Adaptive Threshold Algorithm Research Articles

A Hybrid Segmentation Framework for Computer-Assisted Dental Procedures

SUMMARY Teeth segmentation in computed tomography (CT) images is a major and challenging task for various computer assisted procedures. In this paper, we introduced a hybrid method for quantification of teeth in CT volumetric dataset inspired by our previous experiences and anatomical knowledge of teeth and jaws. In this regard, we propose a novel segmentation technique using an adaptive thresholding, morphological operations, panoramic re-sampling and variational level set algorithm. The proposed method consists of several steps as follows: first, we determine the operation region in CT slices. Second, the bony tissues are separated from other tissues by utilizing an adaptive thresholding technique based on the 3D pulses coupled neural networks (PCNN). Third, teeth tissue is classified from other bony tissues by employing panorex lines and anatomical knowledge of teeth in the jaws. In this case, the panorex lines are estimated using Otsu thresholding and mathematical morphology operators. Then, the proposed method is followed by calculating the orthogonal lines corresponding to panorex lines and panoramic re-sampling of the dataset. Separation of upper and lower jaws and initial segmentation of teeth are performed by employing the integral projections of the panoramic dataset. Based the above mentioned procedures an initial mask for each tooth is obtained. Finally, we utilize the initial mask of teeth and apply a variational level set to refine initial teeth boundaries to final contour. In the last step a surface rendering algorithm known as marching cubes (MC) is applied to volumetric visualization. The proposed algorithm was evaluated in the presence of 30 cases. Segmented images were compared with manually outlined contours. We compared the performance of segmentation method using ROC analysis of the thresholding, watershed and our previous works. The proposed method performed best. Also, our algorithm has the advan

The Modeling and the Sensor Fault Diagnosis of a Continuous Stirred Tank Reactor with a Takagi-Sugeno Recurrent Fuzzy Neural Network

In this paper, a novel Takagi-Sugeno recurrent fuzzy neural network (TSRFNN) is constructed for modeling and sensor fault diagnosis of a Continuous Stirred Tank Reactor (CSTR), a nonlinear dynamic system. The TSRFNN is composed of 9 layers, including premise network and consequence network. The temporal information is embedded in the TSRFNN by adding the feedback connections between the output layer and the input layer of the fuzzy neural network (FNN). It is assumed that the inputs are Gaussian membership functions; the product operation is utilized for the premise and implication, and the weighted center-average method is adopted for defuzzification. The network is a Fuzzy Basis Function(FBF). The general approximation characteristic of the network was proven by the theory reasoning. The identification of the TSRFNN consists of two steps: structure identification and parameter identification. Unsupervised clustering is used to determine the structure of the fuzzy system, the number of fuzzy rules, and the membership functions of the premise using the input-output data of a system. Then in the parameter identification, the Dynamic Backpropagation (DBP) is adopted to determine the membership functions of the conclusion of the fuzzy system. Then the network is applied to the modeling and diagnosis of a CSTR system. The network is applied to set up the sensors models of a CSTR system, including the models of the temperature sensor faults and the models of concentration sensor faults. To set up a high performance diagnosis model, a persistent signal was chosen to sufficiently activate the plant. Finally an Adaptive Threshold Algorithm based on statistics is used to fault diagnose. If there are sensor faults in the CSTR, the bias between the actual output of the plant and the output of the network surpasses the threshold and the faults are detected on-line according to it. The effectiveness of the modeling and diagnosis approach proposed was verified by the simulation results with Matlab. Therefore, the proposed network and the fault diagnosis approach can be employed to a CSTR system successfully and be extended to the modeling and fault detection of other nonlinear systems.

