Background Image Research Articles

Multivariate Regression Models for Predicting the Prognosis of Luxation Injuries of Permanent Teeth: Periodontal and Pulp Analyses.

This study aimed to analyse the relationships between injury variables, treatment variables and the status of the periodontium or pulp of luxated teeth. The electronic medical records and imaging data of patients who underwent treatment for luxation injuries of permanent teeth in the paediatric dentistry and dental emergency departments of the Stomatology Hospital, Zhejiang University School of Medicine between 1 January 2012 and 1 January 2022 were retrospectively analysed. First, the patients' records were reviewed to collect background and follow-up imaging data; then, all early clinical variables were reviewed and used to construct models to predict the periodontal and pulp status of the luxated teeth. The models included the significant factors identified in the univariate analysis, and multivariate analysis was performed to evaluate the relationships between the variables and the final status of the periodontium or dental pulp. The chi-square test and Fisher's exact test were employed to analyse the differences in the frequency of each variable. The variables were systematically screened based on their odds ratios, and significance levels were determined by Pearson's chi-square test. A total of 188 patients were initially identified and ultimately included. The age range of the patients was 7-56 years. Logistic regression models for periodontal and pulp prognosis of the luxated teeth were established. The models were refined by combining the results of feature selection, parameter testing and goodness-of-fit testing. The final model included four variables and accurately predicted the periodontal outcome in 65.79% of the cases. The prognostic model for the dental pulp included three significant factors and had an overall accuracy of 94.59%. The prognostic models developed to predict the influence of various factors on the status of the periodontium and dental pulp of luxated teeth demonstrated notable accuracy and practical utility. Therefore, these models are potentially valuable tools for long-term prognostic assessments. Approval no. ChiCTR2100044897.

Quality and Consistency of Rhinoplasty Photos on Social Media: Prevalence of Potentially Misleading Before-and-After Images.

Introduction: There is significant variability in how rhinoplasty results are presented on social media. This study aims to evaluate the quality and consistency of preoperative and postoperative rhinoplasty photos on the social media platform Instagram. Methods: Instagram was queried on April 4, 2024 using the search term #rhinoplastybeforeandafter. Posts meeting our inclusion criteria were analyzed for inconsistencies in background, makeup, use of personal photo, and variations in face size between preoperative and postoperative images. Account demographics and time since surgery were also noted. Results: A total of 888 sets of before-and-after rhinoplasty photos were included, with nearly all (n = 882) posted by surgeon accounts. Of the posts analyzed, 23.7% had a ≥10% face size discrepancy, and 5.1% had a ≥10% face rotation discrepancy. Only 35.7% of posts were considered "high quality," with no significant photo discrepancies between postop and preop images and with postoperative time interval reported. Conclusion: Instagram before-and-after rhinoplasty photos evaluated in this study exhibited significant heterogeneity in quality and consistency regarding image background, image source, face size, face rotation, and brightness. Surgeons should prioritize consistency between preoperative and postoperative photos when sharing their results to minimize the potential for misleading the public.

Detection of foreign materials on Semen Ziziphi Spinosae using hyperspectral imaging technology coupled with convolutional neural networks

The efficacy of Semen Ziziphi Spinosae, a valued medicinal material, can be compromised by the presence of foreign materials, which may also worsen patient conditions and lead to complications. Traditional machine vision techniques often struggle to identify foreign entities that closely resemble Semen Ziziphi Spinosae in color and shape. This study investigates the application of hyperspectral imaging technology for the detection and differentiation of four types of foreign materials (moldy Semen Ziziphi Spinosae shell, wood, and stone) in processing industries. High-resolution hyperspectral imagery was subjected to image segmentation and background noise reduction to separate the contours of the samples within the images. The average spectral signatures of the regions of interest were extracted to construct models such as Convolutional Neural Networks (CNN), Support Vector Machines (SVMs), Mahalanobis distance, Deep Belief Networks, and Generative Adversarial Networks. These models were employed to extract spectral information indicative of foreign matter within the hyperspectral data to identify foreign materialss in Semen Ziziphi Spinosae. Subsequently, the classification outcomes were mapped onto the original hyperspectral images, enabling a visual representation of foreign matter within the seeds. The classification performance of alternative models was compared with that of the CNN model. The results demonstrate that a CNN trained with spectral information from hyperspectral data can accurately identify foreign materialss in Semen Ziziphi Spinosae. The model underwent 30 iterations, achieving an accuracy rate of 99% with a root mean square error (RMSE) of 0.62 for the validation detection, and an accuracy rate of 98% with an RMSE of 0.62 for the validation set.

