Image Segments Research Articles

An adaptive enhanced human memory algorithm for multi-level image segmentation for pathological lung cancer images

Lung cancer is a critical health issue that demands swift and accurate diagnosis for effective treatment. In medical imaging, segmentation is crucial for identifying and isolating regions of interest, which is essential for precise diagnosis and treatment planning. Traditional metaheuristic-based segmentation methods often struggle with slow convergence speed, poor optimized thresholds results, balancing exploration and exploitation, leading to suboptimal performance in the multi-thresholding segmenting of lung cancer images. This study presents ASG-HMO, an enhanced variant of the Human Memory Optimization (HMO) algorithm, selected for its simplicity, versatility, and minimal parameters. Although HMO has never been applied to multi-thresholding image segmentation, its characteristics make it ideal to improve pathology lung cancer image segmentation.The ASG-HMO incorporating four innovative strategies that address key challenges in the segmentation process. Firstly, the enhanced adaptive mutualism phase is proposed to balance exploration and exploitation to accurately delineate tumor boundaries without getting trapped in suboptimal solutions. Second, the spiral motion strategy is utilized to adaptively refines segmentation solutions by focusing on both the overall lung structure and the intricate tumor details. Third, the gaussian mutation strategy introduces diversity in the search process, enabling the exploration of a broader range of segmentation thresholds to enhance the accuracy of segmented regions. Finally, the adaptive t-distribution disturbance strategy is proposed to help the algorithm avoid local optima and refine segmentation in later stages.The effectiveness of ASG-HMO is validated through rigorous testing on the IEEE CEC'17 and CEC'20 benchmark suites, followed by its application to multilevel thresholding segmentation in nine histopathology lung cancer images. In these experiments, six different segmentation thresholds were tested, and the algorithm was compared to several classical, recent, and advanced segmentation algorithms. In addition, the proposed ASG-HMO leverages 2D Renyi entropy and 2D histograms to enhance the precision of the segmentation process.Quantitative result analysis in pathological lung cancer segmentation showed that ASG-HMO achieved superior maximum Peak Signal-to-Noise Ratio (PSNR) of 31.924, Structural Similarity Index Measure (SSIM) of 0.919, Feature Similarity Index Measure (FSIM) of 0.990, and Probability Rand Index (PRI) of 0.924. These results indicate that ASG-HMO significantly outperforms existing algorithms in both convergence speed and segmentation accuracy. This demonstrates the robustness of ASG-HMO as a framework for precise segmentation of pathological lung cancer images, offering substantial potential for improving clinical diagnostic processes.

Methodology For Extracting Poplar Planted Fields From Very High-Resolution Imagery Using Object-Based Image Analysis and Feature Selection Strategy

Abstract. Poplars (Populus sp.), a tree that grows rapidly species, are significant as industrial forest products. The delineation and monitoring of poplar cultivated areas are invaluable for decision-making processes. With the remote sensing technology, accurate detection of poplar planted areas could be determined much faster, more economically, and with minimum labor requirements. The objective of this research is to create a map of poplar plantations in the Sakarya region of Turkey utilizing Worldview-3 satellite imagery. Object-based image analysis (OBIA) through the application of the multi-resolution segmentation method (MRS) was employed to generate image segments, and then three prevailing machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF) and Rotation Forest (RotFor) were implemented to produce LULC maps of the study area including 11 landscape features. The most effective and contributing object features that assure high separability between landscape features were determined using a filter-based Chi-square algorithm for the prediction models constructed with SVM, RF, and RotFor classifiers. Results revealed that the SVM classifier achieved the highest overall accuracy (91.73%) with 38 features out of 88 features, about 3% improvement compared to the other algorithms. According to the SHAP analysis, the IHS feature was the most effective one in the constructed RF model, followed by the CI (red edge), NDVI-1 and NDVI-2 vegetation indices.

The use of artificial intelligence for predicting postinfarction myocardial viability in echocardiographic images.

