Finger Vein Recognition Based on (2D)2 PCA and Metric Learning

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. In this paper, (2D)2 PCA is applied to extract features of finger veins, based on which a new recognition method is proposed in conjunction with metric learning. It learns a KNN classifier for each individual, which is different from the traditional methods where a fixed threshold is employed for all individuals. Besides, the SMOTE technology is adopted to solve the class-imbalance problem. Our experiments show that the proposed method is effective by achieving a recognition rate of 99.17%

Robust Ellipsoid Fitting Using Axial Distance and Combination

In random sample consensus (RANSAC), the problem of ellipsoid fitting can be formulated as a problem of minimization of point-to-model distance, which is realized by maximizing model score. Hence, the performance of ellipsoid fitting is affected by distance metric. In this paper, we proposed a novel distance metric called the axial distance, which is converted from the algebraic distance by introducing a scaling factor to solve nongeometric problems of the algebraic distance. There is complementarity between the axial distance and Sampson distance because their combination is a stricter metric when calculating the model score of sample consensus and the weight of the weighted least squares (WLS) fitting. Subsequently, a novel sample-consensus-based ellipsoid fitting method is proposed by using the combination between the axial distance and Sampson distance (CAS). We compare the proposed method with several representative fitting methods through experiments on synthetic and real datasets. The results show that the proposed method has a higher robustness against outliers, consistently high accuracy, and a speed close to that of the method based on sample consensus.Comment: 13 page

-Means Based Fingerprint Segmentation with Sensor Interoperability

A critical step in an automatic fingerprint recognition system is the segmentation of fingerprint images. Existing methods are usually designed to segment fingerprint images originated from a certain sensor. Thus their performances are significantly affected when dealing with fingerprints collected by different sensors. This work studies the sensor interoperability of fingerprint segmentation algorithms, which refers to the algorithm's ability to adapt to the raw fingerprints obtained from different sensors. We empirically analyze the sensor interoperability problem, and effectively address the issue by proposing a k-means based segmentation method called SKI. SKI clusters foreground and background blocks of a fingerprint image based on the k-means algorithm, where a fingerprint block is represented by a 3-dimensional feature vector consisting of block-wise coherence, mean, and variance (abbreviated as CMV). SKI also employs morphological postprocessing to achieve favorable segmentation results. We perform SKI on each fingerprint to ensure sensor interoperability. The interoperability and robustness of our method are validated by experiments performed on a number of fingerprint databases which are obtained from various sensors

A New Multistage Medical Segmentation Method Based on Superpixel and Fuzzy Clustering

The medical image segmentation is the key approach of image processing for brain MRI images. However, due to the visual complex appearance of image structures and the imaging characteristic, it is still challenging to automatically segment brain MRI image. A new multi-stage segmentation method based on superpixel and fuzzy clustering (MSFCM) is proposed to achieve the good brain MRI segmentation results. The MSFCM utilizes the superpixels as the clustering objects instead of pixels, and it can increase the clustering granularity and overcome the influence of noise and bias effectively. In the first stage, the MRI image is parsed into several atomic areas, namely, superpixels, and a further parsing step is adopted for the areas with bigger gray variance over setting threshold. Subsequently, designed fuzzy clustering is carried out to the fuzzy membership of each superpixel, and an iterative broadcast method based on the Butterworth function is used to redefine their classifications. Finally, the segmented image is achieved by merging the superpixels which have the same classification label. The simulated brain database from BrainWeb site is used in the experiments, and the experimental results demonstrate that MSFCM method outperforms the traditional FCM algorithm in terms of segmentation accuracy and stability for MRI image

Two-Level Evaluation on Sensor Interoperability of Features in Fingerprint Image Segmentation

Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating ability on images derived from a certain sensor may not adapt to segment images derived from other sensors. This degrades the segmentation performance. This paper empirically analyzes the sensor interoperability problem of segmentation feature, which refers to the feature’s ability to adapt to the raw fingerprints captured by different sensors. To address this issue, this paper presents a two-level feature evaluation method, including the first level feature evaluation based on segmentation error rate and the second level feature evaluation based on decision tree. The proposed method is performed on a number of fingerprint databases which are obtained from various sensors. Experimental results show that the proposed method can effectively evaluate the sensor interoperability of features, and the features with good evaluation results acquire better segmentation accuracies of images originating from different sensors

Selective degradation of hyperphosphorylated tau by proteolysis-targeting chimeras ameliorates cognitive function in Alzheimer’s disease model mice

