Under-Display Ultrasonic Fingerprint Recognition With Finger Vessel Imaging

2021 ◽  
Vol 21 (6) ◽  
pp. 7412-7419
Author(s):  
Chang Peng ◽  
Mengyue Chen ◽  
Xiaoning Jiang
2020 ◽  
Author(s):  
Ganesh Awasthi ◽  
Dr. Hanumant Fadewar ◽  
Almas Siddiqui ◽  
Bharatratna P. Gaikwad

Author(s):  
Mariya Nazarkevych ◽  
Serhii Dmytruk ◽  
Volodymyr Hrytsyk ◽  
Olha Vozna ◽  
Anzhela Kuza ◽  
...  

Background: Systems of the Internet of Things are actively implementing biometric systems. For fast and high-quality recognition in sensory biometric control and management systems, skeletonization methods are used at the stage of fingerprint recognition. The analysis of the known skeletonization methods of Zhang-Suen, Hilditch, Ateb-Gabor with the wave skeletonization method has been carried out and it shows a good time and qualitative recognition results. Methods: The methods of Zhang-Suen, Hildich and thinning algorithm based on Ateb-Gabor filtration, which form the skeletons of biometric fingerprint images, are considered. The proposed thinning algorithm based on Ateb-Gabor filtration showed better efficiency because it is based on the best type of filtering, which is both a combination of the classic Gabor function and the harmonic Ateb function. The combination of this type of filtration makes it possible to more accurately form the surroundings where the skeleton is formed. Results: Along with the known ones, a new Ateb-Gabor filtering algorithm with the wave skeletonization method has been developed, the recognition results of which have better quality, which allows to increase the recognition quality from 3 to 10%. Conclusion: The Zhang-Suen algorithm is a 2-way algorithm, so for each iteration, it performs two sets of checks during which pixels are removed from the image. Zhang-Suen's algorithm works on a plot of black pixels with eight neighbors. This means that the pixels found along the edges of the image are not analyzed. Hilditch thinning algorithm occurs in several passages, where the algorithm checks all pixels and decides whether to replace a pixel from black to white if certain conditions are satisfied. This Ateb-Gabor filtering will provide better performance, as it allows to obtain more hollow shapes, organize a larger range of curves. Numerous experimental studies confirm the effectiveness of the proposed method.


2021 ◽  
Vol 52 (1) ◽  
pp. 1368-1371
Author(s):  
Bozhi Liu ◽  
Xuanxian Cai ◽  
Jiaqian Wu ◽  
Xiaoxiao Wu ◽  
Binbin Chen ◽  
...  

2021 ◽  
Vol 52 (1) ◽  
pp. 1358-1360
Author(s):  
Xiaowei Ye ◽  
Guangkun Liu ◽  
Zhou Zhang ◽  
Guowei Zha ◽  
Guanghui Liu

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 654.1-654
Author(s):  
T. Garvey ◽  
C. S. Crowson ◽  
M. Koster ◽  
K. J. Warrington

Background:Diagnostic methods for giant cell arteritis (GCA) have evolved over recent decades, and large vessel imaging plays an increasing role in disease detection.Objectives:This study aims to estimate the incidence of GCA over the past 10 years in a population and compare it to preceding incidence estimates. It also explores trends in the diagnostic modalities used to identify GCA.Methods:A pre-existing population-based cohort of patients diagnosed with GCA between 1950 and 2009 was extended with incident cases from 2010 to 2019. The diagnosis of GCA was confirmed by review of medical records of patients with ICD9/10 codes for GCA between 1/1/2010 and 12/31/2019. Incident cases that met either one of the following sets of inclusion criteria were added to the cohort: one, American College of Rheumatology 1990 GCA classification criteria; or two, patients aged ≥50 years with elevation of erythrocyte sedimentation rate or C-reactive protein and radiographic evidence of large vessel vasculitis attributed to GCA. Incident cases were classified into one of three groups: group 1, temporal artery biopsy (TAB) positive; group 2, TAB negative or not done with positive large-vessel imaging; or group 3, clinical diagnosis of GCA.Results:The study cohort included 305 patients diagnosed with GCA from 1950 until 2019. Fifty-five incident cases were diagnosed between 2010 and 2019; 37 females (67%) and 18 males (33%). The age and sex adjusted incidence rates (95% CI) per 100,000 between 2010 and 2019 for females, males, and the total population were 13.0 (8.8, 17.3), 8.6 (4.6, 12.7), and 10.8 (8.0, 13.7), respectively. The corresponding incidence rates from 2000-2009 were 28.0 (21.0, 35.1), 10.2 (5.0, 15.5), and 20.5 (15.9, 25.1), respectively. This represents a significant decline in the incidence rates in females (p<0.001) and the total group (p<0.001) between the 2000-2009 and 2010-2019 cohorts but no change in males (p=0.64). Of the 55 patients diagnosed between 2010 and 2019, there were 37 (67%) in group 1, 10 (18%) in group 2, and 8 (15%) in group 3. In contrast, of the 250 patients diagnosed between 1950 and 2009 there were 209 (84%) in group 1, 4 (2%) in group 2, and 37 (15%) in group 3. There was a significant difference between the 1950-2009 and 2010-2019 cohorts in the composition of these groups (p<0.001).Conclusion:In this population-based cohort of patients with GCA diagnosed over a 70-year period, the incidence of GCA has declined in recent years. The total decline is driven by a decline in females but not in males. The reasons for this are unclear but should be followed over time and investigated in other population-based cohorts. There has also been a shift in the diagnostic modalities for GCA. In recent years, there are fewer TAB positive patients, and more patients diagnosed with large vessel imaging. This is the first population-based incidence cohort demonstrating a trend towards increased use of large vessel imaging for the diagnosis of GCA.References:[1]Chandran AK, et al. Incidence of Giant Cell Arteritis in Olmsted County, Minnesota, over a 60-year period 1950-2009. Scand J Rheumatol. 2015;44(3):215-218.[2]Gonzalez-Gay MA, et al. Giant cell arteritis: is the clinical spectrum of the disease changing? BMC Geriatr. 2019; Jul 29;19(1):200.[3]Rubenstein E, et al. Sensitivity of temporal artery biopsy in the diagnosis of giant cell arteritis: a systemic literature review and meta-analysis. Rheumatology (Oxford). 2020 May 1:59(5):1011-1020.Figure 1.Trends in the incidence of GCA in Olmsted County by sex (1950-2019).Acknowledgements:This study was made possible using the resources of the Rochester Epidemiology Project, which is supported by the National Institute on Aging of the National Institutes of Health (NIH) under Award Number R01 AG034676, and CTSA Grant Number UL1 TR000135 from the National Center for Advancing Translational Sciences (NCATS), a component of the NIH. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.Disclosure of Interests:Thomas Garvey: None declared, Cynthia S. Crowson: None declared, Matthew Koster: None declared, Kenneth J Warrington Grant/research support from: Clinical research support from Eli Lilly and Kiniksa


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