Image Segmentation Algorithm Research Articles

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier.

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study's commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.

An enhanced chameleon swarm algorithm for global optimization and multi-level thresholding medical image segmentation

An enhanced chameleon swarm algorithm for global optimization and multi-level thresholding medical image segmentation

Correction: Boosted Aquila Arithmetic Optimization Algorithm for multi-level thresholding image segmentation

Correction: Boosted Aquila Arithmetic Optimization Algorithm for multi-level thresholding image segmentation

Automatic Flood Detection from Sentinel-1 Data Using a Nested UNet Model and a NASA Benchmark Dataset

During flood events near real-time, synthetic aperture radar (SAR) satellite imagery has proven to be an efficient management tool for disaster management authorities. However, one of the challenges is accurate classification and segmentation of flooded water. A common method of SAR-based flood mapping is binary segmentation by thresholding, but this method is limited due to the effects of backscatter, geographical area, and surface characterstics. Recent advancements in deep learning algorithms for image segmentation have demonstrated excellent potential for improving flood detection. In this paper, we present a deep learning approach with a nested UNet architecture based on a backbone of EfficientNet-B7 by leveraging a publicly available Sentinel‑1 dataset provided jointly by NASA and the IEEE GRSS Committee. The performance of the nested UNet model was compared with several other UNet-based convolutional neural network architectures. The models were trained on flood events from Nebraska and North Alabama in the USA, Bangladesh, and Florence, Italy. Finally, the generalization capacity of the trained nested UNet model was compared to the other architectures by testing on Sentinel‑1 data from flood events of varied geographical regions such as Spain, India, and Vietnam. The impact of using different polarization band combinations of input data on the segmentation capabilities of the nested UNet and other models is also evaluated using Shapley scores. The results of these experiments show that the UNet model architectures perform comparably to the UNet++ with EfficientNet-B7 backbone for both the NASA dataset as well as the other test cases. Therefore, it can be inferred that these models can be trained on certain flood events provided in the dataset and used for flood detection in other geographical areas, thus proving the transferability of these models. However, the effect of polarization still varies across different test cases from around the world in terms of performance; the model trained with the combinations of individual bands, VV and VH, and polarization ratios gives the best results.

Brain tumor image segmentation algorithm based on multimodal feature fusion of Bayesian weight distribution

AbstractThis study proposes an improved U‐Net model to address the issues of large semantic differences in skip connections and insufficient utilization of cross‐channel information in magnetic resonance imaging (MRI) images leading to inaccurate segmentation of brain tumor regions in the field of brain tumor segmentation. Firstly, by adding a deep residual module to alter the receptive field, the network's ability to learn tumor information is enhanced. Secondly, a dual attention mechanism was established using Bayesian weighting technology, achieving multi‐channel and multi‐scale feature fusion, and improving the model's learning and extraction of brain tumor boundary information. Finally, the tumor features extracted from different patterns are concatenated through skip connections, effectively integrating feature information from different levels and scales, and reducing semantic differences. We evaluated the performance of the proposed model on the BraTS2018 and BraTS2019 brain tumor image segmentation datasets. The experimental results showed that for the BraTS2018 dataset, the model improved the average dice score by 12.8%, the average sensitivity by 10.4%, and the average Hausdorff Distance by 5.75 compared to traditional U‐Net. On the BraTS2019 dataset, three indicators improved by 12.6%, 11.2%, and 7.46, respectively. The experimental results show that the proposed improved U‐Net model can improve the segmentation performance of brain tumor MRI images without increasing computational time.

MSA-UNet: A Multiscale Lightweight U-Net Lung CT Image Segmentation Algorithm Under Attention Mechanism

Automatic and precise segmentation of lung images can assist doctors in locating and diagnosing lung lesions. However, current traditional lung CT image lesion segmentation algorithms suffer from the problem of low segmentation accuracy, while deep learning-based segmentation algorithms struggle to strike a better balance between lightweight and high accuracy. In response to this issue, a multi-scale and lightweight U-Net lung image segmentation algorithm with an attention mechanism is proposed. This algorithm introduces CA convolution after the convolution in the encoding stage to extract channel relationships and positional information from the feature maps. Furthermore, the RFB module is employed to extract features from different perspectives. Lastly, upward residual connections are introduced between the RFB modules in the encoder and decoder to enhance inter-network information interaction. Experiments conducted on the LUNA (lung nodule analysis) dataset and the COVID-QU-Ex dataset for COVID-19 pneumonia demonstrate that the proposed MSA-UNet algorithm achieves the best results in terms of Precision and Dice metrics. It outperforms mainstream models such as U-Net++ and DeeplabV3+ in terms of segmentation effectiveness and segmentation generality. The model has a floating-point operation count (FLOPs) of 18.15 G, a network parameter counts of 8.83×106, and achieves a Precision of 99.37%. The algorithm achieves a good balance between computational efficiency, model size, and segmentation accuracy.

