Accurate Image Research Articles

Diagnostic Accuracy of Magnetic Resonance Imaging in the Diagnosis of Anterior Cruciate Ligament Injury taking Arthroscopy as Gold Standard

Objective: To determine the diagnostic accuracy of magnetic resonance imaging (MRI) in the diagnosis of anterior cruciate ligament injury, using arthroscopy as the gold standard. Methodology: It was a descriptive, cross-sectional study that included 154 patients with suspected anterior cruciate ligament injury from the Department of Orthopaedics, Imran Idrees Teaching Hospital, Sialkot. All patients underwent arthroscopy and the presence or absence of ACL (anterior cruciate ligament) injury was noted. MRI findings were compared with arthroscopy findings. Results: Among the 94 MRI-positive patients, 88 (True Positive) had ACL injury while 06 (False Positive) had no ACLinjury on arthroscopy. Out of 60 MRI-negative patients, 08 (False Negative) had ACL injury on arthroscopy while 52 (True Negative) had no ACL injury. Overall sensitivity, specificity, PPV (positive predictive value), NPV (negative predictive value) and DA (diagnostic accuracy) of MRI in the diagnosis of ACLinjury, taking arthroscopy as the gold standard was 91.67%, 89.66%, 83.62%, 86.67% and 90.91% respectively. Conclusion: The study concluded that MRI is a very sensitive and precise diagnostic modality. It has significantly improved the ability to diagnose ACL injuries, allowing for more accurate and reliable assessments. Additionally, the use of MRI has been instrumental in reducing the number of purely diagnostic arthroscopies, thereby minimizing the need for invasive procedures and enhancing patient care. Keywords: Knee injury, Magnetic Resonance Imaging, Arthroscopy, Anterior cruciate ligament.

A multi-frame fusion video deraining neural network based on depth and luminance features

Rainy weather is a common natural occurrence, but capturing clear and accurate images in rainy conditions can be challenging. Consequently, addressing the effects of rain on video imagery is essential for enhancing visual quality and the accuracy and reliability of computer vision applications. In this work, we introduce a novel rain model accompanied by a multi-frame fusion video rain removal algorithm that exploits depth and luminance features. We present an innovative three-stage video deraining methodology that synergizes multi-frame fusion with advanced neural network design to achieve highly realistic and clear rain removal. Our Option-Flow-Depth-Luminance (OFDL) Derain algorithm propels the research forward by offering a new perspective on rain removal. Incorporating depth and luminance features into image processing, we devise a novel rain model that allows the algorithm to handle complex environments and a variety of rain patterns effectively. Additionally, our model takes into account degradation factors such as rain streaks, accumulation, runoff, and occlusion, leading to the generation of more authentic rain-affected images that improve the training and performance evaluation of the model. Experimental tests conducted on the RainSynLight25, RainSynComplex25, and NTURain datasets demonstrate that our method outperforms current state-of-the-art techniques, achieving increases of 4.3 dB, 2.2 dB, and 2.2 dB in PSNR, and enhancements of 0.062, 0.109, and 0.008 in Structural Similarity Index Measure (SSIM), respectively. Moreover, our approach exhibits superior processing speed, further underscoring its practical advantages.

Evaluating the accuracy of hyperspectral and multispectral images in wetland cover classification using data mining models (Case study: Shadegan wetland)

Evaluating the accuracy of hyperspectral and multispectral images in wetland cover classification using data mining models (Case study: Shadegan wetland)

True cost estimation of common imaging procedures for cost-effectiveness analysis - insights from a Singapore hospital emergency department

