Adaptive Threshold Algorithm Research Articles

Automatic liver parenchyma segmentation system from abdominal CT scans using hybrid techniques

In this paper, a multi–layer heuristic approach is introduced to segment liver region from other tissues in multi–slice CT images. Image noise is a principal factor which hampers the visual quality of medical images and can therefore lead to misdiagnosis. To address this issue, we first utilise an algorithm based on median filter to remove noise and enhance the contrast of the CT image. This is followed by performing an adaptive threshold algorithm and morphological operators to preserve the liver structure and remove the fragments of other organs. Then, connected component labelling algorithm was applied to remove false positive regions and focused on liver region. To evaluate the performance of the proposed system, we present tests on different liver CT scans images. The experimental results show that the overall accuracy offered by the employed system is high compared with other related works as well as very fast which segment liver from abdominal CT in less than 0.6 s/slice.

Efficient adaptive thresholding algorithm for in-homogeneous document background removal

Image binarization refers to convert gray-level images into binary ones, and many binarization algorithms have been developed. The related algorithms can be classified as either high quality computation or high speed performance. This paper presents an algorithm that ensures both benefits at the same time. The proposed algorithm intelligently segments input images into several different sized sub-images by using a Sobel like matrix. After which each sub-image will be classified into background set or foreground set according to it's feature. Finally the foreground set sub-images will be binarized by Otsu's method independently. Experimental results reveal that our algorithm provides the appropriate quality with the medium speed.

Characterizing changes in cropping patterns using sequential Landsat imagery: an adaptive threshold approach and application to Phoenix, Arizona

Since the 1970s, the Phoenix Active Management Area has experienced rapid urbanization, mostly through land conversions from agricultural lands to urban land use. Rapid urban expansion and population growth have placed unprecedented pressure on agricultural production in this region. Agricultural intensification, in particular double cropping, has been observed globally as an important response to the growing pressure on land. However, the intensification has a number of negative impacts on water quality, biodiversity, and biogeochemical cycles. Thus, quantifying the spatial pattern of cropping intensity is important for natural resource management. In this study, we developed an adaptive threshold approach to map cropping intensity using time series Landsat data and examined the spatiotemporal patterns of cropping intensity in the Phoenix Active Management Area from 1995 to 2010 at 5-year intervals. To map cropping intensity accurately, the adaptive threshold algorithm was designed specifically to address several issues caused by the complex cropping patterns in the study area. The adaptive threshold method has abilities to (1) distinguish true crop cycles from multiple false phenological peaks, (2) minimize errors caused by data noise and missing data, (3) identify alfalfa and interyear crops and to distinguish alfalfa from double crops, and (4) adapt to temporal profiles with different numbers of observations. The adaptive threshold algorithm is effective in characterizing cropping intensity with overall accuracies exceeding 97%. Results show that there is a dramatic decline in the area of total croplands (46.1%), single crops (46.3%), and double crops (43.4%) during the study period. There was a small conversion (1.9%) from single to double crop from 1995 to 2000, whereas a reverse conversion (1.3%) was observed from 2005 to 2010. Updated and accurate information on the spatial distribution of cropping intensity provide important implications on effective and sustainable cropping practices. In addition, joint investigation on cropping patterns and irrigation water use can shed light on future agricultural water demand, which is of paramount importance in this rapidly expanding arid region.

Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm

A segmentation algorithm based on the random walk (RW) method, called 3D-LARW, has been developed to delineate small tumors or tumors with a heterogeneous distribution of FDG on PET images. Based on the original algorithm of RW [1], we propose an improved approach using new parameters depending on the Euclidean distance between two adjacent voxels instead of a fixed one and integrating probability densities of labels into the system of linear equations used in the RW. These improvements were evaluated and compared with the original RW method, a thresholding with a fixed value (40% of the maximum in the lesion), an adaptive thresholding algorithm on uniform spheres filled with FDG and FLAB method, on simulated heterogeneous spheres and on clinical data (14 patients). On these three different data, 3D-LARW has shown better segmentation results than the original RW algorithm and the three other methods. As expected, these improvements are more pronounced for the segmentation of small or tumors having heterogeneous FDG uptake.

