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1

Mohd Noh, Zarina, Abdul Rahman Ramli, Marsyita Hanafi, M. Iqbal Saripan, and Ridza Azri Ramlee. "Palm Vein Pattern Visual Interpretation Using Laplacian and Frangi-Based Filter." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (2018): 578. http://dx.doi.org/10.11591/ijeecs.v10.i2.pp578-586.

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<span lang="EN-US">Detection of palm vein pattern through image processing techniques is an open problem as performance of each technique is closely related to the sample image gathered for the processing. The detected palm vein pattern is useful for further analysis in biometrics application and medical purpose. This paper aims to investigate the application of Laplacian filter and Frangi-based filter in detecting vein pattern contained in a near infrared illuminated palm image. Both filtering techniques are applied independently to two palm image databases to compare their performance in translating vein pattern in the image visually. Through empirical study, it is observed that Laplacian filter can translate the vein pattern in the image effectively. But pre-processings involved before the application of Laplacian filter need to be performed to accurately translate the vein pattern. The implementation of Frangi-based filter, while simplifying the detection process without the need of extra pre-processing, resulted in only certain vein pattern detected. Using pixel-by-pixel objective assessment, the rate for Laplacian filter in detecting vein pattern are generally more than 85% compared to Frangi-based filter; where it ranges from 60% to 100%.</span>
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Zarina, Mohd Noh, Rahman Ramli Abdul, Hanafi Marsyita, Iqbal Saripan M., and Azri Ramlee Ridza. "Palm Vein Pattern Visual Interpretation Using Laplacian and Frangi-Based Filter." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (2018): 578–86. https://doi.org/10.11591/ijeecs.v10.i2.pp578-586.

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Detection of palm vein pattern through image processing techniques is an open problem as performance of each technique is closely related to the sample image gathered for the processing. The detected palm vein pattern is useful for further analysis in biometrics application and medical purpose. This paper aims to investigate the application of Laplacian filter and Frangibased filter in detecting vein pattern contained in a near infrared illuminated palm image. Both filtering techniques are applied independently to two palm image databases to compare their performance in translating vein pattern in the image visually. Through empirical study, it is observed that Laplacian filter can translate the vein pattern in the image effectively. But preprocessings involved before the application of Laplacian filter need to be performed to accurately translate the vein pattern. The implementation of Frangi-based filter, while simplifying the detection process without the need of extra pre-processing, resulted in only certain vein pattern detected. Using pixel-by-pixel objective assessment, the rate for Laplacian filter in detecting vein pattern are generally more than 85% compared to Frangi-based filter; where it ranges from 60% to 100%.
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Yang, Jiang. "Bridge Crack Detection Algorithm Based on Bilateral-frangi Filter." Journal of Physics: Conference Series 2023, no. 1 (2021): 012044. http://dx.doi.org/10.1088/1742-6596/2023/1/012044.

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Abstract The crack detection algorithm based on Bilateral-Frangi filtering can effectively reduce the influence of noise on the crack detection results at the bottom of the bridge.This algorithm effectively removes noise while enhancing cracks at the bottom of the bridge.Through this algorithm,the data analysis results are more accurate.This effectively provides a new idea for crack detection in high-noise images.
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Haotian Li, 李灏天, 陈晓冬 Xiaodong Chen, 徐怀远 Huaiyuan Xu, 许鸿雁 Hongyan Xu, 汪毅 Yi Wang, and 蔡怀宇 Huaiyu Cai. "Bridge Crack Detection Algorithm Based on Bilateral-Frangi Filter." Laser & Optoelectronics Progress 56, no. 18 (2019): 181401. http://dx.doi.org/10.3788/lop56.181401.

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Lin Meng, 孟琳, 刘静 Jing Liu, 曹慧 Hui Cao, 史婷瑶 Tingyao Shi, and 张驰 Chi Zhang. "Retinal Vessel Segmentation Based on Frangi Filter and Otsu Algorithm." Laser & Optoelectronics Progress 56, no. 18 (2019): 181004. http://dx.doi.org/10.3788/lop56.181004.

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Longo, Antonia, Stefan Morscher, Jaber Malekzadeh Najafababdi, Dominik Jüstel, Christian Zakian, and Vasilis Ntziachristos. "Assessment of hessian-based Frangi vesselness filter in optoacoustic imaging." Photoacoustics 20 (December 2020): 100200. http://dx.doi.org/10.1016/j.pacs.2020.100200.

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Gruber, Dieter P., and Matthias Haselmann. "Inspection of Transparent Objects with Varying Light Scattering Using a Frangi Filter." Journal of Imaging 7, no. 2 (2021): 27. http://dx.doi.org/10.3390/jimaging7020027.

