12 research outputs found
Finger Vein Image Enhancement Technique based on Gabor filter and Discrete Cosine Transform
No full textBiometrics is a global technique to establish the identity of a person by measuring one of their physical or behavioral characteristics such as fingerprint, signature, iris, voice and face. Compared to these biometric techniques, the finger vein technique has distinct advantages as it helps to protect privacy and anonymity in automated individual recognition. Many studies showed that the finger vein images were of a low quality because of the variation in the tissues and uneven illumination. Hence, there is a need for effective image enhancement techniques, which can improve the quality of the images. In this study, we proposed a novel technique, which enhances the image quality of the finger veins. This method includes contrast amelioration, use of Gabor filters and image fusion, which generates an image with highly connective patterns. We used three criteria to evaluate the quality of processed images, the mean of grey values, the image entropy, and the image contrast. The obtained result shows higher values when using our approach in comparison to the baseline methods considered in this work
Reinforced Residual Encoder–Decoder Network for Image Denoising via Deeper Encoding and Balanced Skip Connections
No full textTraditional image denoising algorithms often struggle with real-world complexities such as spatially correlated noise, varying illumination conditions, sensor-specific noise patterns, motion blur, and structural distortions. This paper presents an enhanced residual denoising network, R-REDNet, which stands for Reinforced Residual Encoder–Decoder Network. The proposed architecture incorporates deeper convolutional layers in the encoder and replaces additive skip connections with averaging operations to improve feature extraction and noise suppression. Additionally, the method leverages an iterative refinement approach, further enhancing its denoising performance. Experiments conducted on two real-world noisy image datasets demonstrate that R-REDNet outperforms current state-of-the-art approaches. Specifically, it attained a peak signal-to-noise ratio of 44.01 dB and a structural similarity index of 0.9931 on Dataset 1, and it obtained a peak signal-to-noise ratio of 46.15 dB with a structural similarity index of 0.9955 on Dataset 2. These findings confirm the efficiency of our method in delivering high-quality image restoration while preserving fine details
Finger vein identification using deeply-fused Convolutional Neural Network
No full textFinger vein identification is a recently developed biometric technology and has become an essential field in biometrics, garnering increasing attention in recent years. As a biometric trait, using vein patterns allows for personal recognition with high security. In this paper, we have employed an improved deep network, named Merge Convolutional Neural Network (Merge CNN), which uses several CNNs with short paths. The scheme is based on the use of multiple identical CNNs with different input images qualities, and the unification of their outputs into a single layer. To achieve this, we designed different networks and trained them with the FV-USM dataset. The most optimal CNN architecture was used to build our final merged CNN labeled A, which is a combination of original image and image enhanced with Contrast Limited Adaptive Histogram (CLAH) method. Using six images for training, satisfactory performances were obtained from the FV-USM database with a recognition rate of 96.75%. Our proposed approach showed better performance than other methods exist in the literature, for the SDUMLA-HMT database with a recognition rate of 99.48%, when using five images for learning. Our proposed scheme can compete with state-of-the-art methods with recognition rate of 99.56% for the THU-FVFDT2 database
Absence of xenotropic murine leukaemia virus-related virus in UK patients with chronic fatigue syndrome
Get PDFBackground: Detection of a retrovirus, xenotropic murine leukaemia virus-related virus (XMRV), has recently been reported in 67% of patients with chronic fatigue syndrome. We have studied a total of 170 samples from chronic fatigue syndrome patients from two UK cohorts and 395 controls for evidence of XMRV infection by looking either for the presence of viral nucleic acids using quantitative PCR (limit of detection <16 viral copies) or for the presence of serological responses using a virus neutralisation assay.
Results: We have not identified XMRV DNA in any samples by PCR (0/299). Some serum samples showed XMRV neutralising activity (26/565) but only one of these positive sera came from a CFS patient. Most of the positive sera were also able to neutralise MLV particles pseudotyped with envelope proteins from other viruses, including vesicular stomatitis virus, indicating significant cross-reactivity in serological responses. Four positive samples were specific for XMRV.