Adaptive thresholding with inverted triangular area for real-time detection of the heart rate from photoplethysmogram traces on a smartphone.

Photoplethysmogram (PPG) signals acquired by smartphone cameras are weaker than those acquired by dedicated pulse oximeters. Furthermore, the signals have lower sampling rates, have notches in the waveform and are more severely affected by baseline drift, leading to specific morphological characteristics. This paper introduces a new feature, the inverted triangular area, to address these specific characteristics. The new feature enables real-time adaptive waveform detection using an algorithm of linear time complexity. It can also recognize notches in the waveform and it is inherently robust to baseline drift. An implementation of the algorithm on Android is available for free download. We collected data from 24 volunteers and compared our algorithm in peak detection with two competing algorithms designed for PPG signals, Incremental-Merge Segmentation (IMS) and Adaptive Thresholding (ADT). A sensitivity of 98.0% and a positive predictive value of 98.8% were obtained, which were 7.7% higher than the IMS algorithm in sensitivity, and 8.3% higher than the ADT algorithm in positive predictive value. The experimental results confirmed the applicability of the proposed method.

Application of partial volume effect correction and 4D PET in the quantification of FDG avid lung lesions.

The aim of this study is to assess a software-based method with semiautomated correction for partial volume effect (PVE) to quantify the metabolic activity of pulmonary malignancies in patients who underwent non-gated and respiratory-gated 2-deoxy-2-[(18)F]fluoro-D-glucose (FDG)-positron emission tomography (PET)/x-ray computed tomography(CT). The study included 106 lesions of 55 lung cancer patients who underwent respiratory-gated FDG-PET/CT for radiation therapy treatment planning. Volumetric PET/CT parameters were determined by using 4D PET/CT and non-gated PET/CT images. We used a semiautomated program employing an adaptive contrast-oriented thresholding algorithm for lesion delineation as well as a lesion-based partial volume effect correction algorithm. We compared respiratory-gated parameters with non-gated parameters by using pairwise comparison and interclass correlation coefficient assessment. In a multivariable regression analysis, we also examined factors, which can affect quantification accuracy, including the size of lesion and the location of tumor. This study showed that quantification of volumetric parameters of 4D PET/CT images using an adaptive contrast-oriented thresholding algorithm and 3D lesion-based partial volume correction is feasible. We observed slight increase in FDG uptake by using PET/CT volumetric parameters in comparison of highest respiratory-gated values with non-gated values. After correction for partial volume effect, the mean standardized uptake value (SUVmean) and total lesion glycolysis (TLG) increased substantially (p value <0.001). However, we did not observe a clinically significant difference between partial volume corrected parameters of respiratory-gated and non-gated PET/CT scans. Regression analysis showed that tumor volume was the main predictor of quantification inaccuracy caused by partial volume effect. Based on this study, assessment of volumetric PET/CT parameters and partial volume effect correction for accurate quantification of lung malignant lesions by using respiratory non-gated PET images are feasible and it is comparable to gated measurements. Partial volume correction increased both the respiratory-gated and non-gated values significantly and appears to be the dominant source of quantification error of lung lesions.

Lossless Compression of Hyperspectral Images Using Three-Stage Prediction with Adaptive Search Threshold

We propose a new lossless compression algorithm for hyper spectral images based on the third-order interband predictor and the backward pixel search scheme (IP3-BPS). Specifically, we propose an adaptive search threshold algorithm and a bi-directional pixel search scheme. The resulting algorithm takes the bi-directional pixel search and the backward pixel search with adaptive search threshold as the last two predictors; its performance evaluation on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) 1997 images shows that it outperforms the original IP3-BPS algorithm, at lower computational complexity level.

Image Denoising with Contour-Based Directionlet Transform

A novel mutiscale and directionally adaptive image transform called contour based directionlet tansform is presented. Directionlet transform (DT) has shown its charming performance in image processing, but it has scrambled frequencies. Laplacian Pyramid is employed here to separate the low frequencies before applying DT for avoiding the drawback. And an adaptive threshold algorithm is proposed for denoising. Numerical experiments are performed to assess the applicability of the proposed method. The obtained results show that the proposed scheme outperforms Wavelet and Directionlet transforms in terms of numerical and perceptual quality.