Image thresholding by variational minimax optimization

In this paper we introduce an adaptive image thresholding technique via minimax optimization of a novel energy functional that consists of a non-linear convex combination of an edge sensitive data fidelity term and a regularization term. While the proposed data fidelity term requires the threshold surface to intersect the image surface only at places with large image gradient magnitude, the regularization term enforces smoothness in the threshold surface. To the best of our knowledge, all the previously proposed energy functional-based adaptive image thresholding algorithms rely on manually set weighting parameters to achieve a balance between the data fidelity and the regularization terms. In contrast, we use minimax principle to automatically find this weighting parameter value, as well as the threshold surface. Our conscious choice of the energy functional permits a variational formulation within the minimax principle leading to a globally optimum solution. The proposed variational minimax optimization is carried out by an iterative gradient descent with exact line search technique that we experimentally demonstrate to be computationally far more attractive than the Fibonacci search applied to find the minimax solution. Our method shows promising results to preserve edge/texture structures in different benchmark images over other competing methods. We also demonstrate the efficacy of the proposed method for delineating lung boundaries from magnetic resonance imagery (MRI).

Reiterative Robust Adaptive Thresholding for Nonhomogeneity Detection in Non-Gaussian Noise

A robust and data-dependent adaptive thresholding algorithm for nonhomogeneity detection in non-Gaussian interference is addressed. The algorithm is to be used as a preprocessing technique to select a set of homogeneous data from a bulk of nonhomogeneous compound-Gaussian secondary data employed for adaptive radar. An iterative version of the algorithm is also suggested in situations of multiple outliers in the secondary data. Performance analysis is conducted with simulated data as well as with real sea clutter data.

Detection of Retinal Blood Vessels Based on Nonlinear Projections

An automated method for blood vessel segmentation is presented in this paper. The approach uses the nonlinear orthogonal projection to capture the features of vessel networks, and derives a novel local adaptive thresholding algorithm for vessel detection. By embedding in a kind of image decomposition model, the selection of system parameter which reflects the size of concerned convex set is examined. This approach differs from previously known methods in that it uses matched filtering, vessel tracking or supervised methods. The algorithm was tested on two publicly available databases: the DRIVE and the STARE. By comparison with hand-labeled ground truth, an average accuracy of 96.1% is achieved on the former database, and an average accuracy of 90.8% is achieved on the later database.

ANALYSIS OF CFAR DETECTION OF FLUCTUATING TARGETS

Our scope in this paper is to provide a complete analysis of CFARdetection of fluctuating targets when the radar receiver incoherently integrates M returned pulses from a chi-squared fluctuating targets with two and four degrees of freedom and operates in a multitarget environment. Since the Swerling models of fluctuating targets represent a large number of such type of radar targets, we restrict our attention here to this interesting class of fluctuation models. There are four categories of such representation; namely SWI, SWII, SWIII, and SWIV. SWI and SWIII represent scan- to-scan fluctuating targets, while SWII and SWIV represent fast pulse-to-pulse fluctuation. Exact expressions are derived for the probability of detection of 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. The backbone of this procedure is the ω-domain representation of the cumulative distribution function of the test statistic of the processor under consideration. In the CFARcase, the estimation of the noise power levels from the leading and the trailing reference windows is based on the OS technique. The performance of this detector is analyzed in the case where the operating environment is ideal and where it includes some of extraneous targets along with the target under test. The primary and the secondary outlying targets are assumed to be fluctuating in accordance with the four Swerling's models cited above. The numerical results show that, for large SNR, 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 low SNR, on the other hand, the reverse of this behavior is occurred. This observation is common either for fixed-threshold or for adaptive-threshold algorithm.

Application of anomaly detection algorithms for detecting SYN flooding attacks

We investigate statistical anomaly detection algorithms for detecting SYN flooding, which is the most common type of Denial of Service (DoS) attack. The two algorithms considered are an adaptive threshold algorithm and a particular application of the cumulative sum (CUSUM) algorithm for change point detection. The performance is investigated in terms of the detection probability, the false alarm ratio, and the detection delay, using workloads of real traffic traces. Particular emphasis is on investigating the tradeoffs among these metrics and how they are affected by the parameters of the algorithm and the characteristics of the attacks. Such an investigation can provide guidelines to effectively tune the parameters of the detection algorithm to achieve specific performance requirements in terms of the above metrics.

