Adaptive Threshold Algorithm Research Articles

Improved Adaptive Vibe and the Application for Segmentation of Complex Background

To solve the problems that basic Vibe algorithm cannot effectively eliminate the influence of background noise, follower shadow, and ghost under complex background effectively, an adaptive threshold algorithm, AdaVibe, based on the framework of basic Vibe is proposed. Aiming at the shortage of the basic algorithm, this paper puts forward some improvement measures in threshold setting, shadow eliminating, and ghost suppression. Firstly, judgment threshold takes adjustment with the changes of background. Secondly, a fast eliminating ghost algorithm depending on adaptive threshold is introduced. Finally, follower shadow is detected and inhibited effectively through the gray properties and texture characteristics. Experiments show that the proposed AdaVibe algorithm works well in complex environment without affecting computing speed and has stronger robustness and better adaptability than the basic algorithm. Meanwhile, the ghost and follower shadow can be absorbed quickly as well. Therefore, the accuracy of target detection is effectively improved.

Characterization of Cerebral Vascular Response to EEG Bursts Using ICP Pulse Waveform Template Matching.

Neurovascular coupling is the relationship between the activity of the brain and the subsequent change in blood flow to the active region. The most common methods of detecting neurovascular coupling are cumbersome and noncontinuous. However, the integration of intracranial pressure (ICP) and electroencephalography (EEG) may serve as an indirect measure of neurovascular coupling.This study used data collected from burst-suppressed patients who received both ICP and depth EEG monitoring. An adaptive thresholding algorithm was used to detect the start and end of each EEG burst. The morphological clustering and analysis of ICP and pulse morphological template-matching algorithms were then applied to derive several metrics describing the shape of the ICP pulse waveform and track how it changed following an EEG burst. These changes were compared using a template obtained from patients undergoing CO2-induced vasodilation.All segments exhibited a significant period of vasodilation within 1-2 s after burst, and 4 of 5 had a significant period of vasoconstriction within 4-11 s of the EEG burst, suggesting that there might be a characteristic response of vasodilation and subsequent vasoconstriction after a spontaneous EEG burst. Furthermore, these findings demonstrate the potential of integrated EEG and ICP as an indirect measure of neurovascular coupling.

Optimization of Network Lifetime using Layer Determination Scheme with Node Rotation

The big challenge in wireless sensor network is maximizing network lifetime. The Minimization of Energy consumption is important to improve the network lifetime. Sensor nodes nearer to sink consume more energy than other nodes in the network. The nodes nearer to sink drain fast and cause death of the network. The layer based topology control scheme is used to select layers and arranges the nodes in layers according to energy consumption. In Mobile Sensor Relocation, the sensor nodes in high consumption position are relocated with lower consumption position within layers based on energy threshold using Distributed Consumption Rate Algorithm. The energy threshold is calculated using adaptive energy threshold algorithm. So we can use this approach to extend network lifetime by reducing rotation overhead and energy consumption.

Kernelized Fuzzy C-means Clustering with Adaptive Thresholding for Segmenting Liver Tumors

Accurate liver segmentation from computed tomography (CT) scan images is an essential and crucial step in computer-aided diagnosis of liver tumor. The automatic and unsupervised methods are used to segment CT image that reduce the time and eliminate the need for manual interface. In this paper, the adaptive threshold, morphological processing, and kernel fuzzy C-means (KFCM) clustering algorithm have been used with spatial information for visualizing and measuring the tumor area of liver which is segmented from CT abdominal images. The proposed process was tested and evaluated to a series of test images on MICCAI 2008 liver tumor segmentation challenge datasets to extract the tumor region. The area of the tumor reason has been calculated also. The KFCM algorithm introduces a kernel function on fuzzy c-means clustering (FCM) to reduce the effect of noise and improves the ability of clustering. Experimental results showed positive results for the proposed algorithm. The PSNR and MSE have been calculated for FCM and KFCM method to compare the results to segment the images. An experimental result shows that the proposed method could be used effectively to segment the liver cancer region in terms of high PSNR and low MSE values and achieves high consistency to detect small changes in images.

