Imaging Method For Detection Research Articles

The image segmentation and polyp detection in colonoscopy images using computer aided detection (CAD)

Segmentation is the process through which each pixel in an image is assigned a class. Segmentation may be used to medical image analysis to assist in image-guided surgery, radiation therapy, and better radiological diagnostics. This article summarises our study towards creating a computer-assisted detection (CAD) method for colonoscopy images containing polyps. Our method is a hybrid context-shape strategy that uses shape information to accurately detect polyps and context information to eliminate non-polyp structures. To begin, a basic edge map is generated from a colonoscopy picture. Second, we use our unique feature extraction and edge classification technique to remove non-polyp edges from the edge map. Third, we use our updated edge maps and newly developed voting mechanism to identify polyp candidates with probabilistic confidence ratings. The proposed CAD system was compared to two publicly accessible polyp databases: CVC-ColonDB, which includes 300 colonoscopy pictures with 300 polyp occurrences from 15 different polyps, and ASU-Mayo, which contains 19,400 colonoscopy frames with a total of 5,200 polyp instances from 10 distinct polyps.

A Shadow Detection Method for High-Resolution Remote Sensing Images based on Shadow Direction Prior

A Shadow Detection Method for High-Resolution Remote Sensing Images based on Shadow Direction Prior

A Fast Progressive Ship Detection Method for Very Large Full-Scene SAR Images

A Fast Progressive Ship Detection Method for Very Large Full-Scene SAR Images

TKR-FSOD: Fetal Anatomical Structure Few-Shot Detection Utilizing Topological Knowledge Reasoning.

Fetal multi-anatomical structure detection in Ultrasound (US) images can clearly present the relationship and influence between anatomical structures, providing more comprehensive information about fetal organ structures and assisting sonographers in making more accurate diagnoses, widely used in structure evaluation. Recently, deep learning methods have shown superior performance in detecting various anatomical structures in ultrasound images but still have the potential for performance improvement in categories where it is difficult to obtain samples, such as rare diseases. Few-shot learning has attracted a lot of attention in medical image analysis due to its ability to solve the problem of data scarcity. However, existing few-shot learning research in medical image analysis focuses on classification and segmentation, and the research on object detection has been neglected. In this paper, we propose a novel fetal anatomical structure fewshot detection method in ultrasound images, TKR-FSOD, which learns topological knowledge through a Topological Knowledge Reasoning Module to help the model reason about and detect anatomical structures. Furthermore, we propose a Discriminate Ability Enhanced Feature Learning Module that extracts abundant discriminative features to enhance the model's discriminative ability. Experimental results demonstrate that our method outperforms the state-of-the-art baseline methods, exceeding the second best method with a maximum margin of 4.8% on 5-shot of split 1 under 4CC. The code is publicly available at: https://github.com/lixi92/TKR-FSOD.

A pixel-wise segmentation method for automatic X-ray image detection of chip packaging defects

A pixel-wise segmentation method for automatic X-ray image detection of chip packaging defects

A Modified Microwave Imaging Method for Brain Stroke Detection

A Modified Microwave Imaging Method for Brain Stroke Detection

High-Precision Imaging and Dense Vehicle Target Detection Method for Airborne Single-Pass Circular Synthetic Aperture Radar

High-Precision Imaging and Dense Vehicle Target Detection Method for Airborne Single-Pass Circular Synthetic Aperture Radar

Quantitative visualization of myocardial ischemia-reperfusion-induced cardiac lesions via ferroptosis magnetic particle imaging.