Computer Vision and Image Segmentation

Abstract: Image segmentation is a critical step in image processing, computer vision, and pattern recognition, which involves dividing an image into different regions or segments. Image segmentation plays an essential role in many applications, such as object recognition, medical image analysis, autonomous driving, and robotics. This paper aims to provide an overview of image segmentation techniques, including traditional and deep learning-based approaches. The paper also discusses the challenges associated with image segmentation, such as noise, illumination variations, and occlusions. Finally, the paper provides a brief discussion on the evaluation metrics used to assess the performance of image segmentation algorithms.

Pelvic bone tumor segmentation fusion algorithm based on fully convolutional neural network and conditional random field

Background and objectivePelvic bone tumors represent a harmful orthopedic condition, encompassing both benign and malignant forms. Addressing the issue of limited accuracy in current machine learning algorithms for bone tumor image segmentation, we have developed an enhanced bone tumor image segmentation algorithm. This algorithm is built upon an improved full convolutional neural network, incorporating both the fully convolutional neural network (FCNN-4s) and a conditional random field (CRF) to achieve more precise segmentation. MethodologyThe enhanced fully convolutional neural network (FCNN-4s) was employed to conduct initial segmentation on preprocessed images. Following each convolutional layer, batch normalization layers were introduced to expedite network training convergence and enhance the accuracy of the trained model. Subsequently, a fully connected conditional random field (CRF) was integrated to fine-tune the segmentation results, refining the boundaries of pelvic bone tumors and achieving high-quality segmentation. ResultsThe experimental outcomes demonstrate a significant enhancement in segmentation accuracy and stability when compared to the conventional convolutional neural network bone tumor image segmentation algorithm. The algorithm achieves an average Dice coefficient of 93.31 %, indicating superior performance in real-time operations. ConclusionIn contrast to the conventional convolutional neural network segmentation algorithm, the algorithm presented in this paper boasts a more intricate structure, proficiently addressing issues of over-segmentation and under-segmentation in pelvic bone tumor segmentation. This segmentation model exhibits superior real-time performance, robust stability, and is capable of achieving heightened segmentation accuracy.

A rapid evaluation method of blasting effect based on optimized image segmentation algorithm and application in engineering

To quickly determine the blasting block degree and conduct an accurate and objective analysis of the tunnel blasting effect, this study has enhanced and improved upon the traditional genetic algorithm and Otsu algorithm. It has combined it with the marking watershed method and utilized ground digital acquisition to capture images of blasting debris. These images are then used in our custom-developed blasting analysis software to calculate the blasting block degree distribution and provide a quantitative analysis of blasting block degree. The research results show that the optimized image segmentation algorithm effectively improves the traditional threshold segmentation method on the poor effect of segmentation of the edge of the adherent block or the direct application of the watershed segmentation of the over-segmentation problem, to improve the segmentation accuracy based on the new segmentation technology is close to the traditional technology in terms of time. Through the self-developed software, the construction personnel in the project site to quickly obtain the blasting block degree histogram, block degree cumulative curve and other important indicators of the evaluation of the effect of blasting block degree, to provide data support for on-site construction, to assist in the modification of the blasting program, and to improve the efficiency of construction. This study realizes the rapid detection and block identification of blasting blocks, provides data support for the optimization of blasting parameters, and has good application and promotion value.