A novel thresholding based algorithm for detection of vertical root fracture in nonendodontically treated premolar teeth

In this paper, an efficient algorithm is proposed for detection of vertical root fractures (VRFs) in periapical (PA), and cone-beam computed tomography (CBCT) radiographs of nonendodontically treated premolar teeth. PA and CBCT images are divided into some sub-categories based on the fracture space between the two fragments as small, medium, and large for PAs and large for CBCTs. These graphics are first denoised using the combination of block matching 3-D filtering, and principle component analysis model. Then, we proposed an adaptive thresholding algorithm based on the modified Wellner model to segment the fracture and canal. Finally, VRFs are identified with a high accuracy through applying continuous wavelet transform on the segmented radiographs and choosing the most optimal value for sub-images based on the lowest interclass variance. Performance of the proposed algorithm is evaluated utilizing the different tested criteria. Results illustrate that the range of specificity deviations for PA and CBCT radiographs are 99.69 ± 0.22 and 99.02 ± 0.77, respectively. Furthermore, the sensitivity changes from 61.90 to 77.39 in the case of PA and from 79.54 to 100 in the case of CBCT. Based on our statistical evaluation, the CBCT imaging has the better performance in comparison with PA ones, so this technique could be a useful tool for clinical applications in determining the VRFs.

A novel automated image analysis method for counting the population of whiteflies on leaves of crops

Counting the population of insect pests is a key task for planning a successful integrated pest management program. Most image processing and machine vision techniques in the literature are very site-specific and cannot be easily re-usable because their performances are highly related to their ground truth data. In this article a new unsupervised image processing method is proposed which is general and easy to use for non-experts. In this method firstly a hypothesis framework is defined to distinguish pests from other particles in a captured image after texture, color and shape analyses. Then, the decision about each hypothesis is made by estimating a distribution function for sizes of particles which are presented in the image. Performance of the proposed method is evaluated on real captured images that belong to plants in green houses and farms with low and high densities of whiteflies. The obtained results show the greater ability of the proposed method in counting whiteflies on crop leaves compared to adaptive thresholding and K-means algorithms. Furthermore it is shown that better counting of the pest by proposed algorithm not only doesn't lead to extracting more false objects but also it decreases the rate of false detections compared to the results of the alternative algorithms.

Detecting and preventing selfish behaviour in mobile ad hoc network

In mobile ad hoc networks for big data transmission, nodes communicate with each other via intermediate nodes. However, some intermediate nodes may behave selfishly, resulting in reduced throughput and increased delay in the network. Thus, the existence of selfish behaviour in a mobile ad hoc network degrades the performance of the network. In this paper, we propose a scheme that detects selfish behaviour and prevents its occurrence in mobile ad hoc network. The proposed scheme uses an adaptive threshold algorithm to detect selfish behaviour, and prevents it based on a repeated games scheme. Extensive simulations using NS-2 showed that our scheme can effectively detect and prevent selfish behaviour.

A fusion algorithm for infrared and visible images based on saliency analysis and non-subsampled Shearlet transform

This paper proposed a novel fusion method for the infrared and visible image based on the accurate extraction of the target region. Firstly, the super-pixels-based saliency analysis method is used to extract the salient regions of the infrared image and obtain the coarse contour of the infrared target. Then the multi-directional detection operators and the adaptive threshold algorithm are used to refine the boundary of the target region and obtain the fusion decision map. In order to capture the details of the visible image, non-subsampled Shearlet transform (NSST) is used to select the fusion coefficients of the background. Experimental results indicate that the proposed method is superior to other state of the-art methods in subjective visual and objective performance.