ObjectivesThere is a lack of clear and consistent cost reporting for cost-effectiveness analysis in radiology. Estimates are often obtained using costing derived from hospital charge records. This study aims to evaluate the accuracy of hospital charge records compared to a Singapore hospital's true diagnostic imaging costs. MethodsA seven-step process involving a bottom-up micro-costing approach was devised and followed to calculate the cost of imaging using actual data from a clinical setting. We retrieved electronic data from a random sample of 96 emergency department patients who had CT brain, CT and X-ray cervical spine, and X-ray lumbar spine performed to calculate the parameters required for cost estimation. We adjusted imaging duration and number of performing personnel to account for variations. ResultsOur approach determined the average cost for the following imaging procedures: CT brain (€154.00), CT and X-ray cervical spine (€177.14 and €68.22), and X-ray lumbar spine (€79.85). We found that the true cost of both conventional radiography procedures was marginally higher than the subsidized patient charge, and all costs were slightly lower than the private patient charge except for X-ray lumbar spine (€73.49 vs.€79.85). We identified larger differences in cost for both CT procedures and smaller differences in cost for conventional radiography procedures, depending on the patient's private or subsidized status. For private status, the differences were: CT brain (Min: €194.20; Max: €264.40), CT cervical spine (Min: €219.54; Max: €399.05), X-ray cervical spine (Min: €5.27; Max: €61.94), and X-ray lumbar spine (Min: €6.36; Max: €108.04), while for subsidized status, the differences were: CT brain (Min: €7.56; Max: €62.64), CT cervical spine (Min: €47.02; Max: €132.49), X-ray cervical spine (Min: €15.88; Max: €103.44), and X-ray lumbar spine (Min: €13.66; Max: €149.44). Considering examination duration and the number of personnel engaged in a procedure, there were significant variations in the minimum, average, and maximum imaging costs. ConclusionThere is a modest gap between hospital charges and actual costs, and we must therefore exercise caution and recognize the limitations of utilizing hospital charge records as absolute metrics for cost-effectiveness analysis. Our detailed approach can potentially enable more accurate imaging cost determination for future studies.

Deep hashing and attention mechanism-based image retrieval of osteosarcoma scans for diagnosis of bone cancer

BackgroundDue to its intricate nature and substantial data size, microscopic image data of osteosarcoma often present a significant obstacle to the effectiveness of conventional image retrieval methods. Therefore, this study investigates a new approach for medical image retrieval using advanced deep hashing techniques and attention mechanisms to address these challenges more effectively. MethodThe proposed algorithm significantly improves osteosarcoma cell microscopic image retrieval efficiency and accuracy using deep hashing and attention mechanisms. Image preprocessing includes adaptive histogram equalization and dataset augmentation to enhance quality and diversity. Feature extraction employs the WRN-AM model to map high-dimensional features to a low-dimensional hash code space, improving retrieval efficiency. Finally, similarity matching via Hamming distance allows rapid and precise identification of similar images. ResultsThe study shows notable advancements: the WRN-AM model achieves 93.2% classification accuracy and 97.09% mAP using 64-bit hash codes. These findings underscore the technique’s effective performance in extracting and categorizing diverse microscopic cell data efficiently and reliably. ConclusionsThis innovative approach provides a robust solution for retrieving and classifying microscopic data of osteosarcoma cells and other cell types, speeding up clinical diagnosis and medical research. It facilitates quicker access and analysis of patient image data, enhancing diagnostic precision and treatment planning for healthcare professionals. Concurrently, it supports researchers in leveraging medical image data more efficiently, fostering progress and innovation in the medical field.

Advanced image reconstruction for electrostatic tomography in gas-solid two-phase flow based on convolutional autoencoder neural network

The image reconstruction of flowing charged particles in gas-solid two-phase (GSTP) flow can be achieved through electrostatic tomography (EST). Accurate image reconstruction is crucial for detecting the movement patterns of the particles. In order to improve the quality of reconstructed images, a unique convolutional autoencoder neural network (CANN) is proposed. This study uses an image set generated by the linear backprojection (LBP) algorithm to train the CANN, which consists of an encoder and a decoder. The encoder utilizes convolutional and max-pooling layers to reduce the dimensionality of the images and extract key features, while the decoder restores and reconstructs the images through up-sampling and convolutional operations to closely approximate the reference image. To prevent overfitting, dropout layers are introduced after each max-pooling layer in the encoder. To verify the anti-noise capability of the network, Gaussian white noise ranging from 10 dB to 20 dB is added to the test set. The proposed CANN has been validated through simulations and experiments, demonstrating its effectiveness in overcoming noticeable artifacts and noise in reconstructed images when identifying GSTP flow patterns. Furthermore, it shows significant enhancements in imaging outcomes compared to conventional image reconstruction techniques and some current deep learning algorithms.