Evaluation of standard echocardiographic examination with artificial intelligence may help in the diagnosis of myocardial viability and function recovery after acute coronary syndrome. Sixty-one consecutive patients with acute coronary syndrome were enrolled in the present study (43 men, mean age 61 ± 9 years). All patients underwent percutaneous coronary intervention (PCI). 533 segments of the heart echo images were used. After 12 ± 1 months of follow-up, patients had an echocardiographic evaluation. After PCI each patient underwent cardiac magnetic resonance (CMR) with late enhancement and low-dose dobutamine echocardiographic examination. For texture analysis, custom software was used (MaZda 5.20, Institute of Electronics).Linear and non-linear (neural network) discriminative analyses were performed to identify the optimal analytic method correlating with CMR regarding the necrosis extent and viability prediction after follow-up. Texture parameters were analyzed using machine learning techniques: Artificial Neural Networks, Namely Multilayer Perceptron, Nonlinear Discriminant Analysis, Support Vector Machine, and Adaboost algorithm. The mean concordance between the CMR definition of viability and three classification models in Artificial Neural Networks varied from 42% to 76%. Echo-based detection of non-viable tissue was more sensitive in the segments with the highest relative transmural scar thickness: 51-75% and 76-99%. The best results have been obtained for images with contrast for red and grey components (74% of proper classification). In dobutamine echocardiography, the results of appropriate prediction were 67% for monochromatic images. Detection and semi-quantification of scar transmurality are feasible in echocardiographic images analyzed with artificial intelligence. Selected analytic methods yielded similar accuracy, and contrast enhancement contributed to the prediction accuracy of myocardial viability after myocardial infarction in 12 months of follow-up.

SEGMENTATION OF DIGITAL IMAGES WITH WAVELET TRANSFORMATION USING MATLAB VERSION R2010B

The purpose of this research is to explore and apply the use of wavelet transformation for the segmentation of digital images, utilizing MATLAB version R2010B. The study aims to analyze how wavelet transform can be used to enhance the accuracy and effectiveness of image segmentation, which is a critical process in image processing and computer vision. The research contributes to the field of digital image processing by demonstrating the application of wavelets transformation for segmenting digital images. In this study, the study provides insights into how wavelets can be utilized to improve the detection of image features, especially in identifying image features more accurately. The experimental results show that only the Canny operator's edge detection method has the best edge detector in detecting the edges of objects in wavelet images. The technique of determining the threshold value (thresholding) can be carried out in two ways, namely, automatic method and technique carried out by trial and error. Finally, improving automatic thresholding techniques using AI-driven algorithms to reduce the reliance on trial-and-error methods could provide more consistent results in varied application areas.

Quantification of muscle fiber malformations using edge detection to investigate chronic muscle pressure ulcers.

Microscopy of regenerated tissue shows different morphologies between the healing of acute wounds and chronic wounds. This difference can be seen manually by biologists, but computational methods are needed to automate the characterization of morphology and regenerative quality in regenerated muscle tissue. From the detected edge segments, we computed several imaging biomarkers of interest, such as median tortuosity, number of edge segments normalized by area, median edge segment distance and interquartile range of orientation angles of edge segments of the microscope images of successful and unsuccessful muscle regeneration. We observed that muscle fibers in saline-treated pressure ulcers had a larger interquartile range of orientation angles of the edge segments (p = 0.05) and shorter edge segment distances (p = 0.003) compared to those of acute cardiotoxin injuries. Our edge detection method was able to identify statistically significant differences in some of the imaging biomarkers between saline-treated pressure ulcers and cardiotoxin injuries, suggesting that chronic pressure ulcers have increased muscle fiber malformations compared to cardiotoxin injuries.

Automated system for classifying images of video streams for timely detection of fires

ABSTRACT Early detection of a fire during its development stage is highly desired to minimize human and material losses. The improvement of the efficiency of these monitoring systems through the analysis of fire video data drawn from unmanned aerial vehicles is considered . Classification of video images when monitoring a fire event on the territory is used based on their segmentation into identical rectangular segments of a given size and then assigning them to one of three classes: Smoke, Flame, and Indifferent classes. The Walsh-Hadamard transform was used to form descriptors for the Weak classifiers. Descriptors were calculated for three Weak classifiers; first, the Walsh-Hadamard transform was calculated for the window of the entire segment, and its spectral coefficients were used for the first Weak classifier. Next, the descriptors were calculated in windows whose sizes were half and a quarter of the original window size. The classifier consisted of three independently-trained neural networks. A simple ensemble averaging block was used to combine the outputs of those networks. A special software code has been developed to classify video images. The code allows us to create a database of segments of the ‘Smoke’ and ‘Flame’ classes, determine the two-dimensional Walsh-Hadamard spectrum of video image segments, train fully connected neural networks, conduct exploratory analysis and evaluate the relevance of the calculated two-dimensional spectral coefficients. For validation we used real data from Сlosed Circuit Television cameras presented in open databases. The experimental studies on the classification of video data containing flames and smoke showed that the proposed method has an average smoke detection accuracy of 86% and an average flame detection accuracy of 89.5%. Errors II in smoke detection and flame detection had the averages of 13% and 4.5%, respectively.

