Intensity Inhomogeneity Research Articles

Inhomogeneous MHz-scale intensity noise in double-pass fiber amplifiers

Abstract Here we report about the first demonstration of periodic MHz-scale inhomogeneous relative intensity noise (RIN) induced in double-pass fiber amplifiers. An Er:Yb-doped double-pass fiber amplifier operating near 1570.2 nm and stable continuous-wave single-frequency radiation source are used for the demonstration. The periodic structure of RIN has typical period of units of MHz observed in the range of 0–50 MHz and is formed in the radio-frequency spectrum of the amplified signal. It was found that the key factor influencing period of the RIN structure is spacing between the active fiber and a reflector forming the double-pass amplifier configuration. The population dynamics gratings formed in the active fiber are considered to be responsible for the formation of the periodic RIN structure. Here we investigate an influence of various factors on the period and positions of the RIN structure maxima. The results obtained can be used for designing single-frequency double-pass fiber amplifiers.

Medical Image Enhancement using Histogram Processing and Feature Extraction of Cancer Classification

Magnetic Resonance Imaging (MRI) is a critical tool in medical diagnostics, particularly for cancer detection and classification. However, the quality of MRI images often suffers from noise, low contrast, and intensity inhomogeneity, which can hinder accurate diagnosis. Image enhancement techniques, such as histogram processing, play a crucial role in improving image quality by enhancing contrast and emphasizing important fea- tures. This paper explores the application of histogram processing for MRI image enhancement, focusing on contrast enhancement and noise reduction techniques. The enhanced images are subsequently processed for feature extraction, which is crucial for the accurate classification of cancerous tissues. Key features such as texture, shape, and intensity are extracted to distinguish between malignant and be- nign tumors. The classification process utilizes machine learning algorithms, which are trained on the extracted features to achieve high accuracy in cancer classification. The proposed method demonstrates significant improvement in image quality, leading to better feature extraction and more accurate cancer diagnosis. This approach ultimately aids in early detection and improved treatment planning for patients. Index Terms—Image enhancement, Histogram processing, Seg- mentation, Feature extraction, SVM classifier.

An Integrated Framework for Infectious Disease Control Using Mathematical Modeling and Deep Learning.

Infectious diseases are a major global public health concern. Precise modeling and prediction methods are essential to develop effective strategies for disease control. However, data imbalance and the presence of noise and intensity inhomogeneity make disease detection more challenging. Goal: In this article, a novel infectious disease pattern prediction system is proposed by integrating deterministic and stochastic model benefits with the benefits of the deep learning model. Results: The combined benefits yield improvement in the performance of solution prediction. Moreover, the objective is also to investigate the influence of time delay on infection rates and rates associated with vaccination. Conclusions: In this proposed framework, at first, the global stability at disease free equilibrium is effectively analysed using Routh-Haurwitz criteria and Lyapunov method, and the endemic equilibrium is analysed using non-linear Volterra integral equations in the infectious disease model. Unlike the existing model, emphasis is given to suggesting a model that is capable of investigating stability while considering the effect of vaccination and migration rate. Next, the influence of vaccination on the rate of infection is effectively predicted using an efficient deep learning model by employing the long-term dependencies in sequential data. Thus making the prediction more accurate.

Advanced Hybrid Brain Tumor Segmentation in MRI: Elephant Herding Optimization Combined with Entropy-Guided Fuzzy Clustering

