Relevant bibliographies by topics / Frangi-based filter

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Academic literature on the topic 'Frangi-based filter'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Frangi-based filter"

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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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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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Conference papers on the topic "Frangi-based filter"

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Jimenez-Carretero, Daniel, Andres Santos, Sjoerd Kerkstra, Rina Dewi Rudyanto, and Maria J. Ledesma-Carbayo. "3D Frangi-based lung vessel enhancement filter penalizing airways." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556627.

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Nugroho, Hanung Adi, Rezty Amalia Aras, Tri Lestari, and Igi Ardiyanto. "Retinal vessel segmentation based on Frangi filter and morphological reconstruction." In 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC). IEEE, 2017. http://dx.doi.org/10.1109/iccerec.2017.8226686.

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Cai, Cheng-Syun, Chun-Fu Chen, Gwo Giun Lee, et al. "Density analysis of collagen fibers based on enhanced frangi filter in Second Harmonic Generation virtual biopsy images." In 2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP). IEEE, 2014. http://dx.doi.org/10.1109/chinasip.2014.6889286.

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Srivastava, Ruchir, Damon W. K. Wong, Lixin Duan, Jiang Liu, and Tien Yin Wong. "Red lesion detection in retinal fundus images using Frangi-based filters." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7319677.

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