A generic classification-based method for segmentation of nuclei in 3D images of early embryos

BackgroundStudying how individual cells spatially and temporally organize within the embryo is a fundamental issue in modern developmental biology to better understand the first stages of embryogenesis. In order to perform high-throughput analyses in three-dimensional microscopic images, it is essential to be able to automatically segment, classify and track cell nuclei. Many 3D/4D segmentation and tracking algorithms have been reported in the literature. Most of them are specific to particular models or acquisition systems and often require the fine tuning of parameters.ResultsWe present a new automatic algorithm to segment and simultaneously classify cell nuclei in 3D/4D images. Segmentation relies on training samples that are interactively provided by the user and on an iterative thresholding process. This algorithm can correctly segment nuclei even when they are touching, and remains effective under temporal and spatial intensity variations. The segmentation is coupled to a classification of nuclei according to cell cycle phases, allowing biologists to quantify the effect of genetic perturbations and drug treatments. Robust 3D geometrical shape descriptors are used as training features for classification. Segmentation and classification results of three complete datasets are presented. In our working dataset of the Caenorhabditis elegans embryo, only 21 nuclei out of 3,585 were not detected, the overall F-score for segmentation reached 0.99, and more than 95% of the nuclei were classified in the correct cell cycle phase. No merging of nuclei was found.ConclusionWe developed a novel generic algorithm for segmentation and classification in 3D images. The method, referred to as Adaptive Generic Iterative Thresholding Algorithm (AGITA), is freely available as an ImageJ plug-in.

Adaptive Thresholding Algorithm: Efficient Computation Technique Based on 2-D Intelligent Block Detection for Degraded Images

Adaptive Thresholding Algorithm: Efficient Computation Technique Based on 2-D Intelligent Block Detection for Degraded Images

Automated image analysis of lung branching morphogenesis from microscopic images of fetal rat explants.

Background. Regulating mechanisms of branching morphogenesis of fetal lung rat explants have been an essential tool for molecular research. This work presents a new methodology to accurately quantify the epithelial, outer contour, and peripheral airway buds of lung explants during cellular development from microscopic images. Methods. The outer contour was defined using an adaptive and multiscale threshold algorithm whose level was automatically calculated based on an entropy maximization criterion. The inner lung epithelium was defined by a clustering procedure that groups small image regions according to the minimum description length principle and local statistical properties. Finally, the number of peripheral buds was counted as the skeleton branched ends from a skeletonized image of the lung inner epithelia. Results. The time for lung branching morphometric analysis was reduced in 98% in contrast to the manual method. Best results were obtained in the first two days of cellular development, with lesser standard deviations. Nonsignificant differences were found between the automatic and manual results in all culture days. Conclusions. The proposed method introduces a series of advantages related to its intuitive use and accuracy, making the technique suitable to images with different lighting characteristics and allowing a reliable comparison between different researchers.

A New Method Based on Radio Frequency Spectrum for Background Noise Curve Extraction

The background noise is one of the key factors that interferes the radio monitoring. How to efficiently extract original signals from noises is important for the signal-to-noise separation algorithm. Recently, several algorithms have been proposed to extract signal on certain conditions, which also have their limitations. This paper first reviews and discusses the existing signal-to-noise separation algorithm. Then, we propose a piecewise adaptive threshold algorithm based on the principle of smoothing filter. The algorithm can be adjusted adaptively with the change of the external electromagnetic environment, and can be applied in a wide monitoring frequency bands.