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This paper proposes a new machine vision method to test the quality of a semi-transparent automotive illuminant component. Difference images of Frangi filtered surface images are used to enhance defect-like image structures. In order to distinguish allowed structures from defective structures, morphological features are extracted and used for a nearest-neighbor-based anomaly score. In this way, it could be demonstrated that a segmentation of occurring defects is possible on transparent illuminant parts. The method turned out to be fast and accurate and is therefore also suited for in-production testing.
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Oharazawa, Akihiko, Masaki Ogino, Masaru Sugahara, and Masanori Tanahashi. "Skin capillary extraction technique based on independent component analysis and Frangi filter using videomicroscopy." Skin Research and Technology 26, no. 5 (2020): 664–70. http://dx.doi.org/10.1111/srt.12850.

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9

Khan, Khan Bahadar, Amir A. Khaliq, Abdul Jalil, and Muhammad Shahid. "A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising." PLOS ONE 13, no. 2 (2018): e0192203. http://dx.doi.org/10.1371/journal.pone.0192203.

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Shi, Gen, Hao Lu, Hui Hui, and Jie Tian. "Benefit from public unlabeled data: A Frangi filter-based pretraining network for 3D cerebrovascular segmentation." Medical Image Analysis 101 (April 2025): 103442. https://doi.org/10.1016/j.media.2024.103442.

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Guo, Xiaoyu, Jiajun Hu, Tong Lu, Guoyin Li, and Ruoxiu Xiao. "A novel vessel enhancement method based on Hessian matrix eigenvalues using multilayer perceptron." Bio-Medical Materials and Engineering 36, no. 2 (2025): 83–97. https://doi.org/10.1177/09592989241296431.

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Background: Vessel segmentation is a critical aspect of medical image processing, often involving vessel enhancement as a preprocessing step. Existing vessel enhancement methods based on eigenvalues of Hessian matrix face challenges such as inconsistent parameter settings and suboptimal enhancement effects across different datasets. Objective: This paper aims to introduce a novel vessel enhancement algorithm that overcomes the limitations of traditional methods by leveraging a multilayer perceptron to fit a vessel enhancement filter function using eigenvalues of Hessian matrix. The primary goal is to simplify parameter tuning while enhancing the effectiveness and generalizability of vessel enhancement. Methods: The proposed algorithm utilizes eigenvalues of Hessian matrix as input for training the multilayer perceptron-based vessel enhancement filter function. The diameter of the largest blood vessel in the dataset is the only parameter to be set. Results: Experiments were conducted on public datasets such as DRIVE, STARE, and IRCAD. Additionally, optimal parameter acquisition methods for traditional Frangi and Jerman filters are introduced and quantitatively compared with the novel approach. Performance metrics such as AUROC, AUPRC, and DSC show that the proposed algorithm outperforms traditional filters in enhancing vessel features. Conclusion: The findings of this study highlight the superiority of the proposed vessel enhancement algorithm in comparison to traditional methods. By simplifying parameter settings, improving enhancement effects, and showcasing superior performance metrics, the algorithm offers a promising solution for enhancing vessel parts in medical image analysis applications.
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Khawaja, Ahsan, Tariq M. Khan, Khuram Naveed, Syed Saud Naqvi, Naveed Ur Rehman, and Syed Junaid Nawaz. "An Improved Retinal Vessel Segmentation Framework Using Frangi Filter Coupled With the Probabilistic Patch Based Denoiser." IEEE Access 7 (2019): 164344–61. http://dx.doi.org/10.1109/access.2019.2953259.

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Liu, Zhiqiang, Jie Sun, Xianda Zhang, et al. "High-Accuracy Spectral Measurement of Stimulated-Brillouin-Scattering Lidar Based on Hessian Matrix and Steger Algorithm." Remote Sensing 15, no. 6 (2023): 1511. http://dx.doi.org/10.3390/rs15061511.

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The measurement accuracy of Brillouin scattering spectra is crucial for ocean remote sensing by Brillouin scattering lidar. Due to the limited resolution of ICCD cameras, the traditional processing methods remain at the pixel or partial sub-pixel level, which cannot meet the requirements of high-performance lidar. In this paper, to extract the frequency shift with high precision from stimulated Brillouin scattering (SBS) lidar, a novel spectral processing method with sub-pixel recognition accuracy is proposed based on the Hessian matrix and Steger algorithm combined with the least square fitting method. Firstly, the Hessian matrix and Frangi filter are used for signal denoising. Then, the center points of SBS spectra at the sub-pixel level are extracted using the Steger algorithm and are connected and classified according to the signal type. On that basis, the frequency shifts of Brillouin scattering are calculated by using the center and radii of interference spectra after through fitting by the least squares method. Finally, the water temperatures are inverted by using the frequency shifts of Brillouin scattering. The results show that the processing method proposed in this paper can accurately calculate the frequency shift of Brillouin scattering. The measured errors of frequency shift are generally at an order of MHz, and the inversion accuracy of water temperature can be as low as 0.14 °C. This work is essential to the application for remote sensing the seawater parameters by using the Brillouin lidar technique.
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Hossain, Mubdiul, Aziah Ali, Noramiza Hashim, Wan Noorshahida Mohd Isa, Wan Mimi Diyana Wan Zaki, and Aini Hussain. "Mobile Implementation of Retinal Image Analysis for Efficient Vessel, Optic Disc, and Lesion Detection." JOIV : International Journal on Informatics Visualization 7, no. 3-2 (2023): 1022. http://dx.doi.org/10.30630/joiv.7.3-2.2363.