Myocardial ischemia-reperfusion (MI/R) injury is a complication in vascular reperfusion therapy for MI, occurring in approximately 60% of patients. Ferroptosis is an important process in the development of MI/R cardiac lesions. Transferrin receptor 1 (TfR1), a marker of ferroptosis, corresponds to the changes in MI/R cardiac lesions and is expected to be a biomarker for detecting MI/R-induced ferroptosis. However, the noninvasive in vivo visualization of ferroptosis in MI/R is a big challenge. Thus, this study aimed to develop a novel multimodal imaging platform to identify markers of MI/R cardiac lesions in vivo through targeting TfR1. Methods: Magnetic particle imaging (MPI) modality for ferroptosis based on superparamagnetic cubic-iron oxide nanoparticles (SCIO NPs), named feMPI, has been developed. FeMPI used TfR1 as a typical biomarker. The feMPI probe (SCIO-ICG-CRT-CPPs NPs, CCI NPs) consists of SCIO NPs, TfR1-targeting peptides (CRT), cell-penetrating peptides (CPPs), and indocyanine green (ICG). The specificity and sensitivity of CCI NPs in the MI/R mouse model were evaluated by MPI, magnetic resonance imaging (MRI), and near-infrared (NIR) fluorescent imaging. Results: The intensity of the MPI signal correlates linearly with the percentage of infarct area in MI/R stained by TTC, enabling a quantitative assessment of the extent of cardiac lesions. Notably, these findings are consistent with the standard clinical biochemical indicators in MI/R within the first 24 h. FeMPI detects cardiac injury approximately 48 h prior to the current clinical imaging detection methods of MI/R. Conclusion: The feMPI strategy can be a powerful tool for studying the process of MI/R-induced ferroptosis in vivo, providing clues for molecular imaging and drug development of ferroptosis-related treatments.

Detection of Chicken Carcass Defects in Hyperspectral Images Based on the Faster RCNN Model Research on Hyperspectral Image Detection Method of Chicken Carcass Skin Damage Defect Based on Faster RCNN Model

Detection of Chicken Carcass Defects in Hyperspectral Images Based on the Faster RCNN Model Research on Hyperspectral Image Detection Method of Chicken Carcass Skin Damage Defect Based on Faster RCNN Model

Unsupervised spatial self-similarity difference-based change detection method for multi-source heterogeneous images

Multi-source heterogeneous change detection has been widely used in dynamic disaster monitoring, land cover updating, etc. Various methods have been proposed to make heterogeneous data comparable. However, heterogeneous images are difficult to compare directly and may be affected by noise. Most existing methods obtain change information through mapping and regression, lacking the utilisation of image spatial information and a comprehensive portrayal of the changes, which may affect change detection results. To address these challenges, we propose an unsupervised spatial self-similarity difference-based change detection (USSD) method for multi-source heterogeneous images to evaluate the similarity of spatial relationships in heterogeneous images. First, the images are divided into image blocks to construct spatial self-difference images between individual image blocks aiming to make the data comparable. Second, the change information is portrayed in terms of both the magnitude differences and similarity differences to obtain a more comprehensive spatial self-difference change magnitude map. Then, the spatial neighbourhood information of the spatial self-difference change magnitude map is considered to avoid noise. Experimental results on six open datasets indicate that the overall accuracy of the USSD method was approximately 85%–95%. This method improves the change magnitude map discrimination, better detects the change region, and avoids noise in synthetic aperture radar images.

Towards complex scenes: A deep learning-based camouflaged people detection method for snapshot multispectral images

Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO (MS-YOLO), which utilizes the SPD-Conv and SimAM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset (MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.

Electrochemiluminescence of hot exciton nanomaterial with boosted efficiency for visual bioanalysis

The electrochemiluminescence (ECL) efficiency (ΦECL) of organic ECL emitters is greatly influenced by the excited state property and component. Here we design a hot exciton molecule (BCzP-BT) with hybridized local and charge-transfer (HLCT) excited state property to prepare the first hot exciton organic nanorods (BB NRs). The BB NRs exhibit both sensitive annihilation and intense coreactant ECL emissions with a band gap emission model. Due to the reverse intersystem crossing among high-lying excited states (hRISC), the HLCT excited state property leads to highly efficient utilization of triplet excitons and thus high photoluminescence quantum yields (ΦPL) of more than 80% at 554 nm, which is demonstrated by solvatochromic experiments and quantum chemical calculations. The high ΦPL endows BB NRs with superior ΦECL over other nanoemitters, even the thermally activated delayed fluorescence materials, and thus offers highly-efficient nanoemitters for the design of ECL imaging strategy. As a proof of concept, an ECL probe is constructed by assembling BHQ2-ssDNA on BB NRs to integrate CRISPR/Cas12a system for target recognition, which produces a rapid and high-throughput ECL imaging platform. The proposed imaging method for HPV16 DNA detection shows excellent performance with a detection limit of 0.6 fM. This work broadens the application of hot exciton materials in ECL field, and opens a new avenue for developing next-generation ECL devices.

