Crowd detection and counting using a static and dynamic platform: state of the art

Automated object detection and crowd density estimation are popular and important area in visual surveillance research. The last decades witnessed many significant research in this field however, it is still a challenging problem for automatic visual surveillance. The ever increase in research of the field of crowd dynamics and crowd motion necessitates a detailed and updated survey of different techniques and trends in this field. This paper presents a survey on crowd detection and crowd density estimation from moving platform and surveys the different methods employed for this purpose. This review category and delineates several detections and counting estimation methods that have been applied for the examination of scenes from static and moving platforms

Multi scale entropy based adaptive fuzzy contrast image enhancement for crowd images

Contrast enhancement is a very important issue in image processing, pattern recognition and computer vision. Fuzzy logic based techniques perform enhancement using more detailed information of grayness of an image. However, these methods do not perform well on images taken in uncontrolled environment which pose different challenges such as illumination variation, perspective distortion and viewpoint variation. In this paper, we have worked to devise a more robust image enhancement method using fuzzy logic. We propose a novel multi scale entropy based measurement performed using fuzzy logic image processing and utilize it to define and enhance the contrast. For this purpose, we present a mathematical formula to calculate contrast using an adaptive amplification constant. Our approach uses both the local and global entropy information. We have experimented our algorithm on images from Crowd Counting UCF dataset, which contains very dense crowds and complex texture that stands in line with the challenges targeted in this paper. The results show an improved quality than original dataset images and prove that our method enhances the images with a more dynamic ranged contrast as well as better visual results

Neural Signaling and Communication

To understand the complex nature of the human brain, network science approaches have played an important role. Neural signaling and communication form the basis for studying the dynamics of brain activity and functions. The neuroscientific community is interested in the network architecture of the human brain its simulation and for prediction of emergent network states. In this chapter we focus on how neurosignaling and communication is playing its part in medical psychology, furthermore, we have also reviewed how the interaction of network topology and dynamic models of a brain network

Segmentation Method for Pathological Brain Tumor and Accurate Detection using MRI

Image segmentation is challenging task in field of medical image processing. Magnetic resonance imaging is helpful to doctor for detection of human brain tumor within three sources of images (axil, corneal, sagittal). MR images are nosier and detection of brain tumor location as feature is more complicated. Level set methods have been applied but due to human interaction they are affected so appropriate contour has been generated in discontinuous regions and pathological human brain tumor portion highlighted after applying binarization, removing unessential objects; therefore contour has been generated. Then to classify tumor for segmentation hybrid Fuzzy K Mean-Self Organization Mapping (FKM-SOM) for variation of intensities is used. For improved segmented accuracy, classification has been performed, mainly features are extracted using Discrete Wavelet Transformation (DWT) then reduced using Principal Component Analysis (PCA). Thirteen features from every image of dataset have been classified for accuracy using Support Vector Machine (SVM) kernel classification (RBF, linear, polygon) so results have been achieved using evaluation parameters like Fscore, Precision, accuracy, specificity and recall

Hybrid segmentation method with confidence region detection for tumor identification

Segmentation methods can mutually exclude the location of the tumor. However, the challenge of complex location or incomplete identification is located in segmentation challenge dataset. Identificationof tumor location is difficult due to the variation of intensities in MRI image. Vairation of intensity extends up to edema. Confidence Region with Contour Detection identifies the variation of intensities and level set algorithm (Region Scale Fitting) is used to delineate among the region of inner and outer of the tumor. Automatic feature selection method is required due to data complexity. An improved Self Organization Feature Map. Method is required. Weighted SOM Map selects a deterministic feature. This feature is one higher trained accuracy feature. When this specific feature is combines with cluster therefore it is known as deterministic feature clustering. This method gives confidence element. Confidence Region with Contour detection is facing the issue due to extended variations of intensities. These intensities are segmented by hybrid SOM Pixel Labelling with Reduce Cluster Membership and Deterministic Feature Clustering. This hyhbrid method segments the complex tumor intensities. This method produces a potential cluster which is achieved through the hybrid of three unsupervised learning techniques. Hybrid cluster method segments the tumor region. Extended intensities are also segmented by this hybrid approach. Above methods are validated on MICCAI BraTs brain tumor dataset, this is a segmentation challenge dataset. Proposed hybrid algorithm is efficient and it's accuracy can be seen with testing parameters like Dice Overlap Index, Jaccard Tanimoto Coefficient Index, Mean Squared Error and Peak Signal to Noise Ratio. Dice OverlapIndex is 98%, Jaccard Index is 96 percent, Mean Squared Error is 0.06 and Peak Signal To Noise ratio is 18db. The performance of the suggested algorithm is compared to other state of the art