Bridging real and simulated data for cross-spatial- resolution vegetation segmentation with application to rice crops

Accurate image segmentation is essential for image-based estimation of vegetation canopy traits, as it minimizes background interference. However, existing segmentation models often lack the generalization ability to effectively tackle both ground-based and aerial images across a wide range of spatial resolutions. To address this limitation, a cross-spatial-resolution image segmentation model for rice crop was trained using the integration of in-situ and in silico multi-resolution images. We collected more than 3,000 RGB images (real set) covering 17 different resolutions reflecting diverse canopy structures, illumination conditions and background in rice fields, with vegetation pixels annotated manually. Using the previously developed Digital Plant Phenotyping Platform, we created a simulated dataset (sim set) including 10,000 RGB images with resolutions ranging from 0.5 to 3.5 mm/pixel, accompanied by corresponding mask labels. By employing a domain adaptation technique, the simulated images were further transformed into visually realistic images while preserving the original labels, creating a simulated-to-realistic dataset (sim2real set). Building upon a SegFormer deep learning model, we demonstrated that training with multi-resolution samples led to more generalized segmentation results than single-resolution training on the real dataset. Our exploration of various integration strategies revealed that a training set of 9,600 sim2real images combined with only 60 real images achieved the same segmentation accuracy as 2,400 real images (IoU = 0.819, F1 = 0.901). Moreover, combining 2,400 real images and 1,200 sim2real images resulted in the best performing model, effective against six challenging situations, such as specular reflections and shadows. Compared with models trained with single-resolution samples and an established model (i.e., VegANN), our model effectively improved the estimation of both green fraction and green area index across spatial resoultions. The strategy of bridging real and simulated data for cross-resolution deep learning model is expected to be applicable to other crops. The best trained model is available at https://github.com/PheniX-Lab/crossGSD-seg.

Hybrid improved fuzzy C-means and watershed segmentation to classify Alzheimer’s using deep learning

Brain damage and deficits in interactions among brain cells are the primary causes of dementia and Alzheimer’s disease (AD). Despite ongoing research, no effective medications have yet been developed for these conditions. Therefore, early detection is crucial for managing the progression of these disorders. In this study, we introduce a novel tool for detecting AD using non invasive medical tests, such as magnetic resonance imaging (MRI). Our method employs fuzzy C-means clustering to identify features that enhance image accuracy. The standard fuzzy C-means algorithm has been augmented with fuzzy components to improve clustering performance. This enhanced approach optimizes segmentation by extracting image information and utilizing a sliding window to calculate center coordinates and establish a stable group matrix. These critical features are subsequently integrated with a two-phase watershed segmentation process. The resulting segmented images are then used to train an optimal convolutional neural network (CNN) for AD classification. Our methodology demonstrated a 98.20% accuracy rate in the detection and classification of segmented MRI brain images, highlighting its efficacy in identifying disease types.

Advancements in phasor-based FLIM: multi-component analysis and lifetime probes in biological imaging.

Fluorescence lifetime imaging microscopy (FLIM) is a reliable method that achieves imaging by detecting fluorescence lifetimes within samples. Owing to its unique temporal characteristic, it can complement fluorescence intensity measurement. Technological and methodological advancements in FLIM have broadened its applications across various domains. The processing of fluorescence lifetime data is crucial for enhancing the speed and accuracy of imaging. Thus, various lifetime fitting algorithms have been developed to improve the imaging speed. The phasor analysis (PA) method is an approach for processing fluorescence lifetime data, capable of directly converting lifetime signals into visual graphics without fitting, which outperforms traditional approaches in speed. Furthermore, lifetime probes with distinct lifetimes are readily implemented for visualization and cluster analysis combined with PA, facilitating the prediction of specific biological states or functions. This review examines various lifetime probes employed in phasor-based FLIM and discusses their roles in the PA method. The methods for multi-component PA within complex biological environments were also described. Additionally, we focused on the advantages of the phasor vector rule and the unmixing of multi-component analysis based on PA. The integration of lifetime probes with phasor-based FLIM facilitates rapid and intuitive detection methods for analyzing complex biological environments.

131I Accumulation Due to an Intrauterine Device in a Papillary Thyroid Carcinoma Patient.