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Smartphone-based mobile fundus photography is gaining popularity due to the rise of handheld fundus lenses, allowing a portable solution for a mobile-based computer-assisted diagnostic system (CADS). With such a system, professionals can monitor and diagnose numerous retinal diseases, including diabetic retinopathy (DR), glaucoma, age-related macular degeneration, etc. on their smartphone devices. In this study, we proposed a unified CADS tool for smartphone devices that can detect and identify six crucial retinal features utilizing both a filtering approach and a deep learning (DL) approach. These features are retinal blood vessels (RBV), optic discs (OD), hemorrhages (HM), microaneurysm (MA), hard exudates (HE), and soft exudates (SE). Traditional filtering is applied for RBV segmentation using B-COSFIRE and Frangi filter, whereas vessel inpainting and automatic canny edge-based Hough transform are used to localize OD center and radius. The DR lesions (HM, MA, HE, OD segmentation, and SE) are detected using a transfer learning-based Resnet50 backbone and multiclass DL U-net architecture. RBV segmentation achieved 94.94% and 94.44% accuracy in the DRIVE and STARE datasets. OD localization achieved an accuracy of 99.60% in the MESSIDOR dataset. Lastly, the IDRiD dataset is used to train and validate the DR lesions with an overall accuracy of 99.7%, F1-score of 77.4, and mean IoU of 59.2. The smartphone application can perform all the segmentation tasks at once in an average of 30 seconds. Given the availability, it is possible to improve the accuracy of the DL method further by training with more mobile fundus datasets.
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Mubeen, Azmath, and Uma N. Dulhare. "Feature Extraction and Identification of Rheumatoid Nodules Using Advanced Image Processing Techniques." Rheumato 4, no. 4 (2024): 176–92. http://dx.doi.org/10.3390/rheumato4040014.

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Background/Objectives: Accurate detection and classification of nodules in medical images, particularly rheumatoid nodules, are critical due to the varying nature of these nodules, where their specific type is often unknown before analysis. This study addresses the challenges of multi-class prediction in nodule detection, with a specific focus on rheumatoid nodules, by employing a comprehensive approach to feature extraction and classification. We utilized a diverse dataset of nodules, including rheumatoid nodules sourced from the DermNet dataset and local rheumatologists. Method: This study integrates 62 features, combining traditional image characteristics with advanced graph-based features derived from a superpixel graph constructed through Delaunay triangulation. The key steps include image preprocessing with anisotropic diffusion and Retinex enhancement, superpixel segmentation using SLIC, and graph-based feature extraction. Texture analysis was performed using Gray-Level Co-occurrence Matrix (GLCM) metrics, while shape analysis was conducted with Fourier descriptors. Vascular pattern recognition, crucial for identifying rheumatoid nodules, was enhanced using the Frangi filter. A Hybrid CNN–Transformer model was employed for feature fusion, and feature selection and hyperparameter tuning were optimized using Gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). Feature importance was assessed using SHAP values. Results: The proposed methodology achieved an accuracy of 85%, with a precision of 0.85, a recall of 0.89, and an F1 measure of 0.87, demonstrating the effectiveness of the approach in detecting and classifying rheumatoid nodules in both binary and multi-class classification scenarios. Conclusions: This study presents a robust tool for the detection and classification of nodules, particularly rheumatoid nodules, in medical imaging, offering significant potential for improving diagnostic accuracy and aiding in the early identification of rheumatoid conditions.
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Deshpande, Aadit, Sundaresan Raman, Amber Dubey, Pradeep Susvar, and Rajiv Raman. "An ImageJ macro tool for OCTA-based quantitative analysis of Myopic Choroidal neovascularization." PLOS ONE 18, no. 4 (2023): e0283929. http://dx.doi.org/10.1371/journal.pone.0283929.