Connectivity-based local adaptive thresholding for carotid artery segmentation using MRA images

The computer algorithms for the delineation of anatomical structures and other regions of interest on the medical imagery are important component in assisting and automating specific radiological tasks. In addition, the segmentation of region is an important first step for variety image related application and visualization tasks. In this paper, we propose a fast and automated connectivity-based local adaptive thresholding (CLAT) algorithm to segment the carotid artery in sequence medical imagery. This algorithm provides the new feature that is the circumscribed quadrangle on the segmented carotid artery for region-of-interest (ROI) determination. By using the preserved connectivity between consecutive slice images, the size of the ROI is adjusted like a moving window according to the segmentation result of previous slice image. The histogram is prepared for each ROI and then smoothed by local averaging for the threshold selection. The threshold value for carotid artery segmentation is locally selected on each slice image and is adaptively determined through the sequence image. In terms of automated features and computing time, this algorithm is more effective than region growing and deformable model approaches. This algorithm is also applicable to segment the cylinder shape structures and tree-like blood vessels such as renal artery and coronary artery in the medical imagery. Experiments have been conducted on synthesized images, phantom and clinical data sets with various Gaussian noise.

DCT-based adaptive thresholding algorithm for binary motion estimation

This paper presents a discrete-cosine transform (DCT)-based adaptive thresholding algorithm to achieve an effective binarization of video image for binary motion estimation (BME). The BME through the full search mechanism requires extremely low hardware complexity. Experimental results show that the proposed adaptive BME attains performance close to the 8-bit resolution ME for low motion sequences with full search strategy. Compared to the well-known single-layer BME, the proposed adaptive algorithm shows better peak signal-to-noise ratio performance and more robustness to complex images. Moreover, the proposed algorithm has a lower computational complexity that is suitable for VLSI implementation with a smaller hardware overhead.

The Influence of Ink and Paper on Bar Code Print Quality and Readability with OCR Scanning Systems

Postal operators worldwide use two-dimensional Data Matrix bar codes for machine-readable postage payment evidencing. Dynamic interaction of ink jet inks with uncontrolled media like envelope papers generates a variety of print quality defects. Adaptive thresholding algorithms optimized for OCR amplify the impact of these defects on the readability of two-dimensional bar codes. Print quality parameters are determined according to international standards for several combinations of monochrome franking ink jet inks and plain paper envelopes. The ink and paper characteristics relevant in generating the defects are measured and interpreted in terms of wetting and capillarity. We investigate, through computer simulation, the impact of the interaction of print quality defects and OCR adaptive thresholding algorithms on 2D bar code readability.

Video shot characterization

This paper presents a practical approach to detecting shot cuts and extracting keyframes from video sequences. Shot cut detection has two stages - global motion compensation, followed by an adaptive thresholding algorithm. The motion information is further utilized to extract representative keyframes. Special consideration has been given to achieving real-time performance on a regular PC, which led to a motion estimation algorithm of linear complexity.

Frequency domain adaptive filter for gravitational burst analysis

The noise of gravitational wave detectors is Gaussian but non-stationary so any good filtering technique should be adaptive. We propose a two-step adaptive procedure to identify signal and/or noise bursts, consisting of an adaptive zero phase Wiener filter in the frequency domain followed by a series of band-pass filters and an adaptive threshold algorithm to identify the events and their characteristics. This approach has been applied to simulated Virgo-like data.

Motion estimation using a frame-based adaptive thresholding approach

This paper proposes two frame-based adaptive thresholding algorithms for reducing the amount of computation involved in block-based motion estimation in the real-time and low-rate video coders. The proposed algorithms use an adaptive threshold for each of the frames to prejudge if each of the block matching computation is worthwhile. Based on the difference between the current and reference frames, the first algorithm determines a threshold in terms of the optimal trade-off between run-time and distortion, and the second algorithm determines a threshold according to a user specified percentage of total number of blocks. The experimental results demonstrate that the proposed algorithms can significantly reduce the amount of computation compared to the previous algorithms, while almost fully maintaining the quality of the reconstructed image.