Moving across scales: a quantitative assessment of X-ray CT to measure the porosity of rocks

We apply multidimensional X-ray CT to quantify the porosity of Berea Sandstone by using both medical- and synchrotron-based X-ray radiation, so as to produce images of the same sample with mm- and micron-resolution, respectively. Three different samples are used and the obtained tomograms are compared by considering the spatial distribution of porosity values for the range of voxel sizes 0.25–16 mm3. The agreement between the two independent techniques is assessed by means of the concordance correlation coefficient. Statistically significant correlations are found for each sample up to the maximum resolution of the medical CT scanner, i.e. for images with a voxel size of $$(0.5 \times 0.5 \times 1\,\hbox {mm})^{3}$$ . The direct comparison of images obtained by medical- and synchrotron-based X-ray radiation has a dual benefit. First, it objectively informs the segmentation step required for the binarization of the high-resolution synchrotron images that is otherwise prone to operator bias; in this context, the applicability of the proposed workflow is demonstrated with two widely applied locally adaptive thresholding algorithms, namely the hysteresis and the watershed methods. Secondly, once this calibration has occurred, the coupling of the two techniques allows analyzing porosity heterogeneity across a range of length-scales that spans over more than eight orders of magnitude. We anticipate that the ability to perform a true multi-scale experiment may represent the required point of departure for developing up-scaling approaches that capture the inherently complex heterogeneity of rocks.

AIS-Based Evaluation of Target Detectors and SAR Sensors Characteristics for Maritime Surveillance

This paper studies the performances of different ship detectors based on adaptive threshold algorithms. The detection algorithms are based on various clutter distributions and assessed automatically with a systematic methodology. Evaluation using large datasets of medium resolution SAR images and AIS (automatic identification system) data as ground truths allows to evaluate the efficiency of each detector. Depending on the datasets used for testing, the detection algorithms offer different advantages and disadvantages. The systematic method used in discriminating real detected targets and false alarms in order to determine the detection rate, allows us to perform an appropriate and consistent comparison of the detectors. The impact of SAR sensors characteristics (incidence angle, polarization, frequency and spatial resolution) is fully assessed, the vessels' length being also considered. Experiments are conducted on Radarsat-2 and CosmoSkymed ScanSAR datasets and AIS data acquired by coastal stations.

Adaptive monotone fast iterative shrinkage thresholding algorithm for fluorescence molecular tomography

Fluorescence molecular tomography is an ill-posed inverse problem. Considering the sparsity of the fluorescent source, authors proposed to alleviate this problem by including the L1-norm regularisation term in the objective function. To obtain a solution to such an optimisation problem, an innovative version of the traditional over-relaxation algorithm was proposed by including additional procedures for updating the step size and the regularisation parameter adaptively. Simulation results demonstrate that our proposed algorithm can improve the reconstruction accuracy and convergence speed effectively as compared with existed algorithms such as the perturbation algorithm and the over-relaxation algorithm.

Flicker defined form, standard perimetry and Heidelberg retinal tomography: Structure-function relationships

ObjectiveTo compare flicker defined form (FDF) perimetry using the Heidelberg edge perimeter (HEP) with standard automated perimetry (SAP) on the Humphrey visual field (HVF) analyzer and to compare their relationship to structural measurements acquired with the Heidelberg retina tomograph. DesignProspective, observational study. ParticipantsThirty-one glaucomatous eyes with varying severity and 13 normal control eyes were included in this analysis. MethodsAll subjects underwent FDF testing on the HEP using the 24-2 protocol by the adaptive staircase thresholding algorithm standard strategy and SAP on the HVF analyzer 750 II using the SITA-Standard 24-2 test. Heidelberg retina tomography (HRT) testing was obtained for each patient. Spearman correlation coefficient, mean deviation (MD), and pattern standard deviation measurements by both machines were compared. ResultsFDF and SAP MD were significantly correlated (r = 0.81, p < 0.001). FDF and SAP MD were significantly correlated with HRT cup/disc ratio (FDF MD: p < 0.001; SAP MD: p = 0.003), disc area (FDF MD: p = 0.005; SAP MD: p = 0.059), rim volume (FDF MD: p < 0.001; SAP MD: p < 0.001), and retinal nerve fibre layer (FDF MD: p < 0.001; SAP MD: p < 0.001). ConclusionsThis pilot study shows that the MD parameter of FDF correlated with SAP results. FDF and SAP had significant correlations with HRT parameters in glaucomatous and healthy eyes. The potential utility of FDF in the clinical management of glaucoma requires further investigation.