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Myopic Choroidal neovascularization (mCNV) is one of the most common vision-threatening com- plications of pathological myopia among many retinal diseases. Optical Coherence Tomography Angiography (OCTA) is an emerging newer non-invasive imaging technique and is recently being included in the investigation and treatment of mCNV. However, there exists no standard tool for time-efficient and dependable analysis of OCTA images of mCNV. In this study, we propose a customizable ImageJ macro that automates the OCTA image processing and lets users measure nine mCNV biomarkers. We developed a three-stage image processing pipeline to process the OCTA images using the macro. The images were first manually delineated, and then denoised using a Gaussian Filter. This was followed by the application of the Frangi filter and Local Adaptive thresholding. Finally, skeletonized images were obtained using the Mexican Hat filter. Nine vascular biomarkers including Junction Density, Vessel Diameter, and Fractal Dimension were then computed from the skeletonized images. The macro was tested on a 26 OCTA image dataset for all biomarkers. Two trends emerged in the computed biomarker values. First, the lesion-size dependent parameters (mCNV Area (mm2) Mean = 0.65, SD = 0.46) showed high variation, whereas normalized parameters (Junction Density(n/mm): Mean = 10.24, SD = 0.63) were uniform throughout the dataset. The computed values were consistent with manual measurements within existing literature. The results illustrate our ImageJ macro to be a convenient alternative for manual OCTA image processing, including provisions for batch processing and parameter customization, providing a systematic, reliable analysis of mCNV.
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Zhong, Ding, Jiaqi Wang, Yixiao Guo, Yicong Liu, Junyang Chen, and Tianji Xu. "A Frangi Filter Aided Deep Learning Approach for Paleochannel Recognition." Geophysical Journal International, December 22, 2023. http://dx.doi.org/10.1093/gji/ggad491.

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Summary In view of the problem of identifying paleochannels with high concealment and complex geological structures, this paper proposes an intelligent recognition method for paleochannels based on Frangi filtering and deep learning. The methodology makes use of Maximum Entropy Wigner-Ville Distribution (MEWVD) method to process the original instantaneous amplitude data, which enhance the distinct features of micro paleochannels in different sensitive frequency bands. The sample two-dimensional stratigraphic images generated from these data is labeled for the pre-training process of Attention R2U-Net neural network model. Subsequently, Frangi filter is employed to identify and enhance the linear structures of river channels in target stratigraphic images, improving the identification effect of the neural network. Finally, RGB data fusion and three-dimensional visualization carving are performed on the identification data. This method not only eliminates redundant information using the Frangi filter but also proves that the Attention R2U-Net network model structure with attention mechanism and residual convolution structure can effectively improve the segmentation effect for river channels at different scales. Experimental examples show that this method achieves pixel-level feature segmentation of 3D seismic data for paleochannels, accurately depicting their shape, width, thickness, flow direction and other features, thus providing support for subsequent well deployment and horizontal well fracturing selection.
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Díaz, Mario, Erelle Fuchs, Hendrik Mattern, et al. "Contrast‐agnostic deep‐learning‐based detection of perivascular spaces in magnetic resonance imaging." Alzheimer's & Dementia 20, S2 (2024). https://doi.org/10.1002/alz.088012.

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AbstractBackgroundTo date, all computerised perivascular spaces (PVS) quantification methods require case‐wise, imaging modality, or study‐specific parameter adjustments, and suffer from generalisability problems in clinical settings, and misdetection of other cerebral small vessel disease (CSVD) markers. We propose a deep learning‐based PVS detection method to overcome these issues. We compare our proposal on magnetic resonance imaging data of CSVD participants against the performance of the Frangi filter.MethodT1‐weighted (T1w) and T2‐weighted (T2w) neuroimaging data from 16 patients with severe CSVD were collected at the Hospital of the Otto‐von‐Guericke University Magdeburg. PVS across the entire brain tissue were manually segmented in T2w images by a medical student.We extended SynthSeg (https://surfer.nmr.mgh.harvard.edu/fswiki/SynthSeg) for PVS detection (Figure 1). PVS‐SynthSeg, our proposal, is based on a convolutional neural network and trained using synthetic data sampled from a generative model that relies on brain atlases as a conditioning factor, as well as domain randomisation. We incorporated tubular structures into such brain atlases to expand the model’s capabilities for the identification of PVS. This provides PVS‐SynthSeg with the ability to detect PVS in scans from diverse scanners and protocols without requiring additional fine‐tuning or parameter adjustments.We compared the precision and recall of PVS‐SynthSeg against that from the Frangi filter on both imaging modalities.ResultThe Frangi filter detected the majority of PVS in both T1w and T2w scans, albeit with high false positive rates–recall of approximately 80% and precision below 30% (Figure 2). PVS‐SynthSeg had more variability in recall but near‐one precision across both modalities, despite the presence of white matter hyperintensities (WMH).ConclusionWe propose a deep learning‐based PVS detection algorithm. In a small sample, PVS‐SynthSeg detected PVS in T1w and T2w scans better than the Frangi filter without additional adjustments, even in scans affected by motion artefacts or with WMH. These findings highlight the promise of PVS‐SynthSeg as a modular and effective PVS detection method, however larger cohort testing needs to be conducted. Better PVS quantification can help us shed light into the role of PVS in CSVD and Alzheimer’s disease.
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Mahmoudi, Ramzi, Narjes Benameur, Hmida Badii, and Momahed Hedi Bedoui. "Mitral Valve Leaflets segmentation approaches based upon Frangi filter and ISODATA clustering." Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, July 13, 2021, 1–12. http://dx.doi.org/10.1080/21681163.2021.1936648.

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"Retraction: A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising." PLOS ONE 13, no. 8 (2018): e0203418. http://dx.doi.org/10.1371/journal.pone.0203418.