Adaptive clustering object detection method for UAV images under long-tailed distributions

UAV images are characterized by small targets, difficult to identify in the background image, clustering and sparse distribution of targets, etc. Many researchers have proposed the clustering target detection method (ClusDet) for UAV images. However, due to the large differences in target scales and uneven distribution of targets in UAV images, showing long-tailed distribution, the traditional ClusDet algorithm tends to truncate large and medium targets in the process of clustering; in the detection process, the fixed-threshold NMS method in the ClusDet algorithm is difficult to adaptively detect targets of different sizes, clustering and mutual occlusion. To address the above problems, this paper proposes an adaptive clustered target detection algorithm based on UAV images under long-tail distribution. The method is divided into three sub-networks: the adaptive clustering sub-network, which outputs several segmented images of small target clustering regions by extracting potential small target clustering regions in UAV aerial images; the segmentation and filling sub-network, which fills the images with disproportionate aspect ratio for the output of the adaptive clustering network to keep the size of the images within the reasonable range required by the detection network; and the detection sub-network, which detects the targets within the reasonable range required by the detection network by introducing attention mechanism, using variable threshold NMS, and training using sample balancing strategy effectively improve the detection accuracy of targets in the clustered region. Trained in VisDrone 2019 dataset, the simulation results show that the UAV image adaptive clustering target detection method based on long-tailed distribution has a large improvement in the detection accuracy of small targets, and can effectively improve the detection accuracy of the model for targets in the aggregation region, while the model has good generalization ability.

RETRACTED ARTICLE: Human motion image detection and tracking method based on Gaussian mixture model combined with light sensor

RETRACTED ARTICLE: Human motion image detection and tracking method based on Gaussian mixture model combined with light sensor

Intelligent Detection Method for Wildlife Based on Deep Learning.

Wildlife is an important part of natural ecosystems and protecting wildlife plays a crucial role in maintaining ecological balance. The wildlife detection method for images and videos based on deep learning can save a lot of labor costs and is of great significance and value for the monitoring and protection of wildlife. However, the complex and changing outdoor environment often leads to less than satisfactory detection results due to insufficient lighting, mutual occlusion, and blurriness. The TMS-YOLO (Takin, Monkey, and Snow Leopard-You Only Look Once) proposed in this paper is a modification of YOLOv7, specifically optimized for wildlife detection. It uses the designed O-ELAN (Optimized Efficient Layer Aggregation Networks) and O-SPPCSPC (Optimized Spatial Pyramid Pooling Combined with Cross Stage Partial Channel) modules and incorporates the CBAM (Convolutional Block Attention Module) to enhance its suitability for this task. In simple terms, O-ELAN can preserve a portion of the original features through residual structures when extracting image features, resulting in more background and animal features. However, O-ELAN may include more background information in the extracted features. Therefore, we use CBAM after the backbone to suppress background features and enhance animal features. Then, when fusing the features, we use O-SPPCSPC with fewer network layers to avoid overfitting. Comparative experiments were conducted on a self-built dataset and a Turkish wildlife dataset. The results demonstrated that the enhanced TMS-YOLO models outperformed YOLOv7 on both datasets. The mAP (mean Average Precision) of YOLOv7 on the two datasets was 90.5% and 94.6%, respectively. In contrast, the mAP of TMS-YOLO in the two datasets was 93.4% and 95%, respectively. These findings indicate that TMS-YOLO can achieve more accurate wildlife detection compared to YOLOv7.

CdTe quantum dots as fluorescent nanotools for in vivo glioblastoma imaging

Glioblastoma (GBM) is the most aggressive and infiltrating primary tumor of the central nervous system (CNS), showing a variety of mutations and a high degree of vascularity, cell polymorphism, and nuclear atypia. GBM treatment often recurs to surgical resection, but such protocol lacks efficacy since complete tumor removal is not entirely successful due to invasive cells that cannot be detected at the moment of the surgery. Here, we describe a new in vivo targeting and imaging method for GBM detection in an orthotropic mouse model using fluorescent CdTe quantum dots (CdTe QDs) conjugated to anti-glial fibrillary acidic protein (anti-GFAP). We conjugated and optimized red-emitting CdTe QDs to anti-GFAP to label GBM (U87 cell line) in vivo. The in vivo tumor growth was visualized by the hematoxylin and eosin staining and showed the successful delivery of GBM cells into the mouse brain parenchyma. CdTe/anti-GFAP QDs were injected into the tumor region, and their uptake by tumor cells was visualized by fluorescence microscopy, showing a specific dual labeling with vimentin-immunoreactive GBM. The results reported here provide new perspectives for using CdTe QDs in GBM detection, suggesting their potential application in imaging-guided surgery and a potential fluorescent tool to be applied in the monitoring of 3D tumor glial cultures.