Conclusions: No association between XMRV infection and CFS was observed in the samples tested, either by PCR or serological methodologies. The non-specific neutralisation observed in multiple serum samples suggests that it is unlikely that these responses were elicited by XMRV and highlights the danger of over-estimating XMRV frequency based on serological assays. In spite of this, we believe that the detection of neutralising activity that did not inhibit VSV-G pseudotyped MLV in at least four human serum samples indicates that XMRV infection may occur in the general population, although with currently uncertain outcomes
Humans with inherited MyD88 and IRAK-4 deficiencies are predisposed to hypoxemic COVID-19 pneumonia
Get PDFX-linked recessive deficiency of TLR7, a MyD88- and IRAK-4-dependent endosomal ssRNA sensor, impairs SARS-CoV-2 recognition and type I IFN production in plasmacytoid dendritic cells (pDCs), thereby underlying hypoxemic COVID-19 pneumonia with high penetrance. We report 22 unvaccinated patients with autosomal recessive MyD88 or IRAK-4 deficiency infected with SARS-CoV-2 (mean age: 10.9 yr; 2 mo to 24 yr), originating from 17 kindreds from eight countries on three continents. 16 patients were hospitalized: six with moderate, four with severe, and six with critical pneumonia, one of whom died. The risk of hypoxemic pneumonia increased with age. The risk of invasive mechanical ventilation was also much greater than in age-matched controls from the general population (OR: 74.7, 95% CI: 26.8-207.8, P < 0.001). The patients' susceptibility to SARS-CoV-2 can be attributed to impaired TLR7-dependent type I IFN production by pDCs, which do not sense SARS-CoV-2 correctly. Patients with inherited MyD88 or IRAK-4 deficiency were long thought to be selectively vulnerable to pyogenic bacteria, but also have a high risk of hypoxemic COVID-19 pneumonia.sponsorship: We thank the patients and their families for placing their trust in us. We warmly thank A. Dominguez-Acosta, P. Santana-Falcon, E. Rodriguez-Gonzalez, and M.E. Rosales-Bordon for technical assistance, and Y. Nemirovskaya, M. Woollett, D. Liu, S. Boucherit, C. Rivalain, M. Chrabieh and L. Lorenzo for administrative assistance. We are indebted to the "Biobanc de l'Hospital Infantil Sant Joan de Deu per a la Investigacio" for sample and data procurement and "Kids Corona Platform" Hospital Sant Joan de Deu, Barcelona. We would like to thank the members of the International IPF Genetics Consortium (https://github.com/genomicsITER/PFgenetics) for granting access to the genome-wide association studies summary data in the study across five cohorts, and to J.M. Aznar for providing data about mutations in RTEL1 in Spanish patients with IEI. We thank Erin Williams for organizing the logistics of patient samples and whole-exome sequencing. The graphical abstract was created with BioRender.com. The study was funded by Instituto de Salud Carlos III (COV20_01333, COV20_01334, PI16/00759, PI18/00223, PI19/00208, PI20/00876, and PI21/00211), the Spanish Ministry of Science and Innovation (RTC-2017-6471-1; AEI/FEDER), the Fundacion Canaria Instituto de Investigacion Sanitaria de Canarias (FIISC19/43 and FIISC22/27), Grupo DISA (OA18/017 and OA22/035), Fundacion MAPFRE Guanarteme (OA21/131), Cabildo Insular de Tenerife (CGIEU0000219140 and "Apuestas cientificas del ITER para colaborar en la lucha contra la COVID-19"), a 2022 Convocatoria de Beques de Recerca IRSJD-Carmen de Torres 2022 (2022AR-IRSJD-CdTorres), CERCA Programme/Generalitat de Catalunya, the Else Kroener-Fresenius Stiftung (EKFS, 2017_A110), the German Federal Ministry of Education and Research (01GM1910C), the Intramural Research Program of the National Institute of Allergy and Infectious Diseases, National Institutes of Health, and the Horizon Europe Framework Programme of the European Union under Grant Agreement no. 101057100. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. (Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation|COV20_01333, Spanish Ministry of Science and Innovation|COV20_01334, Spanish Ministry of Science and Innovation|PI16/00759, Spanish Ministry of Science and Innovation|PI18/00223, Spanish Ministry of Science and Innovation|PI19/00208, Spanish Ministry of Science and Innovation|PI20/00876, Spanish Ministry of Science and Innovation|PI21/00211, Fundacion Canaria Instituto de Investigacion Sanitaria de Canarias|RTC-2017-6471-1, Grupo DISA|FIISC19/43, Grupo DISA|FIISC22/27, Fundacion MAPFRE Guanarteme|OA18/017, Fundacion MAPFRE Guanarteme|OA22/035, Cabildo Insular de Tenerife|OA21/131, 2022 Convocatoria de Beques de Recerca IRSJD-Carmen de Torres 2022|CGIEU0000219140, CERCA Programme/Generalitat de Catalunya|2022AR-IRSJD-CdTorres, Else Kroener-Fresenius Stiftung (EKFS), German Federal Ministry of Education and Research|2017_A110, Intramural Research Program of the National Institute of Allergy and Infectious Diseases, National Institutes of Health|01GM1910C, Horizon Europe Framework Programme of the European Union, 101057100, National Institute of Allergy and Infectious Diseases|R01AI163029, National Institute of Allergy and Infectious Diseases|R01AI088364, National Institute of Allergy and Infectious Diseases|ZIAAI001265, National Institute of Allergy and Infectious Diseases|ZIAAI001270)status: Publishe
Finger Vein Image Enhancement Technique based on Gabor filter and Discrete Cosine Transform
Get PDFBiometrics is a global technique to establish the identity of a person by measuring one of their physical or behavioral characteristics such as fingerprint, signature, iris, voice and face. Compared to these biometric techniques, the finger vein technique has distinct advantages as it helps to protect privacy and anonymity in automated individual recognition. Many studies showed that the finger vein images were of a low quality because of the variation in the tissues and uneven illumination. Hence, there is a need for effective image enhancement techniques, which can improve the quality of the images. In this study, we proposed a novel technique, which enhances the image quality of the finger veins. This method includes contrast amelioration, use of Gabor filters and image fusion, which generates an image with highly connective patterns. We used three criteria to evaluate the quality of processed images, the mean of grey values, the image entropy, and the image contrast. The obtained result shows higher values when using our approach in comparison to the baseline methods considered in this work