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Zhang, Zhi-Bin, Yong-Ning Zou, Ye-Ling Huang, and Qi LI. "CT image crack segmentation method based on linear feature enhancement." Journal of X-Ray Science and Technology, June 12, 2022, 1–15. http://dx.doi.org/10.3233/xst-221171.

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Industrial computed tomography (CT) crack segmentation is a key technology in industrial CT image processing. Unfortunately, the interference of artifact and noise in CT image often bring great trouble to the crack segmentation. In order to improve the segmentation accuracy of cracks in CT images, we propose to develop and test a new crack segmentation algorithm based on linear feature enhancement by analyzing the features of cracks in CT images. Firstly, the total variational model is used to denoise the input image. Next, a Frangi multiscale filter is used to extract linear structures in the image, and then the extracted linear structures are used to enhance the contrast of the image. Finally, the cracks in the image are detected and segmented by Otsu algorithm. By comparing with the manual segmentation results, the average intersection-over-union (IOU) reaches 86.10% and the average F1 score reaches 92.44%, which verifies the effectiveness and correctness of the algorithm developed in this study. Overall, experiments demonstrate that the new algorithm improves the accuracy of crack segmentation and it is effective applying to industry CT images.
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Wang, Bin, Han Shi, Enuo Cui, et al. "A robust and efficient framework for tubular structure segmentation in chest CT images." Technology and Health Care, December 31, 2020, 1–11. http://dx.doi.org/10.3233/thc-202431.

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BACKGROUND: Tubular structure segmentation in chest CT images can reduce false positives (FPs) dramatically and improve the performance of nodules malignancy levels classification. OBJECTIVE: In this study, we present a framework that can segment the pulmonary tubular structure regions robustly and efficiently. METHODS: Firstly, we formulate a global tubular structure identification model based on Frangi filter. The model can recognize irregular vascular structures including bifurcation, small vessel, and junction, robustly and sensitively in 2D images. In addition, to segment the vessels from JVN, we design a local tubular structure identification model with a sliding window. Finally, we propose a multi-view voxel discriminating scheme on the basis of the previous two models. This scheme reduces the computational complexity of obtaining high entropy spatial tubular structure information. RESULTS: Experimental results have shown that the proposed framework achieves TPR of 85.79%, FPR of 24.83%, and ACC of 84.47% with the average elapsed time of 162.9 seconds. CONCLUSIONS: The framework provides an automated approach for effectively segmenting tubular structure from the chest CT images.
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Sorelli, Michele, Irene Costantini, Leonardo Bocchi, Markus Axer, Francesco Saverio Pavone, and Giacomo Mazzamuto. "Fiber enhancement and 3D orientation analysis in label-free two-photon fluorescence microscopy." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-30953-w.

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AbstractFluorescence microscopy can be exploited for evaluating the brain’s fiber architecture with unsurpassed spatial resolution in combination with different tissue preparation and staining protocols. Differently from state-of-the-art polarimetry-based neuroimaging modalities, the quantification of fiber tract orientations from fluorescence microscopy volume images entails the application of specific image processing techniques, such as Fourier or structure tensor analysis. These, however, may lead to unreliable outcomes as they do not isolate myelinated fibers from the surrounding tissue. In this work, we describe a novel image processing pipeline that enables the computation of accurate 3D fiber orientation maps from both grey and white matter regions, exploiting the selective multiscale enhancement of tubular structures of varying diameters provided by a 3D implementation of the Frangi filter. The developed software tool can efficiently generate orientation distribution function maps at arbitrary spatial scales which may support the histological validation of modern diffusion-weighted magnetic resonance imaging tractography. Despite being tested here on two-photon scanning fluorescence microscopy images, acquired from tissue samples treated with a label-free technique enhancing the autofluorescence of myelinated fibers, the presented pipeline was developed to be employed on all types of 3D fluorescence images and fiber staining.
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Gunter, Jeffrey L., Scott A. Przybelski, Kohl Johnson Sparrman, et al. "Multi‐channel segmentation allows simultaneous estimation of classic tissue compartments, WMH and enlarged perivascular spaces." Alzheimer's & Dementia 19, S16 (2023). http://dx.doi.org/10.1002/alz.080221.