Neural deflection field for sparse-view tomographic background oriented Schlieren

Three-dimensional (3D) density-varying turbulent flows are widely encountered in high-speed aerodynamics, combustion, and heterogeneous mixing processes. Multicamera-based tomographic background-oriented Schlieren (TBOS) has emerged as a powerful technique for revealing 3D flow density structures. However, dozens of cameras are typically required to obtain high-quality reconstructed density fields. Limited by the number of available optical windows and confined space in the harsh experimental environments, TBOS with only sparse views and limited viewing angles often becomes the necessary choice practically, rendering the inverse problem for TBOS reconstruction severely ill-posed and resulting in degraded tomography quality. In this study, we propose a novel TBOS reconstruction method, neural deflection field, utilizing an extremely light-weight deep neural networks to represent the density gradient fields without using any pretrained neural network models. Particularly, state-of-the-art positional encoding techniques and hierarchical sampling strategies are incorporated to capture the density structures of high spatial frequencies. Required background images for TBOS reconstructions are synthesized based on a high-fidelity nonlinear ray-tracing method with the ground truth flows from conducting large eddy simulations on premixed turbulent flames. Owing to these synthesized BOS images, the superiority of the proposed method is quantitatively verified compared to the classical TBOS reconstruction methods, and the specific contributions from the position encoding and the hierarchical sampling strategy are also elucidated.

A multimodal framework for pepper diseases and pests detection

Pepper diseases and pests typically exhibit small target proportions, diverse shapes and sizes, complex imaging backgrounds, and similarities with the background. Existing detection methods perform poorly in identifying targets of different sizes and shapes within the same scene, and they lack adequate noise suppression capabilities. To address the practical needs of detecting pepper diseases and pests in complex scenarios, we have constructed the first multimodal pepper diseases and pests object detection dataset (PDD). This dataset includes a wide variety of diseases and pests images, along with detailed natural language descriptions of their attributes. Locating the described targets in complex scenes with similar disease symptoms and leaf occlusion presents a significant challenge. To tackle this issue, we propose the PepperNet model for object detection in pepper diseases and pests images using natural language descriptions. This model decomposes complex multimodal features of language and images into explicit attribute features and employs fine-grained multimodal attribute contrast learning strategies. This approach effectively distinguishes subtle local differences between similar objects, achieving fine-grained mapping from language to vision in complex scenarios. Our detection results show a mAP@0.5 of 91.93% and a detection speed of 121.8 frames per second. Visualizations indicate that the model maintains high robustness under varying noise levels and occlusion conditions, demonstrating superior performance and stability across diverse complex scenarios.

Mask-Space Optimized Transformer for Semantic Segmentation of Lithium Battery Surface Defect Images

The segmentation of surface defects in lithium batteries is crucial for enhancing the overall quality of the production process. However, the severe foreground–background imbalance in surface images of lithium batteries, along with the irregular shapes and random distribution of foreground regions, poses significant challenges for defect segmentation. Based on these observations, this paper focuses on the separation of foreground and background in surface defect images of lithium batteries and proposes a novel Mask Space Optimization Transformer (MSOFormer) for semantic segmentation of these images. Specifically, the Mask Boundary Loss (MBL) module in our model provides more efficient supervision during training to enhance the accuracy of the mask computation within the mask attention mechanism, thereby improving the model’s performance in separating foreground and background. Additionally, the Dynamic Spatial Query (DSQ) module allocates spatial information of the image to each query, enhancing the model’s sensitivity to the positions of small foreground targets in various scenes. The Efficient Pixel Decoder (EPD) ensures deformable receptive fields for irregularly shaped foregrounds while further improving the model’s performance and efficiency. Experimental results demonstrate that our method outperforms other state-of-the-art methods in terms of mean Intersection over Union (mIoU). Specifically, our approach achieves an mIoU of 84.18% on the lithium battery surface defect test set and 85.53% and 87.05% mIoUs on two publicly available defect test sets with similar defect characteristics to lithium batteries.

