Variable Threshold Method Research Articles

Exploration of the importance of physical properties of Indonesian peatlands to assess critical groundwater table depths, associated drought and fire hazard

Widespread degradation of Indonesian peatlands by deforestation and excessive drainage results into more frequent fires, particularly in El Niño years, which causes: (i) release of enormous amounts of peat soil carbon to the atmosphere, impacting climate, (ii) severe air pollution, affecting human health and air traffic, and (iii) decreased ecosystem services through loss of biodiversity. Groundwater table decline is the main driver of these negative processes and, therefore restoration of peatland hydrology is essential. Although groundwater table depth is critical to counteract peatland degradation, optimal depths are not generic for all peatlands, but depend on peat physical properties (i.e. water retention, unsaturated conductivity), which are related to the degree of peat humification (Fibric, Hemic, Sapric). Unfortunately only few of these peat physical properties are available while they are essential input data in hydrological models required to extend the usually short observed groundwater hydrographs. An experiment with the Soil-Water-Atmosphere-Plant model (SWAP) for two locations in Indonesian peatlands illustrates the impact of the degree of peat humification on physical properties and thereby on calculated groundwater table depth, hydrological drought and associated fires hazards. The Variable Threshold Method is applied to convert groundwater table depths into hydrological drought, and next the modified Keetch-Byram Drought Index (mKBDI) is used to assess wildfire hazard. Peat physical properties that reflect higher peat humification (Hemic and Sapric) result into lower water tables during dry periods, in particular during El Niño years, more severe hydrological drought, and an earlier and longer fire season. Using the limited available peat physical properties the importance is demonstrated of initiating a comprehensive programme to build a database of peat physical properties covering different environmental conditions in which tropical peatlands occur. Availability of such a database connected to a long-term monitoring programme, will support the ongoing rewetting, revegetation and revitalisation programme for Indonesian peatlands, which eventually will contribute to sustainable livelihoods for local people and reduce impact on the regional climate.

A Novel Multiscale Gaussian-Matched Filter Using Neural Networks for the Segmentation of X-Ray Coronary Angiograms.

The accurate and efficient segmentation of coronary arteries in X-ray angiograms represents an essential task for computer-aided diagnosis. This paper presents a new multiscale Gaussian-matched filter (MGMF) based on artificial neural networks. The proposed method consists of two different stages. In the first stage, MGMF is used for detecting vessel-like structures while reducing image noise. The results of MGMF are compared with those obtained using six GMF-based detection methods in terms of the area (Az) under the receiver operating characteristic (ROC) curve. In the second stage, ten thresholding methods of the state of the art are compared in order to classify the magnitude of the multiscale Gaussian response into vessel and nonvessel pixels, respectively. The accuracy measure is used to analyze the segmentation methods, by comparing the results with a set of 100 X-ray coronary angiograms, which were outlined by a specialist to form the ground truth. Finally, the proposed method is compared with seven state-of-the-art vessel segmentation methods. The vessel detection results using the proposed MGMF method achieved an Az = 0.9357 with a training set of 50 angiograms and Az = 0.9362 with the test set of 50 images. In addition, the segmentation results using the intraclass variance thresholding method provided a segmentation accuracy of 0.9568 with the test set of coronary angiograms.

Micro-Droplet Detection Method for Measuring the Concentration of Alkaline Phosphatase-Labeled Nanoparticles in Fluorescence Microscopy.