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AbstractBackgroundLarge neuroimaging studies like ADNI‐4 now include T1‐weighted, T2‐weighted and FLAIR images at isotropic1mm resolution for investigation of both neurodegenerative and cerebrovascular disease. These three input types allow segmentation of GM, WM, DeepGM, WMH, and CSF compartments. Additionally, this supports automated assessment of enlarged perivascular spaces (PVS). Our objective was to propose a unified framework for segmentation of intracranial classes and PVS while reducing human effort.MethodsFLAIR and T2‐weighted images are aligned to the T1‐weighted image.. Standard SPM12 unified segmentation using all three input images establishes deformations between subject and template space and bias field corrections. Extended tissue priors including deep GM are deformed into subject space. Adding mean intensities from high probability deep GM voxels as seeds and using the extended prior set, segmentation is re‐started, refining model parameters, deformations and bias‐field estimates. The procedure repeats, adding a WMH channel, resulting in a segmentation including GM, WM, CSF, deep GM, WMH and nuisance classes. Frangi filters are applied to the T1‐ and T2‐ weighted images. Filter response images are smoothed, averaged, masked and inserted in place of the CSF prior. Probability maps of vessel‐like shapes with CSF‐like contrast are estimated to identify PVS. Visual grading was also performed for comparison.ResultsThe method was applied to 300 participants. Visual quality control found two instances of poor WMH over‐segmentation. Perivascular space maps were all visually reasonable. Standard segmentation classes were visibly superior to single T1‐based segmentation. Figures 1,2 present examples. Pearson correlations of PVS fractional volume with multiple human raters’ PVS counts in CSO/CR was over 0.7 in a subset of 125 participants (Figure 3).ConclusionModern dementia protocols include high‐resolution T1‐weighted, T2‐weighted, and FLAIR images that can be used together as inputs to extend probabilistic segmentation adding deep grey and WMH classes with automated PVS assessment.
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Gunter, Jeffrey L., Scott A. Przybelski, Kohl Johnson Sparrman, et al. "Multi‐channel segmentation allows simultaneous estimation of classic tissue compartments, WMH and enlarged perivascular spaces." Alzheimer's & Dementia 19, S10 (2023). http://dx.doi.org/10.1002/alz.081987.

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AbstractBackgroundLarge neuroimaging studies like ADNI‐4 now include T1‐weighted, T2‐weighted and FLAIR images at isotropic1mm resolution for investigation of both neurodegenerative and cerebrovascular disease. These three input types allow segmentation of GM, WM, DeepGM, WMH, and CSF compartments. Additionally, this supports automated assessment of enlarged perivascular spaces (PVS). Our objective was to propose a unified framework for segmentation of intracranial classes and PVS while reducing human effort.MethodFLAIR and T2‐weighted images are aligned to the T1‐weighted image.. Standard SPM12 unified segmentation using all three input images establishes deformations between subject and template space and bias field corrections. Extended tissue priors including deep GM are deformed into subject space. Adding mean intensities from high probability deep GM voxels as seeds and using the extended prior set, segmentation is re‐started, refining model parameters, deformations and bias‐field estimates. The procedure repeats, adding a WMH channel, resulting in a segmentation including GM, WM, CSF, deep GM, WMH and nuisance classes. Frangi filters are applied to the T1‐ and T2‐ weighted images. Filter response images are smoothed, averaged, masked and inserted in place of the CSF prior. Probability maps of vessel‐like shapes with CSF‐like contrast are estimated to identify PVS. Visual grading was also performed for comparison.ResultThe method was applied to 300 participants. Visual quality control found two instances of poor WMH over‐segmentation. Perivascular space maps were all visually reasonable. Standard segmentation classes were visibly superior to single T1‐based segmentation. Figures 1,2 present examples. Pearson correlations of PVS fractional volume with multiple human raters’ PVS counts in CSO/CR was over 0.7 in a subset of 125 participants (Figure 3).ConclusionModern dementia protocols include high‐resolution T1‐weighted, T2‐weighted, and FLAIR images that can be used together as inputs to extend probabilistic segmentation adding deep grey and WMH classes with automated PVS assessment.
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K., Renu Sree1 K. Divya Vani2 G. Samyuktha Rani3 N. Shilpa Reddy4 N. Sreenivasa Rao5. "FUNDUS IMAGES FOR DIAGNOSIS OF DIABETIC RETINOPATHY USING SEGMENTATION OF BLOOD VESSELS." September 12, 2022. https://doi.org/10.5281/zenodo.7988947.

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In this major project, our team will analyze and able to get strategy yields competitive results for both pre-processing modalities, i.e., Contrast Limited Adaptive Histogram Equalization (CLAHE) and Generalized Linear Model (GLM) of Sensitivity (Sn), Specificity (Sp), Accuracy (Acc) parameters. Computer-Aided Diagnosis (CAD) is a widely used technique to detect and diagnose diseases like tumors, cancers, edemas, etc. Several critical retinal diseases like diabetic retinopathy (DR), hypertensive retinopathy (HR), Macular degeneration, retinitis pigmentosa (RP) are mainly analyzed based on the observation of fundus images. The raw fundus images are of inferior quality to represent the minor changes directly. To detect and analyze minor changes in retinal vasculature or to apply advanced disease detection algorithms, the fundus image should be enhanced enough to visibly present vessel touristy, for contrast enhancement, various retinal-vessel segmentation methods apply image-contrast enhancement as a pre- processing step, which can introduce noise in an image and affect vessel detection. Specifically, for retinal vessels segmentation, accurate segmentation of fundus images is highly challenging due to low vessel contrast, varying widths, branching, and the crossing of vessels most vessel segmentation strategies utilize contrast enhancement as a pre-processing step, which has an inherent tendency to aggravate thenoise and therefore, impede accurate vessel detection. To alleviate this problem, we propose to use the state-of-the-art Probabilistic Patch Based (PPB) denoiser within the framework of an unsupervised retinal vessel segmentation strategy based on the Frangi filter. The performance evaluation of the proposed method is evaluated on two recognized open-access datasets, viz: DRIVE. The proposed strategy yields competitive results for both pre-processing modalities, i.e., Contrast Limited Adaptive Histogram Equalization (CLAHE) and Generalized Linear Model (GLM) of Sensitivity (Sn), Specificity (Sp), Accuracy (Acc) parameters.
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Memon, Falak, Moazzam jawaid, and Shahnawaz Talpur. "Detection & Quantification of Lung Nodules Using 3D CT images." International Journal of Innovations in Science and Technology, January 29, 2023, 68–81. http://dx.doi.org/10.33411/ijist/2023050105.