Alzheimer’s disease (AD) is one of the most common chronic neurodegenerative diseases. Hyperphosphorylated tau plays an indispensable role in neuronal dysfunction and synaptic damage in AD. Proteolysis-targeting chimeras (PROTACs) are a novel type of chimeric molecule that can degrade target proteins by inducing their polyubiquitination. This approach has shown promise for reducing tau protein levels, which is a potential therapeutic target for AD. Compared with traditional drug therapies, the use of PROTACs to reduce tau levels may offer a more specific and efficient strategy for treating AD, with fewer side effects. In the present study, we designed and synthesized a series of small-molecule PROTACs to knock down tau protein. Of these, compound C8 was able to lower both total and phosphorylated tau levels in HEK293 cells with stable expression of wild-type full-length human tau (termed HEK293-htau) and htau-overexpressed mice. Western blot findings indicated that C8 degraded tau protein through the ubiquitin–proteasome system in a time-dependent manner. In htau-overexpressed mice, the results of both the novel object recognition and Morris water maze tests revealed that C8 markedly improved cognitive function. Together, our findings suggest that the use of the small-molecule PROTAC C8 to degrade phosphorylated tau may be a promising therapeutic strategy for AD

Finger Vein Recognition Based on a Personalized Best Bit Map

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition

Pathological Comparisons of the Hippocampal Changes in the Transient and Permanent Middle Cerebral Artery Occlusion Rat Models

© Copyright © 2019 Shah, Li, Kury, Zeb, Khatoon, Liu, Yang, Liu, Yao, Khan, Koh, Jiang and Li. Ischemic strokes are categorized by permanent or transient obstruction of blood flow, which impedes delivery of oxygen and essential nutrients to brain. In the last decade, the therapeutic window for tPA has increased from 3 to 5–6 h, and a new technique, involving the mechanical removal of the clot (endovascular thrombectomy) to allow reperfusion of the injured area, is being used more often. This last therapeutic approach can be done until 24 h after stroke onset. Due to this fact, more acute ischemic stroke patients are now being recanalized, and so tMCAO is probably the “best” model to address these patients that have a potential good outcome in terms of survival and functional recovery. However, permanent occlusion patients are also important, not only to increase survival rate but also to improve functional outcomes, although these are more difficult to achieve. So, both models are important, and which target different stroke patients in the clinical scenario. Hippocampus has a vital role in memory and cognition, is prone to ischemic induced neurodegeneration. This study was designed to delineate the molecular, pathological, and neurological changes in rat models of t-MCAO, permanent MCAO (pMCAO), and pMCAO with diabetic conditions in hippocampal tissue. Our results showed that these three models showed distinct discrepancies at numerous pathological process, including key signaling molecules involved in neuronal apoptosis, glutamate induced excitotoxicity, neuroinflammation, oxidative stress, and neurotrophic changes. Our result suggests that the two commonly used MCAO models exhibited tremendous differences in terms of neuronal cell loss, glutamate excitotoxic related signaling, synaptic transmission markers, neuron inflammatory and oxidative stress molecules. These differences may reflect the variations in different models, which may provide valuable information for mechanistic and therapeutic inconsistences as experienced in both preclinical models and clinical trials

Melatonin Protects MCAO-Induced Neuronal Loss via NR2A Mediated Prosurvival Pathways

Stroke is the significant cause of human mortality and sufferings depending upon race and demographic location. Melatonin is a potent antioxidant that exerts protective effects in differential experimental stroke models. Several mechanisms have been previously suggested for the neuroprotective effects of melatonin in ischemic brain injury. The aim of this study is to investigate whether melatonin treatment affects the glutamate N-methyl-D-aspartate (NMDA) and alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor signaling in cerebral cortex and striatum 24 h after permanent middle cerebral artery occlusion (MCAO). Melatonin (5 mg/kg) attenuated ischemia-induced down regulation of NMDA receptor 2 (NR2a), postsynaptic density-95 (PSD95) and increases NR2a/PSD95 complex association, which further activates the pro-survival PI3K/Akt/GSK3β pathway with mitigated collapsin response mediator protein 2 (CRMP2) phosphorylation. Furthermore, melatonin increases the expression of γ-enolase, a neurotrophic factor in ischemic cortex and striatum, and preserve the expression of presynaptic (synaptophysin and SNAP25) and postsynaptic (p-GluR1845) protein. Our study demonstrated a novel neuroprotective mechanism for melatonin in ischemic brain injury which could be a promising neuroprotective agent for the treatment of ischemic stroke