This paper developed and evaluated a quantitative image analysis method to measure the concentration of the nanoparticles on which alkaline phosphatase (AP) was immobilized. These AP-labeled nanoparticles are widely used as signal markers for tagging biomolecules at nanometer and sub-nanometer scales. The AP-labeled nanoparticle concentration measurement can then be directly used to quantitatively analyze the biomolecular concentration. Micro-droplets are mono-dispersed micro-reactors that can be used to encapsulate and detect AP-labeled nanoparticles. Micro-droplets include both empty micro-droplets and fluorescent micro-droplets, while fluorescent micro-droplets are generated from the fluorescence reaction between the APs adhering to a single nanoparticle and corresponding fluorogenic substrates within droplets. By detecting micro-droplets and calculating the proportion of fluorescent micro-droplets to the overall micro-droplets, we can calculate the AP-labeled nanoparticle concentration. The proposed micro-droplet detection method includes the following steps: (1) Gaussian filtering to remove the noise of overall fluorescent targets, (2) a contrast-limited, adaptive histogram equalization processing to enhance the contrast of weakly luminescent micro-droplets, (3) an red maximizing inter-class variance thresholding method (OTSU) to segment the enhanced image for getting the binary map of the overall micro-droplets, (4) a circular Hough transform (CHT) method to detect overall micro-droplets and (5) an intensity-mean-based thresholding segmentation method to extract the fluorescent micro-droplets. The experimental results of fluorescent micro-droplet images show that the average accuracy of our micro-droplet detection method is 0.9586; the average true positive rate is 0.9502; and the average false positive rate is 0.0073. The detection method can be successfully applied to measure AP-labeled nanoparticle concentration in fluorescence microscopy.

5-1 可変しきい値を用いたリニア出力型広ダイナミックレンジ撮像デバイス

5-1 可変しきい値を用いたリニア出力型広ダイナミックレンジ撮像デバイス

Variable Threshold Method for Determining the Boundaries of Imaged Subvisible Particles

An accurate assessment of particle characteristics and concentrations in pharmaceutical products by flow imaging requires accurate particle sizing and morphological analysis. Analysis of images begins with the definition of particle boundaries. Commonly a single threshold defines the level for a pixel in the image to be included in the detection of particles, but depending on the threshold level, this results in either missing translucent particles or oversizing of less transparent particles due to the halos and gradients in intensity near the particle boundaries. We have developed an imaging analysis algorithm that sets the threshold for a particle based on the maximum gray value of the particle. We show that this results in tighter boundaries for particles with high contrast, while conserving the number of highly translucent particles detected. The method is implemented as a plugin for FIJI, an open-source image analysis software. The method is tested for calibration beads in water and glycerol/water solutions, a suspension of microfabricated rods, and stir-stressed aggregates made from IgG. The result is that appropriate thresholds are automatically set for solutions with a range of particle properties, and that improved boundaries will allow for more accurate sizing results and potentially improved particle classification studies.

A novel Gaussian matched filter based on entropy minimization for automatic segmentation of coronary angiograms

This paper presents a new method for automatic detection and segmentation of coronary arteries in X-ray angiograms. In the vessel detection stage, a novel Gaussian matched filter (GMF) based on an entropy minimization fitness function is used to detect blood vessels in angiographic images. The detection results of the proposed Gaussian matched filter are compared with those obtained by five state-of-the-art GMF-based methods using the area (Az) under the receiver operating characteristic (ROC) curve. In the second stage, the inter-class variance thresholding method has proven to be the most efficient compared with six different methods in order to classify vessel and non vessel pixels from the Gaussian filter response using the accuracy measure and the ground-truth angiograms drawn by a specialist. Finally, the proposed method is compared with eight state-of-the-art vessel segmentation methods. Due to the high rating of similarity (0.97) between the highest Az value and the Az value acquired by the fitness function over the whole dataset of angiograms, the result of vessel detection using the proposed GMF demonstrated high performance achieving Az=0.945 with a test set of 45 angiograms. In addition, the results of vessel segmentation with the inter-class variance thresholding method provided an accuracy of 0.961 with the test set of angiograms.

Segmentation of Coronary Angiograms Using Gabor Filters and Boltzmann Univariate Marginal Distribution Algorithm.

