis software, and combine the processing strategy based on image processing algorithm and computer vision analysis model. Intelligent optimization algorithm, also known as modern heuristic algorithm, is an algorithm with global optimization performance, strong universality, and suitable for parallel processing. This algorithm generally has a strict theoretical basis, rather than relying solely on expert experience. In theory, the optimal solution or approximate optimal solution can be found in a certain time. This work studies the hepatobiliary surgery through intelligent image segmentation technology, and analyzes them through intelligent optimization algorithm. The research results showed that when other conditions were the same, there were three patients who had adverse reactions in hepatobiliary surgery through intelligent image segmentation technology, accounting for 10%. The number of patients with adverse reactions in hepatobiliary surgery by conventional methods was nine, accounting for 30%, which was significantly higher than the former, indicating a positive relationship between intelligent image segmentation technology and hepatobiliary surgery.

Medical Image Segmentation Research Status and Development Trends

As one of the important steps in medical image processing, medical image segmentation plays a pivotal role in clinical surgery and is widely used in application scenarios such as preoperative diagnosis, intraoperative navigation, and postoperative evaluation. In this paper, medical image segmentation technology is studied, and a variety of medical image segmentation methods are categorized and compared in an attempt to explore the development law of medical image segmentation technology. Firstly, the medical image segmentation technology is classified and studied according to its different methods, and this paper mainly researches and organizes the deep learning method for medical image segmentation; secondly, the principle, advantages and disadvantages, and applicable scenarios of each model are analyzed; and lastly, the latest progress of the medical image segmentation technology is objectively described. inherent deficiencies and solutions of the existing techniques, and provides a direction for continued improvement in the future. As one of the important steps in medical image processing, medical image segmentation plays a pivotal role in clinical surgery and is widely used in application scenarios such as preoperative diagnosis, intraoperative navigation, and postoperative evaluation. In this paper, we explore the critical role of medical image segmentation in medical image processing, particularly in clinical surgery and its various applications such as preoperative diagnosis, intraoperative navigation, and postoperative evaluation. Our study focuses on categorizing and comparing different medical image segmentation methods, with a special emphasis on deep learning techniques. We delve into the principles, advantages, disadvantages, and suitable scenarios for each model. Additionally, we objectively present the latest progress in medical image segmentation technology, addressing existing deficiencies and proposing potential solutions. This research provides valuable insights to foster continuous advancements in the field.

Retracted: Image Segmentation Technology Based on Attention Mechanism and ENet.

[This retracts the article DOI: 10.1155/2022/9873777.].