On-line Rock Fragment Delineation on Simple Thresholding with Fuzzy Comprehensive Evaluation

The classification and measurement of rock fragments is very important in mining and construction engineering. The monitoring system acquires and analyses the fragment images from a gravitational falling stream at the end of a moving conveyor belt, and the key function of the system is to construct an image segmentation algorithm. To achieve this goal, an adaptive thresholding algorithm with fuzzy comprehensive evaluation is proposed. Firstly, a grabbed image is roughly segmented by using a global auto-thresholding algorithm. Then each of the objects is measured and analyzed if it includes the multiple fragments touching each other, based on the fuzzy comprehensive evaluation method in which the salient fragment features of area, perimeter, shape, gradient magnitude and gray-level flatness are extracted, and for each of the features, the membership function is constructed experimentally. Finally, each of the touching fragment regions, as one image, is auto-thresholded again, and this procedure is repeated until no region can be further separated. The experimental results show that compared to cluster analysis, graph based, and FCM image segmentation algorithms the new algorithm can make image segmentation well for the falling fragments on-line. Keyword: Rock fragment image, thresholding, fuzzy comprehensive evaluation

Performance comparison of two resolution modeling PET reconstruction algorithms in terms of physical figures of merit used in quantitative imaging

PurposeResolution modeling (RM) of PET systems has been introduced in iterative reconstruction algorithms for oncologic PET. The RM recovers the loss of resolution and reduces the associated partial volume effect. While these methods improved the observer performance, particularly in the detection of small and faint lesions, their impact on quantification accuracy still requires thorough investigation. The aim of this study was to characterize the performances of the RM algorithms under controlled conditions simulating a typical 18F-FDG oncologic study, using an anthropomorphic phantom and selected physical figures of merit, used for image quantification. MethodsMeasurements were performed on Biograph HiREZ (B_HiREZ) and Discovery 710 (D_710) PET/CT scanners and reconstructions were performed using the standard iterative reconstructions and the RM algorithms associated to each scanner: TrueX and SharpIR, respectively. ResultsRM determined a significant improvement in contrast recovery for small targets (≤17 mm diameter) only for the D_710 scanner. The maximum standardized uptake value (SUVmax) increased when RM was applied using both scanners. The SUVmax of small targets was on average lower with the B_HiREZ than with the D_710. Sharp IR improved the accuracy of SUVmax determination, whilst TrueX showed an overestimation of SUVmax for sphere dimensions greater than 22 mm. The goodness of fit of adaptive threshold algorithms worsened significantly when RM algorithms were employed for both scanners. ConclusionsDifferences in general quantitative performance were observed for the PET scanners analyzed. Segmentation of PET images using adaptive threshold algorithms should not be undertaken in conjunction with RM reconstructions.