Characterization of coal permeability considering fracture shape—Using the MP-Otsu threshold segmentation algorithm

Coal seepage simulation based on digital images is capable of visualizing the three-dimensional seepage process and providing permeability data. However, Otsu's conventional thresholding algorithm is prone to image mis-segmentation for extracting pores and fractures, which makes the reconstructed model unable to reestablish the coal structure truly. The present work first explains the failure mechanism of the conventional Otsu algorithm for image segmentation based on computed tomography (CT) images of coal. Three weighting factors, including the slope of the mineral content fitting curve against the Otsu threshold and the approximate proportion of the target in the image, are introduced to enhance the accuracy of the threshold relation. In continuing, the MP-Otsu thresholding algorithm is proposed. Finally, the 3D fracture model of the coal is reconstructed from the CT image via the MP-Otsu thresholding algorithm. Focusing on the shape of the model, the permeability is then calculated on the basis of the pipe flow model and the parallel plate model. The contribution of fractures with various radii in the pipe model and different apertures in the parallel plate model is appropriately incorporated into the permeability evaluation. The obtained results reveal that the presence of mineral components and the low porosity of coal are the chief reasons for the failure of the conventional Otsu algorithm in the digital image segmentation of coal. The porosities calculated by the improved MP-Otsu algorithm are predicted to be 7.13 %-72.03 % lower than those of the Otsu’s method, which effectively improves the over-segmentation of fractures by Otsu and could accurately extract fractures in images. The permeability model, considering the fracture shape, gives results similar to the simulation results, and parallel plate fractures remarkably affect the permeability of the coal body. In particular, fractures with radius R > 7 μm and apertures b > 50 μm account for more than 90 % of the permeability and thus play a key role in the seepage process.

IMPROVEMENT OF NEURAL NETWORK MODEL TOPOLOGY FOR OBJECT SEGMENTATION IN DIGITAL IMAGES BASED ON CONVOLUTIONAL NEURAL NETWORKS

Nowadays, convolutional neural networks have demonstrated significant performance gains over traditional machine learning methods for various real-world computational intelligence tasks such as digital image classification. However, to achieve the best accuracy, the network topology should be modeled using different architectures with different number of filters, kernel size, number of layers, etc., which actualizes the problem of developing and justifying appropriate selection methods. Taking into account the above mentioned, the aim of the paper is to justify an approach that will improve the topology of the neural network model for object segmentation in digital images based on convolutional neural networks. The research methods are system analysis, modeling, machine learning and fuzzy logic theory, and decision-making theory. As a result of the analysis, the paper proposes an algorithm to improve the topology of the neural network model based on differential evolution to optimize the accuracy of image segmentation and the training time of the network. Differential evolution is applied to determine the optimal number of layers in the network topology, which promotes faster convergence. Within the proposed algorithm, an encoding step was identified to represent the structure of each network using a fixed-length integer array, after which it is proposed to utilize differential evolution processes (mutation, recombination, and selection) to efficiently explore the search space. Prospects for further research are to develop methods and techniques to encode a candidate solution using different numbers of hidden blocks in each convolution.

Improved genetic algorithm for multi-threshold optimization in digital pathology image segmentation

This paper presents an improved genetic algorithm focused on multi-threshold optimization for image segmentation in digital pathology. By innovatively enhancing the selection mechanism and crossover operation, the limitations of traditional genetic algorithms are effectively addressed, significantly improving both segmentation accuracy and computational efficiency. Experimental results demonstrate that the improved genetic algorithm achieves the best balance between precision and recall within the threshold range of 0.02 to 0.05, and it significantly outperforms traditional methods in terms of segmentation performance. Segmentation quality is quantified using metrics such as precision, recall, and F1 score, and statistical tests confirm the superior performance of the algorithm, especially in its global search capabilities for complex optimization problems. Although the algorithm’s computation time is relatively long, its notable advantages in segmentation quality, particularly in handling high-precision segmentation tasks for complex images, are highly pronounced. The experiments also show that the algorithm exhibits strong robustness and stability, maintaining reliable performance under different initial conditions. Compared to general segmentation models, this algorithm demonstrates significant advantages in specialized tasks, such as pathology image segmentation, especially in resource-constrained environments. Therefore, this improved genetic algorithm offers an efficient and precise multi-threshold optimization solution for image segmentation, providing valuable reference for practical applications.