This paper presents a novel method for improving the training step of the single-scale Gabor filters by using the Boltzmann univariate marginal distribution algorithm (BUMDA) in X-ray angiograms. Since the single-scale Gabor filters (SSG) are governed by three parameters, the optimal selection of the SSG parameters is highly desirable in order to maximize the detection performance of coronary arteries while reducing the computational time. To obtain the best set of parameters for the SSG, the area (A z) under the receiver operating characteristic curve is used as fitness function. Moreover, to classify vessel and nonvessel pixels from the Gabor filter response, the interclass variance thresholding method has been adopted. The experimental results using the proposed method obtained the highest detection rate with A z = 0.9502 over a training set of 40 images and A z = 0.9583 with a test set of 40 images. In addition, the experimental results of vessel segmentation provided an accuracy of 0.944 with the test set of angiograms.

Monitoring Individual Wave Characteristics in the Inner Surf with a 2-Dimensional Laser Scanner (LiDAR)

This paper presents an investigation into the use of a 2-dimensional laser scanner (LiDAR) to obtain measurements of wave processes in the inner surf and swash zones of a microtidal beach (Rousty, Camargue, France). The bed is extracted at the wave-by-wave timescale using a variance threshold method on the time series. Individual wave properties were then retrieved from a local extrema analysis. Finally, individual and averaged wave celerities are obtained using a crest-tracking method and cross-correlation technique, respectively, and compared with common wave celerity predictors. Very good agreement was found between the individual wave properties and the wave spectrum analysis, showing the great potential of the scanner to be used in the surf and swash zone for studies of nearshore waves at the wave-by-wave timescale.

Associating rare genetic variants with human diseases.

Genome researches have revealed that a large portion (over 50%) of genetic variants on human chromosomes are rare variants (RVs) with extremely low allele frequency (usually defined as less than 1%) in populations. In recent years, advances of DNA genotyping and sequencing technologies have been facilitating the discovery of RVs, and the association between RVs and human diseases is of rapidly growing interest in understanding genetic and molecular mechanism of both common and rare diseases (Cirulli and Goldstein, 2010; Gibson, 2012).

Oil Leak Image Data Analysis Based on Maximum Variance Threshold Method

The oil leak is detected by the computer image recognition technique and the oil leak images are processed by the threshold segmentation and semi-thresholding of the maximum variance threshold method. Through capture of fluorescent images of leakage, the oil leak image information is acquired. The maximum variance threshold method is adopted for image data analysis to compute the dilation area of oil images and achieve the detection of leakage.

Tracking Target Identification Model Based on Multiple Algorithms

In view of current situation of bad data synchronization, image blurring and tracking station stability in tracking target identification, a kind of tracking target identification model based on multiple algorithms was put forward, firstly establishing the image degradation model, using the wavelet algorithm for image preprocessing, doing image edge segmentation by using Robert algorithm after pretreatment, then using the maximum variance threshold method for image threshold segmentation, then extracting target features from the segmented image, and finally using the ABS algorithm to finish target tracking. Experiments proved the proposed model practical and effective.

Study on the Image Processing and Recognition Algorithm for Portable Precision Visual Measurement

In portable precision visual measurement, processing result of the measurement image has an important influence on final measurement accuracy in large field working space. However, the images taken in large field working space always have the following characteristics: containing a large amount of information, having a high redundancy and including a lot of background noises. In order to solve these problems, precision image processing and recognition algorithm is researched. Firstly, portable precision visual measurement method is introduced. Then, the design method of global control points including noncoding points and coding points is displayed. Finally, the image processing and recognition algorithm is investigated in detail, including the largest variance threshold method used to partly binary the image, the detection algorithm of global control points based on the constraint of distance and acreage and coding value recognition method through correcting the image feature. A large amount of experiments show that the image processing and recognition algorithm is accurate and effective to processing the image taken in the working space.

Button Cell Characterization Testing Technology Research Based on Computer Vision

The use of computer vision to detect and recognize target objects in the industrial field has high academic value. In this paper, computer vision is used to identify cell’s label and detect the surface scratch. Using inter variance threshold method of image acquisition threshold processing, to obtain a better image binarization , subsequent using characterization information to establish a template, and the template using mathematical morphology operations. Object matching is achieved by matrix change of the template , in order to achieve the detection. The experiments show that this method has higher accuracy and better real-time, so online detection can be used for battery characterization.