A 46-year-old woman underwent 131I radiotherapy following thyroidectomy for papillary thyroid carcinoma. Posttherapeutic 131I scintigraphy was performed 4 days later. Both attenuation-corrected and non-attenuation-corrected 131I tomography images elucidated an intrauterine device with avid 131I uptake in her uterus. This case illustrated that an intrauterine device should be considered as a potential cause of false-positive results in posttherapeutic 131I scintigraphy, highlighting the need for accurate image interpretation to avoid unnecessary treatment.

Regulating Electron Acceptor Unit to Construct Conjugated Polymer Probe for Raman Imaging of Tumor and Sentinel Lymph Nodes.

Donor-acceptor (D-A) conjugated polymers have large Stokes shifts, high photostability, and good biocompatibility and thus are ideal for use as Raman probes in vivo. However, few D-A conjugated polymers are used as Raman probes for Raman imaging due to their weak Raman signal intensity and intrinsic fluorescence interference. Here, we developed a D-A conjugated polymer probe (CDT-TT) containing alternating cyclopentadithiophene-thienothiophene units for Raman imaging of tumor and sentinel lymph nodes (SLNs). The CDT-TT shows a strong Raman signal at 1389 cm-1 without fluorescence and lipid background signal interference under 785 nm near-infrared light. Moreover, the CDT-TT loaded nanoparticles realized the accurate imaging of tumor cells and tumor tissues. In addition, a high-resolution margin imaging of in situ SLNs was acquired. Taken together, the established method is effective for accurate Raman detection of tumors and SLNs, which may shed new light on the development of D-A polymer Raman probes for clinical imaging.

Fractional flow reserve measurement using dynamic CT perfusion imaging in patients with coronary artery disease

Fractional flow reserve measurement using dynamic CT perfusion imaging in patients with coronary artery disease

Sensitivity Analysis of Sentinel-2 Imagery to Assess Urban Tree Functional Traits: A Physical Approach Based on Local Climate Zones

Urban trees contribute to urban well-being but face challenging environments that can reduce their lifespan and increase young tree mortality. Although many studies have used remote sensing data to monitor the functional status of trees in rural areas, few have done so in urban areas to assess the health or estimate the biomass of large green areas. This study assessed the suitability of using Sentinel-2 images to characterize two urban tree functional traits—leaf chlorophyll content (Cab) and leaf area density (LAD)—in isolated trees and tree rows. Simulated Sentinel-2 images were generated using the DART radiative transfer model, considering 16 tree-endogenous and 14 tree-exogenous parameters, with 15 vegetation indices (VIs) analyzed. Sensitivity analysis was performed in four contrasting urban environments using local climate zone taxonomy. The accuracy of the simulated images was validated with real Sentinel-2 images, field measurements, and ancillary data collected for four tree species in Rennes, France. The results showed that the tree parameters significantly influenced Sentinel-2 spectral bands, with NGBDI and OSAVI VIs being most sensitive to Cab and LAD. The model showed high accuracy, with a mean RMSE of 0.016 for key spectral bands. The results also highlighted the importance of considering ancillary data to capture specific urban characteristics.

Deep learning‐based inversion with discrete cosine transform discretization for two‐dimensional basement relief imaging of sedimentary basins from observed gravity anomalies

AbstractSedimentary basins, integral to Earth's geological history and energy resource exploration, undergo complex changes driven by sedimentation, subsidence and geological processes. Gravity anomaly inversion is a crucial technique offering insights into subsurface structures and density variations. Our study addresses the challenge of complex subsurface structure assessment by leveraging deep neural networks to invert observed gravity anomalies. Optimization approaches traditionally incorporate known density distributions obtained from borehole data or geological logging for inverting basement depth in sedimentary basins using observed gravity anomalies. Our study explores the application of deep neural networks in accurate architectural assessment of sedimentary basins and demonstrates their significance in mineral and hydrocarbon exploration. Recent years have witnessed a surge in the use of machine learning in geophysics, with deep learning models playing a pivotal role. Integrating deep neural networks, such as the feedforward neural networks, has revolutionized subsurface density distribution and basement depth estimation. This study introduces a deep neural network specifically tailored for inverting observed gravity anomalies to estimate two‐dimensional basement relief topographies in sedimentary basins. To enhance computational efficiency, a one‐dimensional discrete cosine transform based discretization approach is employed. Synthetic data, generated using non‐Gaussian fractals, compensates for the scarcity of true datasets for training the deep neural network model. The algorithm's robustness is validated through noise introduction with comparisons against an efficient and traditional global optimization‐based approach. Gravity anomalies of real sedimentary basins further validate the algorithm's efficacy, establishing it as a promising methodology for accurate and efficient subsurface imaging in geological exploration.