Diagnostic accuracy of non-invasive imaging methods in detection of intraplaque hemorrhage in carotid atherosclerotic plaques: A systematic review and meta-analysis

Diagnostic accuracy of non-invasive imaging methods in detection of intraplaque hemorrhage in carotid atherosclerotic plaques: A systematic review and meta-analysis

An Image Detection Model for Aggressive Behavior of Group Sheep

Sheep aggression detection is crucial for maintaining the welfare of a large-scale sheep breeding environment. Currently, animal aggression is predominantly detected using image and video detection methods. However, there is a lack of lightweight network models available for detecting aggressive behavior among groups of sheep. Therefore, this paper proposes a model for image detection of aggression behavior in group sheep. The proposed model utilizes the GhostNet network as its feature extraction network, incorporating the PWConv and Channel Shuffle operations into the GhostConv module. These additional modules improve the exchange of information between different feature maps. An ablation experiment was conducted to compare the detection effectiveness of the two modules in different positions. For increasing the amount of information in feature maps of the GhostBottleneck module, we applied the Inverted-GhostBottleneck module, which introduces inverted residual structure based on GhostBottleneck. The improved GhostNet lightweight feature extraction network achieves 94.7% Precision and 90.7% Recall, and its model size is only 62.7% of YOLOv5. Our improved model surpasses the original model in performance. Furthermore, it addresses the limitation of the video detection model, which was unable to accurately locate aggressive sheep. In real-time, our improved model successfully detects aggressive behavior among group sheep.

Uncertainty-driven trustworthy defect detection for high-resolution powder bed images in selective laser melting

Selective laser melting (SLM) is known as one of the most promising metal additive manufacturing technologies, and how to ensure its consistent quality is still a main challenge, which urgently needs to be addressed. The metal powder spreading quality, as the first stage of SLM, has a direct impact on the subsequent forming and it is necessary to be monitored. However, existing deep learning-based methods for monitoring powder spreading quality often suffer from the problem of unreliability. The high-resolution property of powder bed images is often ignored, and the predicted results are not well evaluated. To address the above issues and achieve trustworthy defect detection, this paper proposes an uncertainty-driven trustworthy defect detection method for high-resolution powder bed images in SLM. A super resolution module based on ISDNet is first proposed to refine the upsampling process. Checkpoint ensemble is adopted for better achieving model uncertainty estimation only requiring a single training process, feature reuse helps reduce the computational load, and temperature scaling is used to further calibrate each ensemble member to get a more precise uncertainty. Besides, we design an uncertainty-driven model improvement method to further improve defect detection performance for regions with high uncertainty. Experiments demonstrate the effectiveness of our proposed method, and our work achieves a trustworthy defect detection for high-resolution powder bed images in SLM.

Comparison of the Efficiency of Hyperspectral and Pulse Amplitude Modulation Imaging Methods in Pre-Symptomatic Virus Detection in Tobacco Plants.

Early detection of pathogens can significantly reduce yield losses and improve the quality of agricultural products. This study compares the efficiency of hyperspectral (HS) imaging and pulse amplitude modulation (PAM) fluorometry to detect pathogens in plants. Reflectance spectra, normalized indices, and fluorescence parameters were studied in healthy and infected areas of leaves. Potato virus X with GFP fluorescent protein was used to assess the spread of infection throughout the plant. The study found that infection increased the reflectance of leaves in certain wavelength ranges. Analysis of the normalized reflectance indices (NRIs) revealed indices that were sensitive and insensitive to infection. NRI700/850 was optimal for virus detection; significant differences were detected on the 4th day after the virus arrived in the leaf. Maximum (Fv/Fm) and effective quantum yields of photosystem II (ΦPSII) and non-photochemical fluorescence quenching (NPQ) were almost unchanged at the early stage of infection. ΦPSII and NPQ in the transition state (a short time after actinic light was switched on) showed high sensitivity to infection. The higher sensitivity of PAM compared to HS imaging may be due to the possibility of assessing the physiological changes earlier than changes in leaf structure.