FDG PET/CT in Crohn’s disease: correlation of quantitative FDG PET/CT parameters with clinical and endoscopic surrogate markers of disease activity

The aim of this study was to determine the feasibility and potential clinical utility of assessment of Crohn's disease (CD) activity by (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT employing a new quantitative approach. A total of 22 subjects (mean age 37) with CD who had undergone FDG PET/CT followed by ileocolonoscopy within 1 week were included in this analysis. The CD endoscopy index of severity (CDEIS) for various bowel segments was calculated. The CD activity index (CDAI) was evaluated, and fecal calprotectin was measured. On PET, regions with increased FDG uptake in large bowel were segmented with an adaptive contrast-oriented thresholding algorithm, and metabolically active volume (MAV), uncorrected mean standardized uptake value (SUV(mean)), partial volume-corrected SUV(mean) (PVC-SUV(mean)), SUV(max), uncorrected total lesion glycolysis (TLG = MAV × SUV(mean)), and PVC total lesion glycolysis (PVC-TLG = MAV × PVC-SUV(mean)) were measured. Global CD activity score (GCDAS) was calculated as the sum of PVC-TLG over all clinically significant FDG-avid regions in each subject. Correlations between regional PET quantification measures (SUVs, TLGs) and CDEIS were calculated. Correlations between the global PET quantification measure (GCDAS, global SUVs) with CDAI, fecal calprotectin, CDEIS, and CRP level were also calculated. SUV(max), PVC-SUV(mean), and PVC-TLG significantly correlated with segment CDEIS subscores (r = 0.50, r = 0.69, and r = 0.31, respectively; p < 0.05). GCDAS significantly correlated with CDAI and fecal calprotectin (r = 0.64 and r = 0.51, respectively; p < 0.05). By employing this new quantitative approach, we were able to calculate indices of regional and global CD activity, which correlated well with both clinical and pathological disease activity surrogate markers. This approach may be of clinical importance in measuring both global disease activity and treatment response in patients with CD.

소형 도마뱀 운동 분석을 위한 마커리스 모션 캡쳐 알고리즘

In this paper, a algorithm to find joints of a small animal like a lizard from the multiple-view silhouette images is presented. The proposed algorithm is able to calculate the 3D coordinates so that the locomotion of the lizard is markerlessly reconstructed. The silhouette images of the lizard was obtained by a adaptive threshold algorithm. The skeleton image of the silhouette image was obtained by Zhang-Suen method. The back-bone line, head and tail point were detected with the A* search algorithm and the elimination of the ortho-diagonal connection algorithm. Shoulder joints and hip joints of a lizard were found by masking of the thicked back-bone line. Foot points were obtained by morphology calculation. Finally elbow and knee joint were calculated by the ortho distance from the lines of foot points and shoulder/hip joint. The performance of the suggested algorithm was evaluated through the experiment of detecting joints of a small lizard.

Adaptive Spectrum Sensing Algorithm Under Different Primary User Utilizations

Spectrum sensing is one of the key technologies to realize dynamic spectrum access in cognitive radio (CR) systems. In this letter, a novel adaptive threshold spectrum sensing algorithm is proposed to achieve an efficient trade-off between the detection and false alarm probability. The proposed adaptive threshold algorithm demonstrates a better spectrum efficiency for both primary users (PUs) and secondary users (SUs) in comparison with the conventional fixed one. A closed-from expression between PUs' spectrum utilization ratio and the proposed adaptive threshold is derived and simplified.

A Video Image Segmentation Technology Based on Adaptive Thresholding Algorithm

Image segmentation is a key step in image processing and image analysis and occupies an important position in image engineering.In this paper, basing on maximum variance between-class, an adaptive and multi-objective image segmentation method is proposed. The concrete implement is to determine adaptively the optimum number of threshold of image using the idea of variance decomposition,while calculating the weighted ratio of within class difference and class difference existing in each classification image. By comparing the ratio, the optimum number of target for image can be get. The experimental results show that the sub-images after segmentation are relatively clear and the differences between classes are obvious.