Accurate and early detection of brain tumors is essential for improving clinical outcomes and guiding effective treatment planning. Traditional segmentation techniques in MRI often struggle with challenges such as noise, intensity variations, and complex tumor morphologies, which can hinder their effectiveness in critical healthcare scenarios. This study proposes an innovative hybrid methodology that integrates advanced metaheuristic optimization and entropy-based fuzzy clustering to enhance segmentation precision in brain tumor detection. This method combines the nature-inspired Elephant Herding Optimization (EHO) algorithm with Entropy-Driven Fuzzy C-Means (EnFCM) clustering, offering significant improvements over conventional methods. EHO is utilized to optimize the clustering process, enhancing the algorithm’s ability to delineate tumor boundaries, while entropy-based fuzzy clustering accounts for intensity inhomogeneity and diverse tumor characteristics, promoting more consistent and reliable segmentation results. This approach was evaluated using the BraTS challenge dataset, a benchmark in the field of brain tumor segmentation. The results demonstrate marked improvements across several performance metrics, including Dice similarity, mean squared error (MSE), peak signal-to-noise ratio (PSNR), and the Tanimoto coefficient (TC), underscoring this method’s robustness and segmentation accuracy. By managing image noise and reducing computational demands, the EHO-EnFCM approach not only captures intricate tumor structures but also facilitates efficient image processing, making it suitable for real-time clinical applications. Overall, the findings reveal the potential of this hybrid approach to advance MRI-based tumor detection, offering a promising tool that enhances both accuracy and computational efficiency for medical imaging and diagnosis.

A Multi-Source Circular Geodesic Voting Model for Image Segmentation.

Image segmentation is a crucial task in artificial intelligence fields such as computer vision and medical imaging. While convolutional neural networks (CNNs) have achieved notable success by learning representative features from large datasets, they often lack geometric priors and global object information, limiting their accuracy in complex scenarios. Variational methods like active contours provide geometric priors and theoretical interpretability but require manual initialization and are sensitive to hyper-parameters. To overcome these challenges, we propose a novel segmentation approach, named PolarVoting, which combines the minimal path encoding rich geometric features and CNNs which can provide efficient initialization. The introduced model involves two main steps: firstly, we leverage the PolarMask model to extract multiple source points for initialization, and secondly, we construct a voting score map which implicitly contains the segmentation mask via a modified circular geometric voting (CGV) scheme. This map embeds global geometric information for finding accurate segmentation. By integrating neural network representation with geometric priors, the PolarVoting model enhances segmentation accuracy and robustness. Extensive experiments on various datasets demonstrate that the proposed PolarVoting method outperforms both PolarMask and traditional single-source CGV models. It excels in challenging imaging scenarios characterized by intensity inhomogeneity, noise, and complex backgrounds, accurately delineating object boundaries and advancing the state of image segmentation.

Oxygen isotope identity of the Earth and Moon with implications for the formation of the Moon and source of volatiles

The Moon formed 4.5 Ga ago through a collision between proto-Earth and a planetesimal known as Theia. The compositional similarity of Earth and Moon puts tight limits on the isotopic contrast between Theia and proto-Earth, or it requires intense homogenization of Theia and proto-Earth material during and in the aftermath of the Moon-forming impact, or a combination of both. We conducted precise measurements of oxygen isotope ratios of lunar and terrestrial rocks. The absence of an isotopic difference between the Moon and Earth on the sub-ppm level, as well as the absence of isotope heterogeneity in Earth's upper mantle and the Moon, is discussed in relation to published Moon formation scenarios and the collisional erosion of Theia's silicate mantles prior to colliding with proto-Earth. The data provide valuable insights into the origin of volatiles in the Earth and Moon as they suggest that the water on the Earth may not have been delivered by the late veneer. The study also highlights the scientific value of samples returned by space missions, when compared to analyses of meteorite material, which may have interacted with terrestrial water.

Assessment of Regional Brain Volume Measurements with Different Brain Extraction and Bias Field Correction Methods in Neonatal MRI