Robust and smooth Couinaud segmentation via anatomical structure-guided point-voxel network

Precise Couinaud segmentation from preoperative liver computed tomography (CT) is crucial for surgical planning and lesion examination. However, this task is challenging as it is defined based on vessel structures, and there is no intensity contrast between adjacent Couinaud segments in CT images. To solve this challenge, we design a multi-scale point-voxel fusion framework, which can more effectively model the spatial relationship of points and the semantic information of the image, producing robust and smooth Couinaud segmentations. Specifically, we first segment the liver and vessels from the CT image and generate 3D liver point clouds and voxel grids embedded with the vessel structure. Then, our method with two input-specific branches extracts complementary feature representations from points and voxels, respectively. The local attention module adaptively fuses features from the two branches at different scales to balance the contribution of different branches in learning more discriminative features. Furthermore, we propose a novel distance loss at the feature level to make the features in the segment more compact, thereby improving the certainty of segmentation between segments. Our experimental results on three public liver datasets demonstrate that our proposed method outperforms several state-of-the-art methods by large margins. Specifically, in out-of-distribution (OOD) testing of LiTS dataset, our method exceeded the voxel-based 3D UNet by approximately 20% in Dice score, and outperformed the point-based PointNet2Plus by approximately 8% in Dice score. Our code and manual annotations of the public datasets presented in this paper are available online: https://github.com/xukun-zhang/Couinaud-Segmentation.

Research on tumors segmentation based on image enhancement method

One of the most effective ways to treat liver cancer is to perform precise liver resection surgery, the key step of which includes precise digital image segmentation of the liver and its tumor. However, traditional liver parenchymal segmentation techniques often face several challenges in performing liver segmentation: lack of precision, slow processing speed, and computational burden. These shortcomings limit the efficiency of surgical planning and execution. In this work, the model initially describes in detail a new image enhancement algorithm that enhances the key features of an image by adaptively adjusting the contrast and brightness of the image. Then, a deep learning-based segmentation network was introduced, which was specially trained on the enhanced images to optimize the detection accuracy of tumor regions. In addition, multi-scale analysis techniques have been incorporated into the study, allowing the model to analyze images at different resolutions to capture more nuanced tumor features. In the presentation of the experimental results, the study used the 3Dircadb dataset to test the effectiveness of the proposed method. The experimental results show that compared with the traditional image segmentation method, the new method using image enhancement technology has significantly improved the accuracy and recall rate of tumor identification.

2D Raman imaging for vibrational and rotational temperature mapping in H2

We showcase a Raman scattering diagnostic for 2D imaging of the rotational and vibrational temperatures of H2. Applications include environments with vibrational-rotational non-equilibrium such as H2-containing supersonic jets and plasma reactors. We image segments of the laser scattering spectrum, containing one or more Raman features, using an ICCD camera that is equipped with one of several bandpass filters. Knowing their transmission spectrum allows the construction of calibration curves that relate the relative intensities of the multi-spectral image to the rotational and vibrational temperatures. Additionally, the absolute intensity indicates the density of H2. We illustrate the capability to independently measure rotational (300–3000 K, ±150 K) and vibrational temperatures (1000–3000 K, ±50 K). The technique is demonstrated on two different H2 microwave plasmas and validated against conventional fitting of 0D and 1D Raman spectra.

VICR: A Novel Software for Unbiased Video and Image Analysis in Scientific Research.

In scientific research, objectivity and unbiased data analysis is crucial for the validity and reproducibility of outcomes. This is particularly important for studies involving video or image categorization. A common approach of decreasing the bias is delegating data analysis to researchers unfamiliar with the experimental settings. However, this requires additional personnel and is prone to cognitive biases. Here we describe the Video & Image Cutter & Randomizer (VICR) software (https://github.com/kkihnphd/VICR), designed for unbiased analysis by segmenting and then randomizing videos or still images. VICR allows a single researcher to conduct and analyze studies in a blinded manner, eliminating the bias in analysis and streamlining the research process. We describe the features of the VICR software and demonstrate its capabilities using zebrafish behavior studies. To our knowledge, VICR is the only software for the randomization of video and image segments capable of eliminating bias in data analysis in a variety of research fields.