Fast adaptive PNN-based thresholding algorithms

Thresholding is a fundamental operation in image processing. Based on the pairwise nearest neighbor technique and the variance criterion, this theme presents two fast adaptive thresholding algorithms. The proposed first algorithm takes O(( m− k) mτ) time where k denotes the number of thresholds specified by the user; m denotes the size of the compact image histogram, and the parameter τ has the constraint 1⩽ τ⩽ m. On a set of different real images, experimental results reveal that the proposed first algorithm is faster than the previous three algorithms considerably while having a good feature-preserving capability. The previous three mentioned algorithms need O( m k ) time. Given a specific peak-signal-to-noise ratio (PSNR), we further present the second thresholding algorithm to determine the number of thresholds as few as possible in order to obtain a thresholded image satisfying the given PSNR. The proposed second algorithm takes O(( m− k) mτ+ γN) time where N and γ denote the image size and the fewest number of thresholds required, respectively. Some experiments are carried out to demonstrate the thresholded images that are encouraging. Since the time complexities required in our proposed two thresholding algorithms are polynomial, they could meet the real-time demand in image preprocessing.

Active surface model-based adaptive thresholding algorithm by repulsive external force

Inspired by the idea that threshold surface always inter- sects the image surface at high gradient points, an active surface- based adaptive thresholding algorithm is proposed to get the bina- rized result. In this model, the external force is designed to be repulsive from the image surface, thus at the equilibrium state the active surface tends to cover the supporting points of high gradient with smooth property, as well as be away from the image surface locally, which makes the obtained threshold surface properly sepa- rate the foreground and background. The description of the algo- rithm is in a simple and reasonable energy functional form, and only two parameters need to be tuned, which gives more convenience to the operation. Analysis and comparison for the experimental results reveal that it cannot only give the proper thresholding result but also restrain the occurrence of the ghost phenomenon. © 2003 SPIE and IS&T. (DOI: 10.1117/1.1556313)

Bone MRI segmentation assessment based on synchrotron radiation computed microtomography

In this paper, we propose an original scheme to assess the efficiency of segmentation algorithms requested to quantify bone architecture from three-dimensional (3-D) micromagnetic resonance imaging (/spl mu/MRI) techniques. Our scheme takes advantage of the high-quality images provided by synchrotron radiation computed microtomography (SR /spl mu/CT). To perform this assessment, calcaneus samples were acquired from both SR /spl mu/CT and MRI devices. The segmentation process is evaluated by comparing morphometric and topologic parameters computed from the SR /spl mu/CT reference images and those computed from the MRI segmented images. Both an adaptive thresholding algorithm and a region-growing algorithm are tested within this framework.

Adaptive thresholding based on variational background

A novel variational background-based adaptive thresholding algorithm is proposed. By denoising and edge detection, the background region is located. The background surface is then estimated by a variational algorithm. Finally, subtraction of the background surface from the preprocessed image is used to find a global threshold to obtain the binary result.

Novel precision target detection with adaptive thresholding for dynamic image segmentation

A new adaptive thresholding algorithm concerning extraction of targets from the background in a given image sequence is proposed. The conventional histogram-based or fixed-value thresholdings are deficient in detecting targets due to the poor contrast between targets and the background, or to the change of illumination. This research solves the problems mentioned above by learning the characteristics of the background from the given images and determines the proper thresholds based on this information. Experiments show that the proposed algorithm is superior to the optimal layering algorithm in target detection and tracking.

Block-median pyramidal transform: analysis and denoising applications

A nonlinear multiscale pyramidal transform based on nonoverlapping block decompositions using the median operation and a polynomial approximation is considered. It is shown that this structure can be useful for denoising of oneand two-dimensional (1-D and 2-D) signals. Various denoising techniques are analyzed, including methods based on spatially adaptive thresholding and partial cycle-spinning algorithms. An analytical method for deriving the distribution function of the transform coefficients is also presented. This, in turn, can be used for the selection of thresholds for denoising applications.

Detection of periodicity information from an audio signal

A waveform-based technique for generating periodicity information from an input signal includes generating a pre-processed signal by applying low pass and non-linear filtering to the input signal, wherein the pre-processed signal has highlighted speech pitch tracks. An adaptive threshold algorithm is applied to the pre-processed signal to generate a detection signal having waveform segments whose peaks are separated by a pitch period of the input signal. A period between peaks in the detection signal is determined that indicates the periodicity information. Information about the period between the peaks in the detection signal is then used to adapt a scaling value to be used by the adaptive threshold algorithm in a subsequent step. The periodicity information may be utilized in a voice activity detector in a telephonic communications system.