BREAST LESION SEGMENTATION AND CLASSIFICATION USING U-NET SALIENCY ESTIMATION AND EXPLAINABLE RESIDUAL CONVOLUTIONAL NEURAL NETWORK

Breast cancer (BrC) is one of the most common causes of death among women worldwide. Images of the breast (mammography or ultrasound) may show an anomaly that represents early indicators of BrC. However, accurate breast image interpretation necessitates labor-intensive procedures and highly skilled medical professionals. As a second opinion for the physician, deep learning (DL) tools can be useful for the diagnosis and classification of malignant and benign lesions. However, due to the lack of interpretability of DL algorithms, it is not easy to understand by experts as to how to predict a label. In this work, we proposed multitask U-Net Saliency estimation and DL model-based breast lesion segmentation and classification using ultrasound images. A new contrast enhancement technique is proposed to improve the quality of original images. After that, a new technique was proposed called UNET-Saliency map for the segmentation of breast lesions. Simultaneously, a MobileNetV2 deep model is fine-tuned with additional residual blocks and trained from scratch using original and enhanced images. The purpose of additional blocks is to reduce the number of parameters and better learning of ultrasound images. Training is performed from scratch and extracted features from the deeper layers of both models. In the later step, a new cross-entropy controlled sine-cosine algorithm is developed and selected best features. The main purpose of this step is the reduction of irrelevant features for the classification phase. The selected features are fused in the next step by employing a serial-based Manhattan Distance (SbMD) approach and classified the resultant vector using machine learning classifiers. The results indicate that a wide neural network (W-NN) obtained the highest accuracy of 98.9% and sensitivity rate of 98.70% on the selected breast ultrasound image dataset. The comparison of the proposed method accuracy is conducted with state-of-the-art (SoArt) techniques which show the improved performance.

Enhancing Binary Convolutional Neural Networks for Hyperspectral Image Classification

Hyperspectral remote sensing technology is swiftly evolving, prioritizing affordability, enhanced portability, seamless integration, sophisticated intelligence, and immediate processing capabilities. The leading model for classifying hyperspectral images, which relies on convolutional neural networks (CNNs), has proven to be highly effective when run on advanced computing platforms. Nonetheless, the high degree of parameterization inherent in CNN models necessitates considerable computational and storage resources, posing challenges to their deployment in processors with limited capacity like drones and satellites. This paper focuses on advancing lightweight models for hyperspectral image classification and introduces EBCNN, a novel binary convolutional neural network. EBCNN is designed to effectively regulate backpropagation gradients and minimize gradient discrepancies to optimize BNN performance. EBCNN incorporates an adaptive gradient scaling module that utilizes a multi-scale pyramid squeeze attention (PSA) mechanism during the training phase, which can adjust training gradients flexibly and efficiently. Additionally, to address suboptimal training issues, EBCNN employs a dynamic curriculum learning strategy underpinned by a confidence-aware loss function, Superloss, enabling progressive binarization and enhancing its classification effectiveness. Extensive experimental evaluations conducted on five esteemed public datasets confirm the effectiveness of EBCNN. These analyses highlight a significant enhancement in the classification accuracy of hyperspectral images, achieved without incurring additional memory or computational overheads during the inference process.

Comparison of X-Ray Absorption in Mandibular Tissues and Tissue-Equivalent Polymeric Materials Using PHITS Monte Carlo Simulations

This study investigates the absorption of X-rays in mandibular tissues by comparing real tissues with tissue-equivalent materials using the PHITS Monte Carlo simulation program. The simulation was conducted over a range of X-ray photon energies from 50 to 100 keV, with increments of 5 keV, to evaluate the dose absorbed by different tissues. Real tissues, such as the skin, parotid gland, and masseter muscle, were compared with their tissue-equivalent polymeric materials, including PMMA, Parylene N, and Teflon. The results showed that the real tissues generally absorbed more X-rays than their corresponding equivalents, especially at lower energy levels. For instance, at 50 keV, differences in the absorbed doses reached up to 50% for the masseter muscle and its equivalent, while this gap narrowed at higher energies. The study highlights the limitations of current tissue-equivalent materials in accurately simulating real tissue behavior, particularly in low-energy X-ray applications. These discrepancies suggest that utilizing tissue-equivalent materials may lead to less accurate medical imaging and radiotherapy dose calculations. Future research should focus on improving tissue-equivalent materials and validating simulation results with experimental data to ensure more reliable dosimetric outcomes. This study provides a foundation for refining radiation dose calculations and improving patient safety in clinical applications involving X-rays.