Patch-wise vector quantization for unsupervised medical anomaly detection

Radiography images inherently possess globally consistent structures while exhibiting significant diversity in local anatomical regions, making it challenging to model their normal features through unsupervised anomaly detection. Since unsupervised anomaly detection methods localize anomalies by utilizing discrepancies between learned normal features and input abnormal features, previous studies introduce a memory structure to capture the normal features of radiography images. However, these approaches store extremely localized image segments in their memory, causing the model to represent both normal and pathological features with the stored components. This poses a significant challenge in unsupervised anomaly detection by reducing the disparity between learned features and abnormal features. Furthermore, with the diverse settings in radiography imaging, the above issue is exacerbated: more diversity in the normal images results in stronger representation of pathological features. To resolve the issues above, we propose a novel pathology detection method called Patch-wise Vector Quantization (P-VQ). Unlike the previous methods, P-VQ learns vector-quantized representations of normal “patches” while preserving its spatial information by incorporating vector similarity metric. Furthermore, we introduce a novel method for selecting features in the memory to further enhance the robustness against diverse imaging settings. P-VQ even mitigates the “index collapse” problem of vector quantization by proposing top-k% dropout. Our extensive experiments on the BMAD benchmark demonstrate the superior performance of P-VQ against existing state-of-the-art methods.

Application of improved and efficient image repair algorithm in rock damage experimental research

In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm’s operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis.

Enhancing ectasia screening using advanced AS-OCT: a case series of challenging refractive candidates.

Ectasia screening in candidates for laser refractive surgery is mandatory during preoperative evaluation. Despite the availability of modern imaging techniques, refractive surgeons often face borderline decisions when patients present with suspicious tomographic findings. This case series presents refractive candidates with suspicious tomographic findings and demonstrates how to interpret them using Scheimpflug imaging and additional anterior segment optical coherence tomography (AS-OCT). Department of Ophthalmology, University Hospital, LMU Munich. This case series examines six potential candidates for refractive surgery with a mean age of 29.2 ± 3.9 years, whose corneal assessments using Scheimpflug imaging raised suspicion for ectasia. Each candidate was additionally examined with AS-OCT and reevaluated. The mean manifest subjective spherical equivalent was -3.67 ± 1.8 diopters. The total corneal thickness measured 537 µm ± 30 µm at its thinnest point. None of the candidates had any reported underlying corneal or ophthalmic diseases, and slit lamp examinations revealed no abnormal morphological findings. Both Scheimpflug imaging and AS-OCT are appropriate tools for screening refractive candidates for ectasia. While topographic and elevation analyses yielded comparable results regarding corneal structure, the epithelial mapping provided by AS-OCT played a critical role in decision-making for cases with borderline tomographic findings. Establishing a global consensus on the use of epithelial mapping in ectasia screening is necessary.

Advancing precision medicine in cutaneous oncology through 3D high-frequency ultrasound imaging.

e13625 Background: Clinical assessment of tumor depth and shape often requires biopsy, especially when evaluating skin cancers. High Frequency Ultrasound (HFUS) is a non-invasive clinical tool that can quickly produce high resolution images ideally suited for evaluating topical tumors. In comparison to other imaging modalities, HFUS do not always provide the spatial data required to model 3-dimensional (3D) tumors. Currently, 3D-images are an amalgamation of many segmented image slices. However, in outpatient clinics it is unrealistic for HFUS devices and clinicians to gather large sets of image data. In our present original research, we aimed to overcome limited number of HFUS and dermatoscopic images by constructing a simple framework to process sparse and non-spatial data into working 3D skin tumor models. Methods: We analyzed two different skin tumors, the dermal duct tumor (DDT) and basal cell carcinoma (BCC), each with different number of HFUS images at 30Hz and 50Hz respectively. 1 set of 6 parallel images were obtained for the BCC, while 2 orthogonal sets of 6 parallel images were obtained for the DDT. For each tumor, a single dermatoscopic photo and sets of bitmap HFUS images with a resolution of 1024 x 512 were taken. Our study was IRB-approved. Mathematica and MATLAB, both with accessible documentation, were used to process images. Results: We developed a novel algorithmic approach to produce 3D tumor models with limited medical data. Both tumors appear corporally hypoechoic under HFUS. Mathematica was used to first reduce HFUS image noise and binarize the tumor region with a bilateral Gaussian filter. The user-friendly MATLAB Image Segmenter application manually filled the region of interest. Next, Mathematica was used to orient and plot tumor borders in 3D. Finally, we encompassed the borders with a 3D convex hull to conservatively predict tight margins around the tumor. Conclusions: Our simplified approach allows clinicians and scientists to produce 3D renderings of tumor margins. Margin control must always be balanced with a positive surgical outcome. By approaching our 3D-model with a convex hull around sampled border cross-sections, we chose a conservative approach that focuses skin tumor margin evaluation. Our results promote precision medicine and planning into the current standard of skin cancer treatment. Our findings can allow tracking tumor progression or treatment possible via non-invasive evaluation of tumor depth and contour. An aim of our present research is to lower the barrier of 3D construction in clinical settings. Simple algorithms, such as shown in our work, transform orthogonal and sparse data into usable data that can be implemented to train machine learning and artificial intelligent programs. Future research tailoring this approach may allow instant programming of HFUS images to non-invasively render cutaneous tumor borders and depth in real time during clinical examination.

