A New Preprocessing Algorithm of Hand Vein Image

2013 ◽  
Vol 462-463 ◽  
pp. 312-315
Author(s):  
Cai Xia Liu
Keyword(s):  
Wavelet Decomposition ◽  
Feature Recognition ◽  
Hot Spot ◽  
Low Frequency ◽  
Histogram Equalization ◽  
Segmentation Algorithm ◽  
Human Hand ◽  
Security Technology ◽  
Hand Vein ◽  
Vein Recognition

Biometrics technology is an important security technology and the research of it has become a new hot spot for its superior security features. Then hand vein recognition is a new biological feature recognition which has many advantages, such as safety, non-contact. According to the features of human hand vein image, a hand vein preprocessing method based on wavelet transform and windows maximum between-class difference method threshold (OTSU) segmentation algorithm is proposed. In this paper, the hand vein image is enhanced by adaptive histogram equalization in low frequency part of the hand vein image after wavelet decomposition and filtering before feature extraction. Then the windows OTSU threshold segmentation algorithm is used to get the features. The experimental results show that this method is simple and easy to realize and has laid a good foundation for the latter part of the vein recognition.

scholarly journals DEEPGENTM—A Novel Variant Calling Assay for Low Frequency Variants

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 507
Author(s):  
Bernd Timo Hermann ◽  
Sebastian Pfeil ◽  
Nicole Groenke ◽  
Samuel Schaible ◽  
Robert Kunze ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Genetic Variants ◽  
Liquid Biopsy ◽  
Hot Spot ◽  
Treatment Success ◽  
Low Frequency ◽  
Variant Calling ◽  
Subsequent Treatment ◽  
Precision Oncology ◽  
Orthogonal Comparison

Detection of genetic variants in clinically relevant genomic hot-spot regions has become a promising application of next-generation sequencing technology in precision oncology. Effective personalized diagnostics requires the detection of variants with often very low frequencies. This can be achieved by targeted, short-read sequencing that provides high sequencing depths. However, rare genetic variants can contain crucial information for early cancer detection and subsequent treatment success, an inevitable level of background noise usually limits the accuracy of low frequency variant calling assays. To address this challenge, we developed DEEPGENTM, a variant calling assay intended for the detection of low frequency variants within liquid biopsy samples. We processed reference samples with validated mutations of known frequencies (0%–0.5%) to determine DEEPGENTM’s performance and minimal input requirements. Our findings confirm DEEPGENTM’s effectiveness in discriminating between signal and noise down to 0.09% variant allele frequency and an LOD(90) at 0.18%. A superior sensitivity was also confirmed by orthogonal comparison to a commercially available liquid biopsy-based assay for cancer detection.

PATH-40. SPORADIC NF2 WILD-TYPE MULTIPLE MENINGIOMAS HARBOR DISTINCT DRIVER MUTATIONS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi124-vi124
Author(s):  
Insa Prilop ◽  
Thomas Pinzer ◽  
Daniel Cahill ◽  
Priscilla Brastianos ◽  
Gabriele Schackert ◽  
...  
Keyword(s):  
Hot Spot ◽  
Low Frequency ◽  
Neurofibromatosis Type ◽  
Driver Mutations ◽  
Wild Type ◽  
Driver Genes ◽  
Who Grade ◽  
Genetic Changes ◽  
Histologic Subtype ◽  
Multiple Meningiomas

Abstract OBJECTIVE Multiple meningiomas (MM) are rare and present a unique management challenge. While the mutational landscape of single meningiomas has been extensively studied, understanding the molecular pathogenesis of sporadic MM remains incomplete. The objective of this study is to elucidate the genetic features of sporadic MM. METHODS We identified nine patients with MM (n=19) defined as ≥2 spatially separated synchronous or metachronous meningiomas. We profiled genetic changes in these tumors using next-generation sequencing (NGS) assay that covers a large number of targetable and frequently mutated genes in meningiomas including AKT1, KLF4, NF2, PIK3CA/PIK3R1, POLR2A, SMARCB1, SMO, SUFU, TRAF7, and the TERT promoter. RESULTS Most of MM were WHO grade 1 (n= 16, 84.2%). Within individual patients, no driver mutation was shared between separate tumors. All but two cases harbored different hot spot mutations in known meningioma-driver genes like TRAF7 (n= 5), PIK3CA (n= 4), AKT1 (n= 3), POLR2A (n=1) and SMO (n= 1). Moreover, individual tumors differed in histologic subtype in 8/9 patients. The low frequency of NF2 mutations in our series stands in contrast to previous studies that included hereditary cases arising in the setting of neurofibromatosis type 2 (NF2). CONCLUSIONS Our findings provide evidence for genomic inter-tumor heterogeneity and an independent molecular origin of sporadic NF2 wild-type MM. Furthermore, these findings suggest that genetic characterization of each lesion is warranted in sporadic MM.

Hand Vein Recognition Based on Feature Fusion

2014 ◽  
Vol 43 (1) ◽  
pp. 110004
Author(s):  
胡云朋 HU Yun-peng ◽  
王志勇 WANG Zhi-yong ◽  
李飞 LI Fei ◽  
杨晓苹 YANG Xiao-ping ◽  
薛玉明 XUE Yu-ming
Keyword(s):  
Feature Fusion ◽  
Hand Vein ◽  
Vein Recognition

scholarly journals Recognition of Dorsal Hand Vein Based Bit Planes and Block Mutual Information

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3718 ◽  
Author(s):  
Yiding Wang ◽  
Heng Cao ◽  
Xiaochen Jiang ◽  
Yuanyan Tang
Keyword(s):  
Mutual Information ◽  
Recognition Rate ◽  
Scale Invariant ◽  
Dorsal Hand ◽  
Hand Vein ◽  
Bit Plane ◽  
Vein Recognition ◽  
Dorsal Hand Vein ◽  
Bit Planes ◽  
Dorsal Hand Vein Recognition

The dorsal hand vein images captured by cross-device may have great differences in brightness, displacement, rotation angle and size. These deviations must influence greatly the results of dorsal hand vein recognition. To solve these problems, the method of dorsal hand vein recognition was put forward based on bit plane and block mutual information in this paper. Firstly, the input gray image of dorsal hand vein was converted to eight-bit planes to overcome the interference of brightness inside the higher bit planes and the interference of noise inside the lower bit planes. Secondly, the texture of each bit plane of dorsal hand vein was described by a block method and the mutual information between blocks was calculated as texture features by three kinds of modes to solve the problem of rotation and size. Finally, the experiments cross-device were carried out. One device was used to be registered, the other was used to recognize. Compared with the SIFT (Scale-invariant feature transform, SIFT) algorithm, the new algorithm can increase the recognition rate of dorsal hand vein from 86.60% to 93.33%.

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