A patch-based deep learning MRI segmentation model for improving efficiency and clinical examination of the spinal tumor

Background and objectiveMagnetic resonance imaging (MRI) plays a vital role in diagnosing spinal diseases, including different types of spinal tumors. However, conventional segmentation techniques are often labor-intensive and susceptible to variability. This study aims to propose a full-automatic segmentation method for spine MRI images, utilizing a convolutional-deconvolution neural network and patch-based deep learning. The objective is to improve segmentation efficiency, meeting clinical needs for accurate diagnoses and treatment planning. MethodsThe methodology involved the utilization of a convolutional neural network to automatically extract deep learning features from spine data. This allowed for the effective representation of anatomical structures. The network was trained to learn discriminative features necessary for accurate segmentation of the spine MRI data. Furthermore, a patch extraction (PE) based deep neural network was developed using a convolutional neural network to restore the feature maps to their original image size. To improve training efficiency, a combination of pre-training and an enhanced stochastic gradient descent method was utilized. ResultsThe experimental results highlight the effectiveness of the proposed method for spine image segmentation using Gadolinium-enhanced T1 MRI. This approach not only delivers high accuracy but also offers real-time performance. The innovative model attained impressive metrics, achieving 90.6% precision, 91.1% recall, 93.2% accuracy, 91.3% F1-score, 83.8% Intersection over Union (IoU), and 91.1% Dice Coefficient (DC). These results indicate that the proposed method can accurately segment spine tumors CT images, addressing the limitations of traditional segmentation algorithms. ConclusionIn conclusion, this study introduces a fully automated segmentation method for spine MRI images utilizing a convolutional neural network, enhanced by the application of the PE-module. By utilizing a patch extraction based neural network (PENN) deep learning techniques, the proposed method effectively addresses the deficiencies of traditional algorithms and achieves accurate and real-time spine MRI image segmentation.

Level2image: A Utility for Making 2D Tile-Based Level Images with Overlays

2D tile-based levels are a common format for video game research, particularly in procedural content generation. Often, tiles are represented as text characters. Here, we describe level2image, a utility that provides a flexible means for converting such levels into a variety of image formats. It can use text tiles or substitute image tiles or backgrounds. There is support for various geometry overlays, such as paths through the level, areas of interest, or boundaries. The utility is a Python script, intended to reduce duplicated work creating such converters within the game research community.

Enhancing wildfire detection: a novel algorithm for controllable generation of wildfire smoke images

Background The lack of wildfire smoke image data is one of the most important factors hindering the development of image-based wildfire detection. Smoke image generation based on image inpainting techniques is a solution worthy of study. However, it is difficult to generate smoke texture with context consistency in complex backgrounds with current image inpainting methods. Aims This work aims to provide a wildfire smoke image database for specific scenarios. Methods We designed an algorithm based on generative adversarial networks (GANs) to generate smoke images. The algorithm includes a multi-scale fusion module to ensure consistency between the generated smoke and backgrounds. Additionally, a local feature-matching mechanism in the discriminator guides the generator to capture real smoke’s feature distribution. Key results We generated 13,400 wildfire smoke images based on forest background images and early fire simulation from the Fire Dynamics Simulator (FDS). Conclusions A variety of advanced object detection algorithms were trained based on the generated data. The experimental results confirmed that the addition of the generated data to the real datasets can effectively improve model performance. Implications This study paves a way for generating object datasets to enhance the reliability of watchtower or satellite wildfire monitoring.