Ensemble CART surrogate-assisted automatic multi-objective rough fuzzy clustering algorithm for unsupervised image segmentation

Multi-objective clustering algorithms (MOCAs) are popular in unsupervised image segmentation due to their merit of meeting multiple segmentation requirements and the prospect of automatically estimating the number of clusters. However, most of them suffer from high time costs and are easily to be influenced by the uncertainty when handling real complex images. To address these issues, we propose an ensemble classification and regression tree (CART) surrogate-assisted automatic multi-objective rough fuzzy clustering (ECS-AMRFC) algorithm for unsupervised image segmentation. Firstly, a cluster medoid-based encoding scheme is employed to represent solutions with different number of clusters and meanwhile lessen the length of encoding. Then, we design an ensemble CART as the surrogate model to significantly reduce the computational burden. Moreover, a surrogate model management strategy is proposed to accelerate the optimization and enhance the quality of surrogate modeling. To handle the uncertainty in data, we extend the rough fuzzy clustering into MOCAs and construct three complementary objective functions to seek proper cluster medoids from multiple perspectives. In addition, the Gaussian kernel is introduced into the objective functions to handle image pixels that cannot separate linearly in the feature space. Finally, a kernelized rough fuzzy clustering validity index is defined to automatically select the optimal solution with no requirements of any prior knowledge. Experiments show that ECS-AMRFC not only identifies appropriate number of clusters on different kinds of images, but also obtains better segmentation results than state-of-the-art rough fuzzy clustering algorithms and automatic MOCAs.

MCMC algorithm based on Markov random field in image segmentation.

In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies.

Self-equilibrium segmentation of near-infrared images of dental microcracks

The issue of missing teeth caused by dental microcracks has become a central topic in dental clinical medicine. Near-infrared technology combined with advanced image segmentation algorithms has become one of the leading technical routes for dental clinical medicine diagnosis. In this study, the NIR image segmentation method of dental microcracks was studied in depth to further utilize the advantages of near-infrared imaging technology in the detection technology of dental microcracks. The main contributions and research contents of this paper include (1) we designed a near-infrared imaging system for dental imaging, which provides solid data support for the study of advanced segmentation algorithms; (2) we designed a self-equilibrium segmentation algorithm for occult fracture of teeth based on U-Net++. The core module of this algorithm includes a multi-resolution residual encoder and a self-equilibrium composite loss function. The multi-scale residual encoder significantly improves the feature extraction capability of U-Net++. The self-equilibrium combined loss function ensures the consistency of the model’s attention to positive and negative samples, which enhances the robustness of U-Net++. (3) We have demonstrated through extensive experiments that our algorithm performs better than other dental occultation segmentation methods. This study is valuable for improving the diagnostic accuracy of dental occult fractures and promptly detecting the potential danger of the dental.

Betel Leaf Diseases Classification using Machine Learning Algorithm: A Feasible Approach

Even though the development in science and technology in various domains, agriculture is essential for humans to survive. It is mandatory to implement the development of science in the agriculture sector. There is a huge demand for agricultural workers because of lots of risk in production behind productivity. One of the major risks is diseases caused in plants. Our approach uses region of interest algorithms for image segmentation and feature extractions are done by GLCM and finally the diseases are classified with the help of ELM algorithm. These improvements in the classification of plant leaf diseases not only increase the precision and effectiveness of disease diagnosis, but also make it easier to accurately monitor vast fields of crops quickly. Due to its ability to precisely diagnose and categorize plant leaf diseases, the extreme learning machine technique is a novel strategy that has attracted a lot of attention. Additionally, this method has benefits over conventional image processing methods, which could find it difficult to categorize various plant leaf diseases. Research on betel leaf has received a lot of attention because of its possible medical benefits. Studies on its impact on dental health have been done because betel leaf extracts have antibacterial and anti-inflammatory properties. Approximately 1047 betel leaf images are captured to test our approach by taking five classes of diseases like Anthracnose, Phytophthora Foot Rot, Fusarium wilt, Bacterial Leaf Rot, Powdery Mildew. The model reports accuracy of 97%.