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In computer vision image detection and quantification play an important role. Image Detection and quantification is the process of identifying nodule position and the amount of covered area. The dataset which we have used for this research contains 3D CT lung images. In our proposed work we have taken 3D images and those are high-resolution images. We have compared the accuracy of the existing mask and our segmented images. The segmentation method that we have applied to these images is Sparse Field Method localized region-based segmentation and for Nodule detection, I have used ray projection. The ray projection method is efficient for making the point more visible by its x, y, and z components. like a parametric equation where the line crossing through a targeted point by that nodule is more dominated. The Frangi filter was to give a geometric shape to the nodule and we got 90% accurate detection. The high mortality rate associated with lung cancer makes it imperative that it be detected at an early stage. The application of computerized image processing methods has the potential to improve both the efficiency and reliability of lung cancer screening. Computerized tomography (CT) pictures are frequently used in medical image processing because of their excellent resolution and low noise. Computer-aided detection systems, including preprocessing and segmentation methods, as well as data analysis approaches, have been investigated in this research for their potential use in the detection and diagnosis of lung cancer. The primary objective was to research cutting-edge methods for creating computational diagnostic tools to aid in the collection, processing, and interpretation of medical imaging data. Nonetheless, there are still areas that need more work, such as improving sensitivity, decreasing false positives, and optimizing the identification of each type of nodule, even those of varying size and form.
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Lin, Yingying, Peng Cao, Shirley Chiu Wai Chan, Kam Ho Lee, Vince Wing Hang Lau, and Ho Yin Chung. "Deep Learning Algorithm of the SPARCC Scoring System in SI Joint MRI." Journal of Magnetic Resonance Imaging, January 2, 2024. http://dx.doi.org/10.1002/jmri.29211.

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BackgroundThe Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system is a sacroiliitis grading system.PurposeTo develop a deep learning‐based pipeline for grading sacroiliitis using the SPARCC scoring system.Study TypeProspective.PopulationThe study included 389 participants (42.2‐year‐old, 44.6% female, 317/35/37 for training/validation/testing). A pretrained algorithm was used to differentiate image with/without sacroiliitis.Field Strength/Sequence3‐T, short tau inversion recovery (STIR) sequence, fast spine echo.AssessmentThe regions of interest as ground truth for models' training were identified by a rheumatologist (HYC, 10‐year‐experience) and a radiologist (KHL, 6‐year‐experience) using the Assessment of Spondyloarthritis International Society definition of MRI sacroiliitis independently. Another radiologist (YYL, 4.5‐year‐experience) solved the discrepancies. The bone marrow edema (BME) and sacroiliac region models were for segmentation. Frangi‐filter detected vessels used as intense reference. Deep learning pipeline scored using SPARCC scoring system evaluating presence and features of BMEs. A rheumatologist (SCWC, 6‐year‐experience) and a radiologist (VWHL, 14‐year‐experience) scored using the SPARCC scoring system once. The radiologist (YYL) scored twice with 5‐day interval.Statistical TestsIndependent samples t‐tests and Chi‐squared tests were used. Interobserver and intraobserver reliability by intraclass correlation coefficient (ICC) and Pearson coefficient evaluated consistency between readers and the deep learning pipeline. We evaluated the performance using sensitivity, accuracy, positive predictive value, and Dice coefficient. A P‐value <0.05 was considered statistically significant.ResultsThe ICC and the Pearson coefficient between the SPARCC scores from three readers and the deep learning pipeline were 0.83 and 0.86, respectively. The sensitivity in identifying BME and accuracy of identifying SI joints and blood vessels was 0.83, 0.90, and 0.88, respectively. The dice coefficients were 0.82 (sacrum) and 0.80 (ilium).Data ConclusionThe high consistency with human readers indicated that deep learning pipeline may provide a SPARCC‐informed deep learning approach for scoring of STIR images in spondyloarthritis.Evidence Level1Technical EfficacyStage 2
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Zhou, Liangdong, Thanh D. Nguyen, Xiuyuan Hugh Wang, et al. "CSF Fraction Mapping Derived from Multi‐echo MR FAST‐T2 Sequence Could be an Alternative Measure of Total Perivascular Space in the Brain Parenchyma." Alzheimer's & Dementia 19, S24 (2023). http://dx.doi.org/10.1002/alz.082906.