Pest Detection and Classification in Peanut Crops

Recent advancements in image processing have significantly improved pest detection and classification in peanut crops. Our study introduces an innovative approach that optimizes image features for accurate pest identification. Leveraging insights from successful image analysis methodologies, our model employs a tailored architecture for pest detection, segmentation, and classification tasks. By integrating dual branch segment representations and a dual-layer transformer encoder, we aim to enhance image representations and consolidate pest image segments of varying sizes. We evaluate our approach using three distinct pest datasets—Aphids, Wireworm, and Gram Caterpillar—ensuring comprehensive analysis and model validation. Prior to training, we preprocess the datasets extensively, employing feature extraction techniques and addressing image quality issues. We then apply normalization procedures to standardize the data for seamless integration into our model architecture. Our methodology focuses on extracting key features through self-attention mechanisms and standardized scaling processes to enhance predictive capabilities. Comprehensive experimentation demonstrates the superiority of our approach, outperforming established benchmarks in pest detection and classification with high accuracy rates. In summary, our study presents a novel framework that optimizes feature extraction and enhances predictive accuracy in pest detection and classification for peanut crops, addressing the unique challenges of agricultural pest identification.

Unsupervised Image Segmentation Parameters Evaluation for Urban Land Use/Land Cover Applications

Image segmentation plays an important role in object-based classification. An optimal image segmentation should result in objects being internally homogeneous and, at the same time, distinct from one another. Strategies that assess the quality of image segmentation through intra- and inter-segment homogeneity metrics cannot always predict possible under- and over-segmentations of the image. Although the segmentation scale parameter determines the size of the image segments, it cannot synchronously guarantee that the produced image segments are internally homogeneous and spatially distinct from their neighbors. The majority of image segmentation assessment methods largely rely on a spatial autocorrelation measure that makes the global objective function fluctuate irregularly, resulting in the image variance increasing drastically toward the end of the segmentation. This paper relied on a series of image segmentations to test a more stable image variance measure based on the standard deviation model as well as a more robust hybrid spatial autocorrelation measure based on the current Moran’s index and the spatial autocorrelation coefficient models. The results show that there is a positive and inversely proportional correlation between the inter-segment heterogeneity and the intra-segment homogeneity since the global heterogeneity measure increases with a decrease in the image variance measure. It was also found that medium-scale parameters produced better quality image segments when used with small color weights, while large-scale parameters produced good quality segments when used with large color factor weights. Moreover, with optimal segmentation parameters, the image autocorrelation measure stabilizes and follows a near horizontal fluctuation while the image variance drops to values very close to zero, preventing the heterogeneity function from fluctuating irregularly towards the end of the image segmentation process.

Near-real-time multiparametric seismic and visual monitoring of explosive activity at Sabancaya volcano, Peru

This study presents the development of a multiparametric system that utilizes artificial intelligence techniques to identify and analyze volcanic explosions in near real-time. The study analyzed 1343 explosions recorded between 2019 and 2021, along with seismic, meteorological, and visible image data from the Sabancaya volcano. Deep learning algorithms like the U-Net convolutional neural network were used to segment and measure volcanic plumes in images, while boosting-based machine learning ensembles were used to classify seismic events related to ash plumes. The findings demonstrate that these approaches effectively handle large amounts of data generated during seismic and eruptive crises. The U-Net network achieved precise segmentation of volcanic plumes with over 98% accuracy and the ability to generalize to new data. The CatBoost classifier achieved an average accuracy of 94.5% in classifying seismic events. These approaches enable the real-time estimation of eruptive parameters without human intervention, contributing to the development of early warning systems for volcanic hazards. In conclusion, this study highlights the feasibility of using seismic signals and images to detect and characterize volcanic explosions in near real-time, making a significant contribution to the field of volcanic monitoring.