CONE BEAM COMPUTED TOMOGRAPHY AND ARTIFICIAL INTELLIGENCE. ¿WHERE WE ARE?

In recent years we have seen a huge number of papers presenting the different possibilities for artificial intelligence (AI) in dentistry. Within the next decade, AI will transform the workflow of modern dental practice. Additionally, AI tools can enable healthcare professionals to increase their reliability, effectiveness, and usefulness, nonetheless potential limitations and errors. (1)Systematic reviews concerning artificial intelligence for radiographic imaging detection of caries lesions concluded that AI-based models exhibit good diagnostic performance. AI provides comprehensive, reliable, and accurate image assessment and disease detection, thus facilitating effective and efficient diagnosis and improving the prognosis of dental caries in periapical radiographs. However, the various AI models come with their own set of pros and cons, so the selection of a model should be based on the specific task objectives and requirements. (2, 3) Accordingly, research projects should be meticulously designed to detect the trueperformance of AI models.

MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with Application in Colonic Polyp Image Segmentation

Accurate polyp image segmentation is of great significance, because it can help in the detection of polyps. Convolutional neural network (CNN) is a common automatic segmentation method, but its main disadvantage is the long training time. Transformer is another method that can be adapted to the automatic segmentation method by employing a self-attention mechanism, which essentially assigns different importance weights to each piece of information, thus achieving high computational efficiency during segmentation. However, a potential drawback with Transformer is the risk of information loss. The study reported in this paper employed the well-known hybridization principle to propose a method to combine CNN and Transformer to retain the strengths of both. Specifically, this study applied this method to the early detection of colonic polyps and to implement a model called MugenNet for colonic polyp image segmentation. We conducted a comprehensive experiment to compare MugenNet with other CNN models on five publicly available datasets. An ablation experiment on MugenNet was conducted as well. The experimental results showed that MugenNet can achieve a mean Dice of 0.714 on the ETIS dataset, which is the optimal performance on this dataset compared to other models, with an inference speed of 56 FPS. The overall outcome of this study is a method to optimally combine two methods of machine learning which are complementary to each other.

Hardware-Feasible and Efficient N-Type Organic Neuromorphic Signal Recognition via Reservoir Computing.

Organic electrochemical synaptic transistors (OESTs), inspired by the biological nervous system, have garnered increasing attention due to their multifunctional applications in neuromorphic computing. However, the practical implementation of OESTs for signal recognition-particularly those utilizing n-type organic mixed ionic-electronic conductors (OMIECs)-still faces significant challenges at the hardware level. Here, a state-of-the-art small-molecule n-type OEST integrated within a physically simple and hardware feasible reservoir-computing (RC) framework for practical temporal signal recognition is presented. This integration is achieved by leveraging the adjustable synaptic properties of the n-OEST, which exhibits tunable nonlinear short-term memory, transitioning from volatility to nonvolatility, and demonstrating adaptive temporal specificity. Additionally, the nonvolatile OEST offers 256 conductance levels and a wide dynamic range (≈147) in long-term potentiation/depression (LTP/LTD), surpassing previously reported n-OESTs. By combining volatile n-OESTs as reservoirs with a single-layer perceptron readout composed of nonvolatile n-OEST networks, this physical RC system achieves substantial recognition accuracy for both handwritten-digit images (94.9%) and spoken digit (90.7%), along with ultrahigh weight efficiency. Furthermore, this system demonstrates outstanding accuracy (98.0%) by grouped RC in practical sleep monitoring, specifically in snoring recognition. Here, a reliable pathway for OMIEC-driven computing is presented to advance bioinspired hardware-based neuromorphic computing in the physical world.