Robust pattern recognition for measurement of three dimensional weld pool surface in GTAW

Ability to observe and measure weld pool surfaces in real-time is the foundation for next generation intelligent welding that partially mimics welders' sensory capability, i.e., acquiring weld status information from the observation of weld pool. To image and measure the mirror-like weld pool surface under the strong arc radiation, a structured light laser pattern, dot matrix, has been projected onto the weld pool surface and its specular reflection is intercepted and imaged by an imaging plane placed with a distance from the arc. The reflection pattern, deformed by the specular liquid weld pool, contains three-dimensional (3D) geometry information of the weld pool. In this paper, a robust recognition procedure has been proposed to identify the reflection pattern in real-time. In particular, an adaptive thresholding algorithm is proposed to distinguish the laser dots in the reflection pattern. Then a reflection pattern recognition algorithm is proposed to determine the row and column number for each reflected laser dot such that reflection pattern can be matched with the projection pattern. The identified reflection pattern can be used to reconstruct the 3D weld pool surface. Experiments with different welding conditions have been conducted to verify the real-time performance of the proposed procedure, including the effectiveness, robustness and time complexity. It has been found that the procedure is capable of identifying the reflection pattern from a captured image in less than 19 ms. The real-time performance meets the most widely used arc welding process for precise welding-gas tungsten arc welding (GTAW) that typically requires to be controlled a few times per second.

Image Denoising in Shearlet Domain by Adaptive Thresholding

Shearlet transform contains some useful and many good properties in signal analysis, such as sparsity, multi-scale and multi-direction. Compared to the existing multi-scale geometry analysis, for instance, the Curvelet and Contourlet, Shearlets are theoretically optimal in representing images and particularly have the ability to fully capture directional and geometrical features. Furthermore, it can be easily implemented. Therefore, it is very suitable for the image denoising. A new adaptive thresholding algorithm in Shearlet domain which based on SURE-LET is proposed in this paper. Noisy image is firstly transformed into Shearlet domain; and then a pointwise thresholding is performed on the Shearlet coefficients based on SURE-LET in order to remove additive white Gaussian noise; at last, the image is reconstructed by the inverse Shearlet transform. Experimental results show that the performance of the proposed method is comparable to the state-of-the-art denoising algorithms, such as NLM and BM3D, but with the advantage of its low complexity and better computational efficiency.

An Adaptive Threshold Algorithm Based on Satisfactory Filtering Principle

In this paper, the problem of designing a adaptive threshold for Fault diagnostic in Full Authority Digital Engine Control system that are subject to environmental disturbances and various input is investigated. According to aero-engine small deviation linearization model, state matrix perturbation model with "soft boundary" pseudo normal distribution is built. The proposed algorithm bases on satisfaction filtering principle. Linear matrix inequality (LMI) method is applied to get upper bound of residual. With the residual distribution probability model and the target false alarm rate, the optimal threshold is determined. The proposed algorithm is then applied to sensor fault detection on a turbofan aero-engine. Simulation results demonstrate and validate the performance and capability of the adaptive threshold algorithm.

Аналитическая оценка рабочих характеристик адаптивного обнаружения флуктуирующих целей радара

A radar target whose return varies up and down in amplitude as a function of time represents the basis of a large number of real targets. This paper is intended to provide a complete analysis of CFAR detection of fluctuating targets when the radar receiver post-detection integrates M returned pulses from χ2 fluctuating targets with two and four degrees of freedom and operates in a non-ideal environment. Owing to the importance of Swerling models in representing a large number of such type of radar targets, we are interested here in adaptive detection of this class of fluctuation models. Swerling cases I and III represent scan-to-scan fluctuating targets, while cases II and IV represent fast pulse-to-pulse fluctuation. Exact expressions of detection probability are derived for all of these models. A simple and an effective procedure for calculating the detection performance of both fixed-threshold and adaptive-threshold algorithms is obtained. In the CFAR case, the estimation of the noise power levels from the leading and the trailing reference windows is based on the CA technique. The performance of this detector is analyzed in the cases when the operating environment is ideal and when it includes some of spurious targets along with the target of interest. The primary and the secondary interfering targets are assumed to be fluctuating in accordance with the four Swerling’s models cited above. The numerical results show that for strength target return the processor detection performance is highest in the case of SWIV model while it attains its minimum level of detection in the case of SWI model. Moreover, SWII model has higher performance than the SWIII representation of fluctuating targets. For weak target return, on the other hand, the reverse of this behavior is occurred. This observation is common for both fixed-threshold or for adaptive-threshold algorithms.