Length of Growing Period over Africa: Variability and Trends from 30 Years of NDVI Time Series

The spatial distribution of crops and farming systems in Africa is determined by the duration of the period during which crop and livestock water requirements are met. The length of growing period (LGP) is normally assessed from weather station data—scarce in large parts of Africa—or coarse-resolution rainfall estimates derived from weather satellites. In this study, we analyzed LGP and its variability based on the 1981–2011 GIMMS NDVI3g dataset. We applied a variable threshold method in combination with a searching algorithm to determine start- and end-of-season. We obtained reliable LGP estimates for arid, semi-arid and sub-humid climates that are consistent in space and time. This approach effectively mapped bimodality for clearly separated wet seasons in the Horn of Africa. Due to cloud contamination, the identified bimodality along the Guinea coast was judged to be less certain. High LGP variability is dominant in arid and semi-arid areas, and is indicative of crop failure risk. Significant negative trends in LGP were found for the northern part of the Sahel, for parts of Tanzania and northern Mozambique, and for the short rains of eastern Kenya. Positive trends occurred across western Africa, in southern Africa, and in eastern Kenya for the long rains. Our LGP analysis provides useful information for the mapping of farming systems, and to study the effects of climate variability and other drivers of change on vegetation and crop suitability.

Precise outdoor localization with a GPS–INS integration system

SUMMARYThis paper proposes a precise outdoor localization algorithm with the integration of Global Positioning System (GPS) and Inertial Navigation System (INS). To achieve precise outdoor localization, two schemes are recently proposed, which consist of de-noising the INS signals and fusing the GPS and INS data. To reduce the noise from the internal INS sensors, the discrete wavelet transform and variable threshold method are utilized, and to fuse the GPS and INS data while filtering out the noise caused by the acceleration, deceleration, and unexpected slips, the Unscented Particle Filter (UPF) is adopted. Conventional de-noising methods mainly employ a combination of low-pass and high-pass filters, which results in signal distortion. This newly proposed system also utilizes the vibration information of the actuator according to the fluctuations of the velocity to minimize the signal distortion. The UPF resolves the nonlinearities of the actuator and non-normal distributions of the noise more effectively than the conventional particle filter (PF) or Extended Kalman Filter–PF. The superiority of the proposed algorithm was verified through experiments, and the results are reported.

DTW 최소누적거리를 이용한 심전도 이상 검출 알고리즘 구현 및 평가

Abstract Recently the convergence of healthcare technology is used for daily life healthcare monitoring. Cardiac arrhythmia is presented by thestate of the heart irregularity. Abnormal heart's electrical signal pathway or heart's tissue disorder could be the cause of cardiacarrhythmia. Fatal arrhythmia could put patient's life at risk. Therefore arrhythmia detection is very important. Previous studies on thedetection of arrhythmia in various ECG analysis and classification methods had been carried out. In this paper, an ECG signal processingtechniques to detect abnormal ECG based on DTW minimum accumulation distance through the template matching for normalized dataand variable threshold method for ECG R-peak detection. Signal processing techniques able to determine the occurrence of normal ECGand abnormal ECG. Abnormal ECG detection algorithm using DTW minimum accumulation distance method is performed using MIT-BIH database for performance evaluation. Experiment result shows the average percentage accuracy of using the propose method for R-peak detection is 99.63 % and abnormal detection is 99.60 %.

Two-dimensional extension of variance-based thresholding for image segmentation

Variance-based thresholding method is a very effective technology for image segmentation. However, its performance is limited in traditional one-dimensional and two-dimensional scheme. In this paper, a novel two-dimensional variance thresholding scheme to improve image segmentation performance is proposed. The two-dimensional histogram of the original and local average image is projected to one-dimensional space in the proposed scheme firstly, and then the variance-based criterion is constructed for threshold selection. The experimental results on bi-level and multilevel thresholding for synthetic and real-world images demonstrate the success of the proposed image thresholding scheme, as compared with the Otsu method, the two-dimensional Otsu method and the minimum class variance thresholding method.