Proper selection and application of preprocessing steps are crucial for obtaining accurate segmentation in brain Magnetic Resonance Imaging (MRI). The aim of this study is to evaluate the impact brain extraction (BE) and bias field correction (BFC) methods have on regional brain volume (RBV) measurements of preterm neonates’ T2w MRI at term-equivalent age (TEA). Five BE methods (Manual, BET2, SWS, HD-BET, SynthStrip) were applied together with two BFC methods (SPM-BFC and N4ITK), before segmenting the neonatal brain into eight tissue classes (cortical grey matter, white matter, cerebral spinal fluid, deep nuclear grey matter, hippocampus, amygdala, cerebellum, and brainstem) using an automated segmentation software (MANTiS). Quantitative assessments were conducted, including the coefficient of variation (CV), coefficient of joint variation (CJV), Dice coefficient (DC), and RBV. HD-BET, together with N4ITK, showed the highest performance (mean ± standard deviation) regarding CV of 0.047 ± 0.005 (white matter) and 0.070 ± 0.005 (grey matter), CJV of 0.662 ± 0.095, DC of 0.942 ± 0.063, and RBV without significant differences (except in the brainstem) from the manual segmentation. Therefore, such combination of methods is recommended for improved skull-stripping accuracy, intensity homogeneity, and reproducibility of RBV of T2w MRI at TEA.

Intensity inhomogeneity correction in brain MRI: a systematic review of techniques, current trends and future challenges

Intensity inhomogeneity correction in brain MRI: a systematic review of techniques, current trends and future challenges

Superpixel-based C-SVC for Brain Tissue Classification in MRI Scans

Accurate identification of brain tissue is an ill-posed problem due to the inhomogeneous intensity and the extremely complicated and irregular border between endocrine tissues. This study introduces a superpixel-based approach to brain tissue classification in MRI scans. The proposed approach starts with image smoothing and feature highlighting, followed by image splitting based on the SLIC superpixel and merging strategy. Then, distinct superpixel-based appearance and boundary features are extracted and refined by minimizing redundancy and maximizing relevance technique before sending to the C-support vector classifier. Finally, a refinement step is adopted based on morphological characteristics and the distance regularized level set evolution model to modify the matter contour. The proposed approach was evaluated and compared with ten existing algorithms using the publicly accessible IBSR dataset. The experimental results show the better efficiency of the proposed approach in delimiting the contour of each matter than the other approaches in the literature.

Automated Visceral Adipose Tissue Segmentation and Quantification from Abdominal MRI using an Enhanced U-Net and Region-growing

The study focuses on enhancing the accuracy and reliability of visceral adipose tissue (VAT) segmentation and quantification from abdominal MRI images. Accurate segmentation of VAT is crucial for assessing obesity-related health risks, as traditional methods struggle with irregular shapes and varying intensities. The research utilizes a methodology consisting of three key modules: homomorphic filtering for intensity inhomogeneity correction, a U-Net architecture with attention mechanisms for primary segmentation, and a region-growing algorithm for refining segmentation. Homomorphic filtering effectively separates bias fields, enhancing image quality by transforming multiplicative artifacts into additive ones and removing them with high-pass filtering. This process ensures precise segmentation by maintaining high-frequency anatomical details. The U-Net model incorporates attention mechanisms and skip connections to focus on VAT regions, utilizing both local and global image contexts.The Combined Healthy Abdominal Organ Segmentation (CHAOS) challenge dataset and the Cancer Imaging Archive (TCIA) dataset are used to train and evaluate the model. It achieves a Dice Similarity Coefficient (DSC) of up to 0.985 on the CHAOS dataset and 0.972 on the TCIA dataset, outperforming existing methods in terms of segmentation accuracy. The region-growing algorithm further refines the segmentation by expanding VAT regions from high-confidence seed points, ensuring accurate boundary delineation and reducing noise. The study's results, evaluated using k-fold cross-validation, show that the proposed methodology significantly improves VAT segmentation efficiency, achieving a median DSC of 0.96 for the CHAOS dataset and 0.95 for the TCIA dataset in the most comprehensive experimental scenario. Comparative analysis indicates that the proposed approach outperforms other models, with higher sensitivity and specificity values, highlighting its potential for clinical applications in obesity management..