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AbstractBackgroundPerivascular space (PVS), as the key pathway of the glymphatic clearance, has been studied from various aspects. The volume of PVS is considered a biomarker of the enlargement of PVS, which may reflect the deficits of the clearance function. However, most of the methods for the estimation of PVS depends on the segmentation of visible PVS on anatomical MRI, which inherently ignored the invisible PVS that are of sub‐voxel size and dominant of the total PVS. We proposed a muti‐echo T2 relaxometry based CSF water fraction (CSFF) mapping method using 3‐water compartment model. The CSFF mapping corresponding to the long T2 (>200 ms) compartment in the total signal fitted using a 6 echo FAST‐T2 data.MethodForty‐one subjects were recruited (Age: 58.5±17.0, 25 normal (NL), 16 Aβ+ MCI/AD) including 26 females and 15 males. CSFF was reconstructed from the 6 echo FASTT2 data by fitting a three‐water compartment model. PVS in cerebral white matter was segmented using the Frangi filter on the enhanced PVS image. Intracranial volume was segmented using SPM12 for the PVS load normalization.ResultFigure 1 shows the distribution of CSFF and PVS by groups. There is significant difference of CSFF between NL and MCI/AD groups, and no difference of PVS between groups. Figure 2 shows the linear relationship between GM CSFF and normalized PVS load. We see CSFF is about 10 times larger than PVS load. We know the CBV is about 3∼5% of the whole brain. CSFF value is about the same level of CBF, which is very reasonable. It also shows that CSFF has better ability to distinguish NL and MCI/AD than PVS load. Figure 3 shows the high consistency between the CSFF from both high and low resolution FASTT2 data. High resolution CSFF has less partial volume effect, that’s why it has about 0.5% higher value than the low resolution CSFF.ConclusionThe CSF fraction (CSFF) mapping is useful to detect early changes in AD, which may reflect the subtle changes of total PVS space in the brain. Acknowledgement: This study is supported by NIA grant AG057848
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Kim, Woo Sik, Roh‐Eul Yoo, Yejin Hwang, et al. "Distinct Association of Amyloid and Vascular Pathologies with Enhanced Perivascular Spaces." Alzheimer's & Dementia 20, S2 (2024). https://doi.org/10.1002/alz.087217.

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AbstractBackgroundGlymphatic system dysfunction as characterized by increased MRI‐visible Perivascular Spaces (PVS) is speculated to play a role in the acceleration of amyloid accumulation in Alzheimer’s Disease (AD). However, while PVS is also prevalent amongst Vascular Dementia (VD), the pathological distinctions between regional PVS in AD‐ and VD‐driven cohorts remain largely unknown. Through a mixed dementia cohort, we examined these pathology‐driven localization patterns via automated PVS segmentations from T2‐weighted MRI.Method99 cognitively unimpaired (CU) and 190 cognitively impaired (CI) patients’ data were collected from the Seoul National University Dementia cohort. Through visual assessments of individual’s 18F‐Florbetaben PET, FLAIR and SWI images, expert radiologists classified CI patients into 4 groups based on both amyloid and vascular‐damage burden (26 A‐VB‐, 63 A‐VB+, 26 A+VB‐, and 75 A+VB+). PVS segmentation involved masking and thresholding hyperintense vessel structures using slice‐wise Frangi filters applied to T2‐weighted MRIs at the basal ganglia (BG) and cerebral white matter (WM), segmented into 4 major lobes (frontal, parietal, temporal, and occipital). We calculated PVS volume fractions (PVS‐VF) as voxel counts normalized by intracranial volumes. Automated segmentations were validated against manually segmented PVS volumes at BG and whole WM (rank correlation coefficients: 0.634, 0.539). Groupwise differences were calculated through two‐sample t‐tests corrected for multiple comparisons.ResultFor A+ groups, significant increases in PVS‐VF were observed at the temporal and occipital lobes as compared to CU, a finding that agree well with prior visual rating studies. However, VB+ groups showed significant PVS‐VF increases across all lobes as compared to CU. Particularly, BG represented the most pronounced VD‐related PVS, with a significantly greater PVS‐VF in the VB+ groups compared to their VB‐ counterparts. Interestingly, PVS volume ratios of lobar regions and BG showed no significant increases for A‐VB+, while all A+ groups showed significant increases in temporal and occipital regions as compared to CU.ConclusionFull utilization of PVS as a core biomarker in discerning glymphatic dysfunctions of AD requires full understanding of their properties in those contexts. Cross‐sectional differentiation of regional PVS localization in AD can become a steppingstone into elucidating those associations within the AD continuum.
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