Boosted Aquila Arithmetic Optimization Algorithm for multi-level thresholding image segmentation

Boosted Aquila Arithmetic Optimization Algorithm for multi-level thresholding image segmentation

LM-DeeplabV3+: A Lightweight Image Segmentation Algorithm Based on Multi-Scale Feature Interaction

Street-view images can help us to better understand the city environment and its potential characteristics. With the development of computer vision and deep learning, the technology of semantic segmentation algorithms has become more mature. However, DeeplabV3+, which is commonly used in semantic segmentation, has shortcomings such as a large number of parameters, high requirements for computing resources, and easy loss of detailed information. Therefore, this paper proposes LM-DeeplabV3+, which aims to greatly reduce the parameters and computations of the model while ensuring segmentation accuracy. Firstly, the lightweight network MobileNetV2 is selected as the backbone network, and the ECA attention mechanism is introduced after MobileNetV2 extracts shallow features to improve the ability of feature representation; secondly, the ASPP module is improved, and on this basis, the EPSA attention mechanism is introduced to achieve cross-dimensional channel attention and important feature interaction; thirdly, a loss function named CL loss is designed to balance the training offset of multiple categories and better indicate the segmentation quality. This paper conducted experimental verification on the Cityspaces dataset, and the results showed that the mIoU reached 74.9%, which was an improvement of 3.56% compared to DeeplabV3+; and the mPA reached 83.01%, which was an improvement of 2.53% compared to DeeplabV3+.

Need for Objective Task-Based Evaluation of Image Segmentation Algorithms for Quantitative PET: A Study with ACRIN 6668/RTOG 0235 Multicenter Clinical Trial Data.

Reliable performance of PET segmentation algorithms on clinically relevant tasks is required for their clinical translation. However, these algorithms are typically evaluated using figures of merit (FoMs) that are not explicitly designed to correlate with clinical task performance. Such FoMs include the Dice similarity coefficient (DSC), the Jaccard similarity coefficient (JSC), and the Hausdorff distance (HD). The objective of this study was to investigate whether evaluating PET segmentation algorithms using these task-agnostic FoMs yields interpretations consistent with evaluation on clinically relevant quantitative tasks. Methods: We conducted a retrospective study to assess the concordance in the evaluation of segmentation algorithms using the DSC, JSC, and HD and on the tasks of estimating the metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary tumors from PET images of patients with non-small cell lung cancer. The PET images were collected from the American College of Radiology Imaging Network 6668/Radiation Therapy Oncology Group 0235 multicenter clinical trial data. The study was conducted in 2 contexts: (1) evaluating conventional segmentation algorithms, namely those based on thresholding (SUVmax40% and SUVmax50%), boundary detection (Snakes), and stochastic modeling (Markov random field-Gaussian mixture model); (2) evaluating the impact of network depth and loss function on the performance of a state-of-the-art U-net-based segmentation algorithm. Results: Evaluation of conventional segmentation algorithms based on the DSC, JSC, and HD showed that SUVmax40% significantly outperformed SUVmax50%. However, SUVmax40% yielded lower accuracy on the tasks of estimating MTV and TLG, with a 51% and 54% increase, respectively, in the ensemble normalized bias. Similarly, the Markov random field-Gaussian mixture model significantly outperformed Snakes on the basis of the task-agnostic FoMs but yielded a 24% increased bias in estimated MTV. For the U-net-based algorithm, our evaluation showed that although the network depth did not significantly alter the DSC, JSC, and HD values, a deeper network yielded substantially higher accuracy in the estimated MTV and TLG, with a decreased bias of 91% and 87%, respectively. Additionally, whereas there was no significant difference in the DSC, JSC, and HD values for different loss functions, up to a 73% and 58% difference in the bias of the estimated MTV and TLG, respectively, existed. Conclusion: Evaluation of PET segmentation algorithms using task-agnostic FoMs could yield findings discordant with evaluation on clinically relevant quantitative tasks. This study emphasizes the need for objective task-based evaluation of image segmentation algorithms for quantitative PET.

Deep Learning-Based Retinal Layer Segmentation in Optical Coherence Tomography Scans of Patients with Inherited Retinal Diseases.

In optical coherence tomography (OCT) scans of patients with inherited retinal diseases (IRDs), the measurement of the thickness of the outer nuclear layer (ONL) has been well established as a surrogate marker for photoreceptor preservation. Current automatic segmentation tools fail in OCT segmentation in IRDs, and manual segmentation is time-consuming. Patients with IRD and an available OCT scan were screened for the present study. Additionally, OCT scans of patients without retinal disease were included to provide training data for artificial intelligence (AI). We trained a U-net-based model on healthy patients and applied a domain adaption technique to the IRD patients' scans. We established an AI-based image segmentation algorithm that reliably segments the ONL in OCT scans of IRD patients. In a test dataset, the dice score of the algorithm was 98.7%. Furthermore, we generated thickness maps of the full retinal thickness and the ONL layer for each patient. Accurate segmentation of anatomical layers on OCT scans plays a crucial role for predictive models linking retinal structure to visual function. Our algorithm for segmentation of OCT images could provide the basis for further studies on IRDs.