GUing: A Mobile GUI Search Engine using a Vision-Language Model

Graphical User Interfaces (GUIs) are central to app development projects. App developers may use the GUIs of other apps as a means of requirements refinement and rapid prototyping or as a source of inspiration for designing and improving their own apps. Recent research has thus suggested retrieving relevant GUI designs that match a certain text query from screenshot datasets acquired through crowdsourced or automated exploration of GUIs. However, such text-to-GUI retrieval approaches only leverage the textual information of the GUI elements, neglecting visual information such as icons or background images. In addition, retrieved screenshots are not steered by app developers and lack app features that require particular input data. To overcome these limitations, this paper proposes GUing, a GUI search engine based on a vision-language model called GUIClip, which we trained specifically for the problem of designing app GUIs. For this, we first collected from Google Play app introduction images which display the most representative screenshots and are often captioned (i.e. labelled) by app vendors. Then, we developed an automated pipeline to classify, crop, and extract the captions from these images. This resulted in a large dataset which we share with this paper: including 303k app screenshots, out of which 135k have captions. We used this dataset to train a novel vision-language model, which is, to the best of our knowledge, the first of its kind for GUI retrieval. We evaluated our approach on various datasets from related work and in a manual experiment. The results demonstrate that our model outperforms previous approaches in text-to-GUI retrieval achieving a Recall@10 of up to 0.69 and a HIT@10 of 0.91. We also explored the performance of GUIClip for other GUI tasks including GUI classification and sketch-to-GUI retrieval with encouraging results.

Automatic Defects Recognition of Lap Joint of Unequal Thickness Based on X-Ray Image Processing.

It is difficult to automatically recognize defects using digital image processing methods in X-ray radiographs of lap joints made from plates of unequal thickness. The continuous change in the wall thickness of the lap joint workpiece causes very different gray levels in an X-ray background image. Furthermore, due to the shape and fixturing of the workpiece, the distribution of the weld seam in the radiograph is not vertical which results in an angle between the weld seam and the vertical direction. This makes automatic defect detection and localization difficult. In this paper, a method of X-ray image correction based on invariant moments is presented to solve the problem. In addition, a novel background removal method based on image processing is introduced to reduce the difficulty of defect recognition caused by variations in grayscale. At the same time, an automatic defect detection method combining image noise suppression, image segmentation, and mathematical morphology is adopted. The results show that the proposed method can effectively recognize the gas pores in an automatic welded lap joint of unequal thickness, making it suitable for automatic detection.

Lower DTI-ALPS index in patients with major depressive disorder: Correlation with fatigue

Background and purposeDiffusion tensor imaging along perivascular spaces (DTI-ALPS) is an index that may provide insights into intracranial waste clearance processes. Glymphatic system dysfunction has been suggested to play a role in the development of major depressive disorder (MDD). Additionally, fatigue—a common precursor of MDD—is also closely connected to the waste clearance function of the central nervous system (CNS), further underscoring the significance of efficient waste removal in MDD. However, evidence linking altered DTI-ALPS index to MDD remains limited. This study aims to investigate the changes in the DTI-ALPS index in patients with MDD and explore the potential interplay between DTI-ALPS index alterations, fatigue, and the presence of MDD. Material and methodsA total of 46 patients with MDD and 55 healthy controls (HC) were included in the study. All participants underwent diffusion tensor imaging using the same 3-T MRI (3-Tesla Magnetic Resonance Imaging) scanner. The DTI-ALPS index was assessed, and the Chalder Fatigue Scale (CFS) was used to evaluate fatigue levels in both groups, and the 17-item Hamilton Depression Rating Scale (HAMD-17) was used to evaluate the severity of depression in the patients. We compared the DTI-ALPS index and clinical characteristics between the MDD and HC group, and explored the relationship among the DTI-ALPS index, CFS scores, and the presence of MDD through mediation analysis. ResultsThe DTI-ALPS index in the right hemisphere (DTI-ALPS-R) is significantly lower in patients with MDD (t = 2.41, P = 0.02). The MDD patients exhibited significantly higher scores on the CFS scales compared with HCs (t = 13.12, P <.001). Mediation analysis showed that the CFS score plays a significant mediating role between DTI-ALPS-R and the presence of MDD, acting as a full mediator (indirect effect β = −0.230, 95 % CI: [-0.388, −0.059]). ConclusionOur study found that patients with MDD have a reduced DTI-ALPS index. This reduction appears to contribute to the development of MDD by facilitating the accumulation of fatigue symptoms. These findings may provide a new perspective on the pathogenesis of MDD, suggest a potential new biomarker for MDD, and offer new insights for its treatment.