Evaluation of association tests for rare variants using simulated data sets in the Genetic Analysis Workshop 17 data

We evaluate four association tests for rare variants—the combined multivariate and collapsing (CMC) method, two weighted-sum methods, and a variable threshold method—by applying them to the simulated data sets of unrelated individuals in the Genetic Analysis Workshop 17 (GAW17) data. The family-wise error rate (FWER) and average power are used as criteria for evaluation. Our results show that when all nonsynonymous SNPs (rare variants and common variants) in a gene are jointly analyzed, the CMC method fails to control the FWER; when only rare variants (single-nucleotide polymorphisms with minor allele frequency less than 0.05) are analyzed, all four methods can control FWER well. All four methods have comparable power, which is low for the analysis of the GAW17 data sets. Three of the methods (not including the CMC method) involve estimation of p-values using permutation procedures that either can be computationally intensive or generate inflated FWERs. We adapt a fast permutation procedure into these three methods. The results show that using the fast permutation procedure can produce FWERs and average powers close to the values obtained from the standard permutation procedure on the GAW17 data sets. The standard permutation procedure is computationally intensive.

Variable Threshold Method for ECG R-peak Detection

In this paper, a wearable belt-type ECG electrode worn around the chest by measuring the real-time ECG is produced in order to minimize the inconvenient in wearing. ECG signal is detected using a potential instrument system. The measured ECG signal is transmits via an ultra low power consumption wireless data communications unit to personal computer using Zigbee-compatible wireless sensor node. ECG signals carry a lot of clinical information for a cardiologist especially the R-peak detection in ECG. R-peak detection generally uses the threshold value which is fixed. There will be errors in peak detection when the baseline changes due to motion artifacts and signal size changes. Preprocessing process which includes differentiation process and Hilbert transform is used as signal preprocessing algorithm. Thereafter, variable threshold method is used to detect the R-peak which is more accurate and efficient than fixed threshold value method. R-peak detection using MIT-BIH databases and Long Term Real-Time ECG is performed in this research in order to evaluate the performance analysis.

Quantification of Urinary Stone Volume: Attenuation Threshold–based CT Method—A Technical Note

To compare two threshold-based computed tomographic (CT) methods for the quantification of urinary stone volume; to assess their accuracy and precision at varying tube voltages, tube currents, and section thicknesses in a phantom; and to determine interobserver agreement with each of these methods in a pilot clinical study. After institutional review board approval, written informed consent was waived. The study was HIPAA compliant. Thirty-six calcium oxalate stones were scanned in an anthropomorphic phantom. For the fixed threshold method, stones were segmented with 0.6-mm-thick sections by using attenuation thresholds of 130 and 575 HU (equal to half of mean attenuation of all stones). For the variable threshold method, stones were segmented at an attenuation threshold equal to half of the attenuation of each stone and at variable section thicknesses (0.6, 1, and 3 mm), tube currents (150, 100, and 50 mAs [reference]), and tube voltages (100 and 80 kVp). Normalized Bland-Altman analysis was used to assess the bias and precision of the two CT methods compared with that of the fluid displacement method (reference standard). Two independent readers retrospectively measured stone volumes in 17 patients (male-to-female ratio, 1.4; mean age, 55 years), and interobserver agreement was assessed by using Bland-Altman limits of agreement. The variable threshold method was more accurate and precise than the fixed threshold method with an attenuation threshold of 130 HU (P < .0001). Thinner sections (0.6 and 1 mm) resulted in more accurate (P < .05) and precise (P < .0001) stone volume measurements than 3-mm-thick sections. With the variable threshold method, no significant difference was seen in the accuracy and precision of stone volume measurements at various tube currents and tube potentials. Interobserver agreement was high with the fixed and variable threshold methods (r > 0.97). An attenuation threshold-based CT method can be used to quantify urinary stone volume even at low radiation doses. The most accurate and precise method utilizes variable attenuation derived from the attenuation of each stone and thin sections.