Stereotactic radiosurgery for single brain metastases from non-small cell lung cancer: progression of extracranial disease correlates with distant intracranial failure

BackgroundLimited data exist regarding management of patients with a single brain lesion with extracranial disease due to non-small cell lung cancer (NSCLC).MethodsEighty-eight consecutive patients with a single brain lesion from NSCLC in the presence of extracranial disease were treated with stereotactic radiosurgery (SRS) alone. Local control (LC), distant intracranial failure (DIF), overall survival (OS), and toxicity were assessed. The logrank test was used to identify prognostic variables.ResultsMedian OS was 10.6 months. One-year DIF was 61%; LC 89%. Treatments were delivered in 1-5 fractions to median BED10 = 60Gy. Five patients developed radionecrosis. Factors associated with shortened OS included poor performance status (PS) (p = 0.0002) and higher Recursive Partitioning Analysis class (p = 0.017). For patients with PS 0, median survival was 22 months. DIF was associated with systemic disease status (progressive vs. stable) (p = 0.0001), as was BED (p = 0.021) on univariate analysis, but only systemic disease (p = 0.0008) on multivariate analysis.ConclusionsThis study identifies a patient population that may have durable intracranial control after treatment with SRS alone. These data support the need for prospective studies to optimize patient selection for up-front SRS and to characterize the impact of DIF on patients’ quality of life

Stereotactic radiosurgery for single brain metastases from non-small cell lung cancer: progression of extracranial disease correlates with distant intracranial failure

BackgroundLimited data exist regarding management of patients with a single brain lesion with extracranial disease due to non-small cell lung cancer (NSCLC).MethodsEighty-eight consecutive patients with a single brain lesion from NSCLC in the presence of extracranial disease were treated with stereotactic radiosurgery (SRS) alone. Local control (LC), distant intracranial failure (DIF), overall survival (OS), and toxicity were assessed. The logrank test was used to identify prognostic variables.ResultsMedian OS was 10.6 months. One-year DIF was 61%; LC 89%. Treatments were delivered in 1-5 fractions to median BED10 = 60Gy. Five patients developed radionecrosis. Factors associated with shortened OS included poor performance status (PS) (p = 0.0002) and higher Recursive Partitioning Analysis class (p = 0.017). For patients with PS 0, median survival was 22 months. DIF was associated with systemic disease status (progressive vs. stable) (p = 0.0001), as was BED (p = 0.021) on univariate analysis, but only systemic disease (p = 0.0008) on multivariate analysis.ConclusionsThis study identifies a patient population that may have durable intracranial control after treatment with SRS alone. These data support the need for prospective studies to optimize patient selection for up-front SRS and to characterize the impact of DIF on patients’ quality of life

Global prevalence and genotype distribution of hepatitis C virus infection in 2015 : A modelling study

Publisher Copyright: © 2017 Elsevier LtdBackground The 69th World Health Assembly approved the Global Health Sector Strategy to eliminate hepatitis C virus (HCV) infection by 2030, which can become a reality with the recent launch of direct acting antiviral therapies. Reliable disease burden estimates are required for national strategies. This analysis estimates the global prevalence of viraemic HCV at the end of 2015, an update of—and expansion on—the 2014 analysis, which reported 80 million (95% CI 64–103) viraemic infections in 2013. Methods We developed country-level disease burden models following a systematic review of HCV prevalence (number of studies, n=6754) and genotype (n=11 342) studies published after 2013. A Delphi process was used to gain country expert consensus and validate inputs. Published estimates alone were used for countries where expert panel meetings could not be scheduled. Global prevalence was estimated using regional averages for countries without data. Findings Models were built for 100 countries, 59 of which were approved by country experts, with the remaining 41 estimated using published data alone. The remaining countries had insufficient data to create a model. The global prevalence of viraemic HCV is estimated to be 1·0% (95% uncertainty interval 0·8–1·1) in 2015, corresponding to 71·1 million (62·5–79·4) viraemic infections. Genotypes 1 and 3 were the most common cause of infections (44% and 25%, respectively). Interpretation The global estimate of viraemic infections is lower than previous estimates, largely due to more recent (lower) prevalence estimates in Africa. Additionally, increased mortality due to liver-related causes and an ageing population may have contributed to a reduction in infections. Funding John C Martin Foundation.publishersversionPeer reviewe