An automated pheochromocytoma and paraganglioma lesion segmentation AI-model at whole-body 68Ga- DOTATATE PET/CT.

Somatostatin receptor (SSR) targeting radiotracer 68Ga-DOTATATE is used for Positron Emission Tomography (PET)/Computed Tomography (CT) imaging to assess patients with Pheochromocytoma and paraganglioma (PPGL), rare types of Neuroendocrine tumor (NET) which can metastasize thereby becoming difficult to quantify. The goal of this study is to develop an artificial intelligence (AI) model for automated lesion segmentation on whole-body 3D DOTATATE-PET/CT and to automate the tumor burden calculation. 132 68Ga-DOTATATE PET/CT scans from 38 patients with metastatic and inoperable PPGL, were split into 70, and 62 scans, from 20, and 18 patients for training, and test sets, respectively. The training set was further divided into patient-stratified 5 folds for cross-validation. 3D-full resolution nnUNet configuration was trained with 5-fold cross-validation. The model's detection performance was evaluated at both scan and lesion levels for the PPGL test set and two other clinical cohorts with NET (n = 9) and olfactory neuroblastoma (ONB, n = 5). Additionally, quantitative statistical analysis of PET parameters including SUVmax, total lesion uptake (TLU), and total tumor volume (TTV), was conducted. The nnUNet AI model achieved an average 5-fold validation dice similarity coefficient of 0.84 at the scan level. The model achieved dice similarity coefficients (DSC) of 0.88, 0.6, and 0.67 at the scan level, the sensitivity of 86%, 61.13%, and 61.64%, and a positive predictive value of 89%, 74%, and 86.54% at the lesion level for the PPGL test, NET and ONB cohorts, respectively. For PPGL cohorts, smaller lesions with low uptake were missed by the AI model (p < 0.001). Anatomical region-based failure analysis showed most of the false negative and false positive lesions within the liver for all the cohorts, mainly due to the high physiologic liver background activity and image noise on 68Ga- DOTATATE PET scans. The developed deep learning-based AI model showed reliable performance for automated segmentation of metastatic PPGL lesions on whole-body 68Ga-DOTATATE-PET/CT images, which may be beneficial for tumor burden estimation for objective evaluation during therapy follow-up. https://www. gov/study/NCT03206060 , https://www. gov/study/NCT04086485 , https://www. gov/study/NCT05012098 .

Semantic Guided Matting Net

Abstract Human matting refers to extracting human parts from natural images with high quality, including human detail information such as hair, glasses, hats, etc. This technology plays an essential role in image synthesis and visual effects in the film industry. When the green screen is not available, the existing human matting methods need the help of additional inputs (such as trimap, background image, etc.), or the model with high computational cost and complex network structure, which brings great difficulties to the application of human matting in practice. To alleviate such problems, we use a segmentation network as the foundation and use multiple branches to achieve human segmentation, contour detail extraction, and information fusion. We also propose a foreground probability map module, which uses the feature maps in the segmentation network to pre-estimate the foreground probabilities of each pixel and obtain Semantic Guided Matting Net. Under the condition that only a single image is needed as the input, the human matting task can be realized by making full use of the semantic information in the image. We validate our method on the P3M-10k dataset. Compared with the benchmark, our method has made significant improvements in various evaluation indicators.