Automated extraction of tidal creeks from airborne laser altimetry data

Summary Tidal creeks (TCs) are transitional waterways between terrestrial and marine environments. Extracting geometric information for tidal creek networks (TCN) geometry from remote sensing is essential to understanding their characteristics, formation, and evolution. Currently, the major obstacles to automated recognition using digital elevation models (DEMs) derived from airborne light detection and ranging (LiDAR) data are low relief, varying widths, high density, strong anisotropy, and complicated patterns. Conventional methods, such as the optimal-elevation threshold method, the optimal-curvature threshold method, and the D8 method, cannot achieve satisfactory performance under these conditions. We propose an automated method for extracting tidal creeks (AMETC) using topographic features detected from LiDAR. Specifically, a multi-window median neighborhood analysis was designed to enhance depressions both in mudflat and marsh environments; a multi-scale and multi-directional Gaussian-matched filtering method was incorporated to enhance width-variant TCs; and a two-stage adaptive thresholding algorithm was implemented to segment low-contrast TCs. The AMETC was tested on two large LiDAR datasets of the Jiangsu coast with different resolutions. The quantitative assessments show that AMETC successfully extracted both small and large TCs from our study areas. The true positive extraction rate reached 95%, outperforming conventional methods. The AMETC is robust and weakly dependent on scale, and rarely requires manual intervention. Further applications suggests that the AMETC has potential for extraction of other types of channel features (e.g., badland networks and ravines).

Feasibility and performance of an adaptive contrast-oriented FDG PET/CT quantification technique for global disease assessment of malignant pleural mesothelioma and a brief review of the literature.

Treatment of malignant pleural mesothelioma (MPM) remains very challenging. Assessment of response to treatment is necessary for modifying treatment and using new drugs. Global disease assessment (GDA) by implementing image processing methods to extract more information out of positron emission tomography (PET) images may provide reliable information. In this study we show the feasibility of this method of semi-quantification in patients with mesothelioma, and compare it with the conventional methods. We also present a review of the literature about this topic. Nineteen subjects with histologically proven MPM who had undergone fluoride-18-fluorodeoxyglucose PET/computed tomography ((18)F-FDG PET/CT) before and after treatment were included in this study. An adaptive contrast-oriented thresholding algorithm was used for the image analysis and semi-quantification. Metabolic tumor volume (MTV), maximum and mean standardized uptake volume (SUVmax, SUVmean) and total lesion glycolysis (TLG) were calculated for each region of interest. The global tumor glycolysis (GTG) was obtained by summing up all TLG. Treatment response was assessed by the European Organisation for Research and Treatment of Cancer (EORTC) criteria and the changes of GTG. Agreement between global disease assessment and conventional method was also determined. In patients with progressive disease based on EORTC criteria, GTG showed an increase of 150.7 but in patients with stable or partial response, GTG showed a decrease of 433.1. The SUVmax of patients before treatment was 5.95 (SD: 2.93) and after the treatment it increased to 6.38 (SD: 3.19). Overall concordance of conventional method with GDA method was 57%. Concordance of progression of disease based on conventional method was 44%, stable disease was 85% and partial response was 33%. Discordance was 55%, 14% and 66%. Adaptive contrast-oriented thresholding algorithm is a promising method to quantify the whole tumor glycolysis in patients with mesothelioma. We are able to assess the total metabolic lesion volume, lesion glycolysis, SUVmax, tumor SUVmean and GTG for this particular tumor. Also we were able to demonstrate the potential use of this technique in the monitoring of treatment response. More studies comparing this technique with conventional and other global disease assessment methods are needed in order to clarify its role in the assessment of treatment response and prognosis of these patients.

Atrial late gadolinium enhancement on MRI relates to the electrophysiological substrate of persistent atrial fibrillation