In-situ registration subtraction image segmentation algorithm for spatiotemporal visualization of copper adsorption onto corn stalk-derived pellet biochar by micro-computed tomography

The non-homogeneous structure and high-density ash composition of biochar matrix pose significant challenges in characterizing the dynamic changes of heavy metal adsorption onto biochar with micro-computed tomography (Micro-CT). A novel in-situ registration subtraction image segmentation method (IRS) was developed to enhance micro-CT characterization accuracy. The kinetics of Cu(II) adsorption onto pellet biochar derived from corn stalks were tested. Respectively, the IRS and traditional K-means algorithms were used for image segmentation to the in-situ three-dimensional (3D) visual characterization of the Cu(II) adsorption onto biochar. The results indicated that the IRS algorithm reduced interference from high-density biochar composition, and thus achieved more precise results (R2 = 0.95) than that of K-means (R2 = 0.72). The visualized dynamic migration of Cu(II) from surface adsorption to intraparticle diffusion reflexed the complex mechanism of heavy metal adsorption. The developed Micro-CT method with high generalizability has great potential for studying the process and mechanism of biochar heavy metal adsorption.

Interactive segmentation of medical images using deep learning.

Medical image segmentation algorithms based on deep learning have achieved good segmentation results in recent years, but they require a large amount of labeled data. When performing pixel-level labeling on medical images, labeling a target requires marking ten or even hundreds of points along its edge, which requires a lot of time and labor costs. To reduce the labeling cost, we utilize a click-based interactive segmentation method to generate high-quality segmentation labels. However, in current interactive segmentation algorithms, only the interaction information clicked by the user and the image features are fused as the input of the backbone network (so-called early fusion). The early fusion method has the problem that the interactive information is much sparse at this time. Furthermore, the interactive segmentation algorithms do not take into account the boundary problem, resulting in poor model performance. So we propose early fusion and late fusion strategy to prevent the interaction information from being diluted prematurely and make better use of the interaction information. At the same time, we propose a decoupled head structure, by extracting the image boundary information, and combining the boundary loss function to establish the boundary constraint term, so that the network can pay more attention to the boundary information and further improve the performance of the network. Finally, we conduct experiments on three medical datasets (Chaos, VerSe and Uterine Myoma MRI) to verify the effectiveness of our network. The experimental results show that our network greatly improved compared with the baseline, and NoC@80(the number of interactive clicks over 80% of the IoU threshold) improved by 0.1, 0.1, and 0.2. In particular, we have achieved a NoC@80 score of 1.69 on Chaos. According to statistics, manual annotation takes 25 min to label a case(Uterine Myoma MRI). Annotating a medical image with our method can be done in only 2 or 3 clicks, which can save more than 50% of the cost.

ANALYSIS OF BRAIN SUB REGIONS USING OPTIMIZATION TECHNIQUES IN ALZHEIMERS DISEASE

AD is a progressive neurodegenerative disorder impacting specific brain sub-regions. Accurate identification and analysis of these regions are crucial for early diagnosis and effective intervention. This study employs optimization techniques to enhance the understanding of AD-related alterations in brain sub-regions. Utilizing medical imaging data, a multi-step approach is implemented. Image segmentation algorithms optimize brain sub-region delineation, while feature selection techniques enhance discriminative information extraction. Machine learning models, fine-tuned through optimization, classify images into AD and non-AD categories. Functional connectivity patterns between sub-regions are explored using network optimization methods. Predictive modeling and treatment planning incorporate optimization for improved accuracy and personalized strategies. This research contributes a comprehensive framework for analyzing AD-affected brain sub-regions, integrating optimization techniques into various stages of analysis. The proposed approach enhances diagnostic accuracy, provides insights into disease mechanisms, and facilitates personalized treatment strategies. The optimized methods demonstrate superior accuracy in image segmentation, classification, and predictive modeling. Connectivity analysis reveals significant alterations, offering novel insights. Personalized treatment plans, optimized for individual patients, show promise in improving therapeutic outcomes.