High-speed in vivo calcium recording using structured illumination with self-supervised denoising

High-speed widefield fluorescence imaging of neural activity in vivo is fundamentally limited by fluctuations in recorded signal due to background contamination and stochastic noise. In this study, we show background and shot noise-reduced imaging of the ultrafast genetically encoded Ca2+ indicator GCaMP8f in CA1 pyramidal neurons using periodic structured illumination (SI) with computational image reconstruction. We implement what we believe to be a novel reconstruction method for data acquired using periodic structured illumination, termed pseudo-HiLo (pHiLo), that combines a pseudo-widefield (pWF) reconstruction with individual SI frames to perform a HiLo reconstruction. We compare this new technique to interleaved optical sectioning structured illumination microscopy (OS-SIM) and pWF reconstruction. We quantify the performance of each reconstruction by evaluating contrast, transient peak-to-noise ratio (PNR), pairwise correlation coefficients between ΔF/F time courses extracted from individual in-focus cells, and correlation coefficients between each cell with surrounding cell-free background pixels. We additionally incorporate a self-supervised deep learning method for real-time noise suppression (DeepCAD-RT) into our data preprocessing pipeline. At 500 Hz frame rates, we demonstrate a 75% increase in PNR using the denoised pHiLo reconstruction compared to pWF. Utilizing DeepCAD-RT, we show significant PNR improvements using both structured illumination (SI) reconstruction methods with OS-SIM showing a 59% increase in PNR after denoising. Both pHiLo and OS-SIM reconstructions result in a ≈65% decrease in the mean correlation coefficient of the ΔF/F time courses between ROIs in comparison with pWF, indicating the potential to remove background fluorescent transients from out-of-focus cells.

A Small-Scale Object Detection Algorithm in Intelligent Transportation Scenarios.

In response to the problem of poor detection ability of object detection models for small-scale targets in intelligent transportation scenarios, a fusion method is proposed to enhance the features of small-scale targets, starting from feature utilization and fusion methods. The algorithm is based on the YOLOv4 tiny framework and enhances the utilization of shallow and mid-level features on the basis of Feature Pyramid Network (FPN), improving the detection accuracy of small and medium-sized targets. In view of the problem that the background of the intelligent traffic scene image is cluttered, and there is more redundant information, the Convolutional Block Attention Module (CBAM) is used to improve the attention of the model to the traffic target. To address the problem of data imbalance and prior bounding box adaptation in custom traffic data sets that expand traffic images in COCO and VOC, we propose a Copy-Paste method with an improved generation method and a K-means algorithm with improved distance measurement to enhance the model's detection ability for corresponding categories. Comparative experiments were conducted on a customized 260-thousand traffic data set containing public traffic images, and the results showed that compared to YOLOv4 tiny, the proposed algorithm improved mAP by 4.9% while still ensuring the real-time performance of the model.

The apparent diffusion coefficient can serve as a predictor of survival in patients with gliomas

Background and purposeMagnetic resonance imaging is indispensable for the preoperative diagnosis of glioma. This study aimed to investigate the role of the apparent diffusion coefficient values as predictors of survival in patients with gliomas.Methods and materialsA retrospective analysis was conducted on 101 patients with gliomas who underwent surgery between 2015 and 2020. Diffusion-weighted MRI was performed before the surgery. The regions of interest were categorized into parenchymal area, non-enhancing peritumoral area, and necrotic or cystic area. All the patients were divided into three subgroups: the parenchyma group, the non-enhancing peritumoral signal abnormality group, and the necrosis or cyst group. Univariate and multivariate analyses were performed using COX regression.ResultsIn the parenchymal group, Ki67, P53, IDH, and the high or low ADC values were identified as independent prognosticators for disease-free survival, while Ki67, IDH, and the high or low ADC values for overall survival. In the non-enhancing peritumoral signal abnormality group, Ki67, P53, IDH, and the ADC parenchymal area/ADC non−enhancing peritumoral area ratio were identified as independent prognostic factors for disease-free survival, while Ki67, IDH, and the ADC parenchymal area/ADC non−enhancing peritumoral area ratio for overall survival. In the necrosis or cyst group, Ki67 was significantly associated with disease-free survival, while Ki67 and the ADC value of the necrotic or cystic area for overall survival.ConclusionsThe ADC values, including the ADC value in the parenchymal area, the ADC parenchymal area/ADC non−enhancing peritumoral area ratio, and the ADC value in the necrotic or cystic area, can serve as an efficient and potential index for predicting the survival of patients with glioma.