Refined speckle contrast estimation in OCT based on compensation of scattering-related distortions of speckle pattern parameters

Abstract Speckle contrast (SC) parameters in optical coherence tomography (OCT) scans are formed by the interplay of several factors—local level of optical wave backscattering by the material inhomogeneities, parameters of spatial distribution of the latter and the degree of cumulative optical wave attenuation during its fourth-and-back propagation. For the optical wavelengths used in OCT, this attenuation is usually dominated by the influence of scattering in the visualized turbid tissues rather than by absorption. For sufficiently high concentrations of scatterers (at least a few scatterers in the coherence volume) characterized by comparable scattering strengths and a fairly homogeneous distribution in space, the interference of locally scattered waves should be characterized by a Rayleigh distribution of speckle amplitudes, for which the local SC tends to 0.52. Equivalently, in terms of speckle intensities, the local SC tends to unity. Local spatial inhomogeneities or strongly uneven scattering strengths lead to the appearance of increased values of SC, which may serve as a diagnostic feature of some tissue components in OCT images. At the same time, in comparison to surface speckles formed by coherent-light scattering from rough surfaces, OCT-beam attenuation during its fourth-and-back propagation may introduce intensity inhomogeneities in OCT scans even for homogeneous tissue areas. This effect results in artefactual distortion of the visible SC in comparison with the above-mentioned expected value. Moreover, the presence of individual strong scatterers may additionally non-locally distort SC values due to the appearance of elongated shadows below such scatterers, which causes lateral inhomogeneities in OCT scans. Here, we propose a refined SC parameter, which is cleaned from distorting scattering-related effects in both axial and lateral directions. Depth-resolved estimation of the optical attenuation coefficient is used to restore attenuation-free OCT scans, for which the refined SC is estimated. The efficiency of the proposed approach is demonstrated using both digital OCT phantoms with highly controlled properties and experimental OCT data.

Development of a protocol for whole-lung in vivo lung perfusion-assisted photodynamic therapy using a porcine model.

Standard treatments for isolated lung metastases remain a clinical challenge. In vivo lung perfusion technique provides flexibility to overcome the limitations of photodynamic therapy (PDT) by replacing the blood with acellular perfusate, allowing greater light penetration. Using Monte Carlo-based simulations, we will evaluate the abilities of a light delivery system to irradiate the lung homogenously. Afterward, we aim to demonstrate the feasibility and safety profile of a whole-lung perfusion-assisted PDT protocol using 5-ALA and Chlorin e6. A porcine model of a simplified lung perfusion procedure was used. PDT was performed at 630 or 660nm with 5-ALA or Chlorin e6, respectively. Light fluence rate measurements and computed tomography (CT) scan segmentations were used to create in silico models of light propagation. Physiologic, gross, CT, and histological assessment of lung toxicity was performed 72h post-PDT. Dose-volume histograms showed homogeneity of light intensity throughout the lung. Predicted and measured fluence rates showed strong reliability. The photodynamic threshold of 5-ALA was , whereas Chlorin e6 showed negligible uptake in lung tissue. We lay the groundwork for personalized preoperative in silico dosimetry planning to achieve desired treatment volumes within the therapeutic range. Chlorin e6 demonstrated the greatest therapeutic potential, with a minimal uptake in healthy lung tissues.

A level-set method for fast image segmentation based on local pre-fitting and bilateral filtering

PurposeAlthough many conventional level-set approaches can be used for segmenting images containing factors such as noise and intensity inhomogeneities, they still can impact the accuracy of the results seriously. To solve this problem, a level-set method for fast image segmentation based on pre-fitting and bilateral filtering is proposed.Design/methodology/approachFirstly, an improved bilateral filter was investigated for image preprocessing. Secondly, by computing the local average intensity of the preprocessed enhanced picture, two local pre-fitting functions were defined. Thirdly, a new level-set energy functional was defined. Finally, a new distance regularized energy term based on the logarithmic and polynomial functions is proposed to evolve the level-set function in a smooth state.FindingsThe experimental results demonstrate that the proposed model has an excellent segmentation capability for images with noise and intensity inhomogeneities and has different degrees of performance improvement compared with the mainstream models.Originality/value(C1) An improved bilateral filter was investigated and integrated into the model. (C2) Proposing two local pre-fitting functions by computing the local average intensity of the preprocessed enhanced image. (C3) Proposing a new level-set energy functional. (C4) A new distance regularized energy term based on the logarithmic and polynomial functions is proposed to evolve the level set function in a smooth state. (C5) Analyzing and comparing the performance of the proposed model with other similar models.