Background The mechanisms responsible for atrial fibrillation (AF) maintenance are poorly understood. We studied the relationship between focal atrial fibrosis as assessed with late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR), and the electrophysiological substrate of persistent AF as assessed with body surface potential mapping (BSM). Methods We studied 41 patients with persistent AF (6 women, age 56+/-12 years). Patients underwent LGE CMR using an respiratory navigated and inversion recovery prepared 3D turbo FLASH sequence with fat saturation (pixel size 1.25×1.25×2.5mm), as well as non-invasive BSM during atrial fibrillation using a 256-electrode vest, enabling real-time panoramic mapping of atrial electrical activation. On CMR images, the bi-atrial wall was manually segmented and LGE was quantified using an adaptive histogram thresholding algorithm. The result was both a global quantification of LGE on the left atrial wall expressed in % of the wall and categorized according to Utah classification (I: 40%), and a patient-specific 3D map displaying LGE distribution on both atria. On BSM data, phase mapping was applied to visualize electrical activation, and atrial fibrillation drivers were defined as rotors (phase singularities) lasting more than 200ms. The sites exhibiting high rotor activity were defined as driving regions and targeted by catheter ablation, with the acute endpoint of AF termination. We assessed the relationship between global LGE burden and patients’ clinical characteristics, electrophysiological characteristics, and acute procedural success. In a subset of 12 patients, CMR and BSM data were registered to assess the spatial relationship between LGE and AF drivers.

DDoS Attack Detection Using Fast Entropy Approach on Flow- Based Network Traffic

Denial of service attack and Distributed Denial of Service attacks are becoming an increasingly frequent disturbance of the global Internet. In this paper we propose improvement in detection of Distributed Denial of Service attacks based on fast entropy method using flow-based analysis. An adaptive threshold algorithm is made use of since both network activities and user's behavior could vary over time. Fast Entropy and flow-based analysis show significant reduction in computational time compared to conventional entropy computation while maintaining good detection accuracy. The network traffic is analyzed and fast entropy of request per flow is calculated. DDoS attack is detected when the difference between entropy of flow count at each instant and mean value of entropy in that time interval is greater than the threshold value that is updated adaptively based on traffic pattern condition to improve the detection accuracy.

Adaptive Threshold and Hysteresis for Handoff Initiation in Next Generation Networks from Path Loss Model

Mobility management is evolving as the most astounding area of research for the upcoming researchers. Many issues such as signalling overhead, latency, low bandwidth, low reliability, etc., existed in traditional mobility schemes in wireless networks. But the next-generation heterogeneous networks have resolved many issues and researchers are looking forward to take best use of upcoming generations to provide constant connectivity. In this paper, a fuzzy logic-based adaptive threshold and adaptive hysteresis handoff initialisation algorithm is proposed. It uses data rate, received signal strength indicator and SINR to determine the value of threshold and hysteresis. This algorithm makes use of Okumura Hata Path loss model to determine the value of RSSI, which is then given to fuzzy inference engine. It has been shown that threshold and hysteresis are not only dependent on RSSI but also on many other parameters such as on transmitter height, receiver height and antenna gain, frequency and cable loss.

An Adaptive Thresholding Method for BTV Estimation Incorporating PET Reconstruction Parameters: A Multicenter Study of the Robustness and the Reliability.

Objective. The aim of this work was to assess robustness and reliability of an adaptive thresholding algorithm for the biological target volume estimation incorporating reconstruction parameters. Method. In a multicenter study, a phantom with spheres of different diameters (6.5–57.4 mm) was filled with 18F-FDG at different target-to-background ratios (TBR: 2.5–70) and scanned for different acquisition periods (2–5 min). Image reconstruction algorithms were used varying number of iterations and postreconstruction transaxial smoothing. Optimal thresholds (TS) for volume estimation were determined as percentage of the maximum intensity in the cross section area of the spheres. Multiple regression techniques were used to identify relevant predictors of TS. Results. The goodness of the model fit was high (R 2: 0.74–0.92). TBR was the most significant predictor of TS. For all scanners, except the Gemini scanners, FWHM was an independent predictor of TS. Significant differences were observed between scanners of different models, but not between different scanners of the same model. The shrinkage on cross validation was small and indicative of excellent reliability of model estimation. Conclusions. Incorporation of postreconstruction filtering FWHM in an adaptive thresholding algorithm for the BTV estimation allows obtaining a robust and reliable method to be applied to a variety of different scanners, without scanner-specific individual calibration.