ILN-SSR: Improved Logarithmic Norm and Sparse Structure Refinement for Infrared Small Target Detection

The effective discrimination of targets from backgrounds in environments characterized by a low signal-to-clutter ratio (SCR) is paramount for the advancement of infrared small target detection (IRSTD). In this work, we propose a novel detection framework predicated on low-rank sparse decomposition (LRSD), incorporating an improved logarithmic norm and a mechanism for sparse structure refinement, herein referred to as the improved logarithmic norm and sparse structure refinement (ILN-SSR). The ILN-SSR framework more precisely characterizes the sparse properties of both the background and the target, enabling a more effective distinction between the target and its background. Initially, our approach entails the utilization of an improved logarithmic norm to precisely estimate the low-rank attributes of the infrared image background. This is followed by the employment of a linear sparse regularization term alongside a target-traits-based sparse regularization term aimed at meticulously identifying targets within sparse regions and refining the sparse structure. Subsequently, we combine these components into the ILN-SSR framework, which formulates IRSTD as an optimization problem. The resolution of this framework is achieved through the implementation of the alternating direction method of multipliers (ADMM). The efficacy of the proposed framework is corroborated through the analysis of six image sequences. Comprehensive experimental assessments affirmed the framework’s substantial robustness in navigating various complex backgrounds.

Towards robust crop disease detection for complex real field background images

Most of the work done in image processing-based crop disease detection focuses on images with plain background. This paper presents a technique for crop disease detection for complex real field background images. A segmentation technique is presented to extract leaf patches from the entire image. Transform domain cepstral analysis is proposed for obtaining cepstral coefficients, to attain two level classifications. The first level classifies the crop species while the second level classifies the species into healthy leaf or leaf with specific type of disease. The work is tested on three crops Banana, Soybean and Grape and is checked on plain background laboratory images and on complex real field images. Suggested technique give species level accuracy of 94.33 %, 94.11 % and 98.44 % and disease level average accuracy of 97.75 %, 96.66 % and 97.95 % for Banana, Soybean and Grape, respectively. Comparison with standard features like texture and shape indicate that the presented technique gives the best results for both plain and complex background images suggesting its utilization in crop disease detection to reduce the agricultural and economic losses.

Improved algorithm for fracture-dissolution pore detection in resistivity imaging logging based on dung beetle optimization

Abstract Resistivity imaging logging has become a direct and precise method for visualizing the structural complexities of reservoir fractures and dissolution pores. The current use of Otsu's thresholding for segmentation results in poor segmentation quality and significant noise. Accurate segmentation of sub-images containing fracture and dissolution pore targets is essential for automated structure identification and subsequent parameter calculation. This study leverages the rapid convergence and robust global optimization capabilities of the Dung Beetle Optimizer to develop enhanced image segmentation approaches. Specifically, it introduces a refined K-Means algorithm for multi-category image segmentation and an Otsu algorithm for multi-threshold image segmentation, both optimized by the Dung Beetle Optimizer. Compared to conventional binary segmentation algorithms, this new algorithm effectively isolates noise and extracts multi-category information. Using the segmented sub-images, this paper integrates mathematical morphology techniques to compute parameters such as area, perimeter, tortuosity length, and pore shape factor for identified targets. Additionally, Principal Component Analysis is used to derive recognition factors for fractures and dissolution pores. Applications show that this factor can identify matrix, fracture, and dissolution pore targets in complex background images. By combining parameter information of the target area, the method effectively removes false information in resistivity imaging and segments sub-images of fractures and dissolution pores, calculating fracture area ratio, dissolution pore area ratio, and total area ratio.