Frequency shift caused by a magnetic field and light intensity inhomogeneity in an optically detected clock.

We present a novel, to the best of our knowledge, frequency shift mechanism in the optically detected atomic clock. This frequency shift is analogous to the light shift that is associated with a detecting light power. However, this shift arises from the inhomogeneity of the magnetic field (C-field) and the detecting light intensity. We call this shift the "pseudo-light shift" (p-LS). This shift allows the correlation between the clock output frequency and the detecting light power to switch between positive and negative, depending on the magnetic field. The mechanism is described and experimentally validated in our cesium beam clock through two experiments. The study of this frequency shift can enhance the accuracy of light shift assessments in atom-laser interaction systems and suppress long-term stability deterioration caused by the light power fluctuation.

OpenMAP-T1: A Rapid Deep-Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain.

This study introduces OpenMAP-T1, a deep-learning-based method for rapid and accurate whole-brain parcellation in T1-weighted brain MRI, which aims to overcome the limitations of conventional normalization-to-atlas-based approaches and multi-atlas label-fusion (MALF) techniques. Brain image parcellation is a fundamental process in neuroscientific and clinical research, enabling a detailed analysis of specific cerebral regions. Normalization-to-atlas-based methods have been employed for this task, but they face limitations due to variations in brain morphology, especially in pathological conditions. The MALF techniques improved the accuracy of the image parcellation and robustness to variations in brain morphology, but at the cost of high computational demand that requires a lengthy processing time. OpenMAP-T1 integrates several convolutional neural network models across six phases: preprocessing; cropping; skull-stripping; parcellation; hemisphere segmentation; and final merging. This process involves standardizing MRI images, isolating the brain tissue, and parcellating it into 280 anatomical structures that cover the whole brain, including detailed gray and white matter structures, while simplifying the parcellation processes and incorporating robust training to handle various scan types and conditions. The OpenMAP-T1 was validated on the Johns Hopkins University atlas library and eight available open resources, including real-world clinical images, and the demonstration of robustness across different datasets with variations in scanner types, magnetic field strengths, and image processing techniques, such as defacing. Compared with existing methods, OpenMAP-T1 significantly reduced the processing time per image from several hours to less than 90 s without compromising accuracy. It was particularly effective in handling images with intensity inhomogeneity and varying head positions, conditions commonly seen in clinical settings. The adaptability of OpenMAP-T1 to a wide range of MRI datasets and its robustness to various scan conditions highlight its potential as a versatile tool in neuroimaging.

A hybrid variational level set model based on double Gaussian distribution fitting energy for image segmentation and bias correction

AbstractThe variational level set model has been widely used in image segmentation. However, its performance is significantly hindered by bias fields and noise within the images. To address these limitations, we introduce a novel hybrid variational level set model based on dual Gaussian distribution fitting (DGDF) energy in this paper. The DGDF energy integrates both local and global Gaussian distributions. The local energy is derived from the original image, while the global energy employs the corrected image, enabling effective segmentation of images with intensity inhomogeneity. Furthermore, the model demonstrates low sensitivity to weighting parameters and robust performance for noisy images. We develop an alternating iteration algorithm that combines variational methods with gradient descent to efficiently solve the proposed model. Experimental results validate the effectiveness of the model and the algorithm. In addition, the proposed model shows competitiveness on test images and three datasets compared to several state‐of‐the‐art models, including other variational level set models and deep learning‐based techniques.

Improvements in Fast Mass Microscopy for Large-Area Samples.

Mass spectrometry imaging (MSI) is a technique that analyzes the chemical information and spatial distribution of surface analytes. Most MSI studies are conducted in microprobe mode, in which a mass spectrum is collected for each pixel to create a mass image. Thus, the spatial resolution, sample imaging area, and imaging speed are linked. In this mode, halving the pixel size quadruples the analytical time, which presents a practical limit on the high spatial resolution MSI throughput. Fast mass microscopy (FMM) is, in contrast, a microscope-mode MSI technique that decouples spatial resolution and imaging speed. FMM circumvents the linear-quadratic relationship of pixel size and analytical time, which enables increased imaging size area and the analytical speed achievable. In this study, we implement instrument modifications to the FMM system, including the addition of linear encoders that enable roughly 8.5× faster imaging than was previously achieved, allowing a 42.5 × 26 mm2 sample area to be imaged at a 1 μm pixel size in <4.5 min. Linear encoders also enable the alignment of multipass images that increase image homogeneity and signal intensity. The applicability of FMM to large area samples has made it important to define the tolerance to height variations of the technique, which was determined to be at least 218 ± 0.03 (n = 3) μm.

Restoration and Segmentation of Old Jawi Manuscripts using Variational Image Inpainting and Active Contour Models

Old Jawi Manuscripts (OJM) are crucial to historical studies, offering insights into past societies. However, degradation from mishandling and environmental factors can impair their legibility. To preserve OJM, image inpainting and segmentation are essential for restoring corrupted areas and identifying text. Recently, the Gaussian Regularization Segmentation (GRS) model has shown effectiveness in intensity inhomogeneity grayscale image segmentation, though it was not designed for corrupted OJM images. Therefore, this study aimed to reformulate the GRS model to restore and segment text from real corrupted OJM images. The methodology begins with the incorporation of the Mumford-Shah and Bertalmio inpainting models into the GRS model as new fitting terms, resulting in the Modified Gaussian Regularization Segmentation Mumford-Shah (MGRSM) model and the Modified Gaussian Regularization Segmentation Bertalmio (MGRSB) model, respectively. MATLAB was used to implement these models, and their performance was assessed on 30 corrupted OJM samples from Malay Ethnomathematics Research Group, with expert evaluations and efficiency measured by average elapsed time. The MGRSM model achieved 38.4 percent and 12.4 percent higher overall total scores from experts in terms of segmentation accuracy compared to the GRS and MGRSB models, respectively. While the GRS model is the fastest, the MGRSM model provides superior accuracy, with an average processing time of 9.35 seconds, making it the most optimal for restoring and segmenting OJM images. This approach not only enhances the preservation of historical manuscripts but also provides a practical tool for researchers and historians in safeguarding our cultural heritage.

A novel locally statistical active contour model for SAR image segmentation and faster algorithm

Abstract In this paper, a novel locally statistical active contour model (LACM) based on the Aubert-Aujol (AA) denoising model and variational level set method is proposed, which can be used for SAR image segmentation with intensity inhomogeneity. By simple transformation, a fast segmentation algorithm with a global optimization solver can be obtained. Experiments using some challenging synthetic images and Envisat SAR images demonstrate the superiority of our proposed models with respect to classic models. Further, we can find that the structure of the proposed algorithm is the same as that of the Transformer or RNN. We will study the nature of the above two types of algorithms to propose a more effective segmentation algorithm based on the energy model.

Non-radiative recombination centres in InGaN/GaN nanowires revealed by statistical analysis of cathodoluminescence intensity maps and electron microscopy

The methodology of statistical analysis of cathodoluminescence (CL) intensity mappings on ensembles of several hundreds of InGaN/GaN nanowires (NWs) used to quantify non-radiative recombination centres (NRCs) was validated on InGaN/GaN NWs exhibiting spatially homogeneous cathodoluminescence at the scale of single NWs. Cathodoluminescence intensity variations obeying Poisson's statistics were assigned to the presence of randomly incorporated point defects acting as NRCs. Additionally, another type of NRCs, namely extended defects leading to spatially inhomogeneous cathodoluminescence intensity at the scale of single InGaN/GaN NWs are revealed by high resolution scanning transmission electron microscopy, geometrical phase analysis and two-beam diffraction conditions techniques. Such defects are responsible for deviations from Poisson's statistics, allowing one to achieve a rapid evaluation of the crystallographic and optical properties of several hundreds of NWs in a single cathodoluminescence intensity mapping experiment.