Point-based Algorithm Research Articles

A Novel Point-Based Indexing Strategy for Biospeckle Sensors

In this letter, a novel point-based algorithm is presented for biospeckle sensors by combining random temporal history, robust normalization, and absolute value difference technique. Performance of the proposed strategy was evaluated and compared with existing strategies in simulation domain in terms of different analysis parameters (viz. frame invariance, correlation linearity, characterization of heterogeneity, and efficient computation). For experimental verification, biospeckle frames corresponding to coffee seeds ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coffea arabica</i> ) and maize seeds ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">zea maize</i> ) were utilized. From obtained results it can be inferred that, the proposed strategy possesses high accuracy in terms of all the analysis parameters. In future, the strategy can be widely utilized for industrial automation and quality inspection.

Development and Validation of Coding Algorithms to Identify Patients with Incident Non-Small Cell Lung Cancer in United States Healthcare Claims Data.

We sought to develop and validate an incident non-small cell lung cancer (NSCLC) algorithm for United States (US) healthcare claims data. Diagnoses and procedures, but not medications, were incorporated to support longer-term relevance and reliability. Patients with newly diagnosed NSCLC per Surveillance, Epidemiology, and End Results (SEER) served as cases. Controls included newly diagnosed small-cell lung cancer and other lung cancers, and two 5% random samples for other cancer and without cancer. Algorithms derived from logistic regression and machine learning methods used the entire sample (Approach A) or started with a previous algorithm for those with lung cancer (Approach B). Sensitivity, specificity, positive predictive values (PPV), negative predictive values, and F-scores (compared for 1000 bootstrap samples) were calculated. Misclassification was evaluated by calculating the odds of selection by the algorithm among true positives and true negatives. The best performing algorithm utilized neural networks (Approach B). A 10-variable point-score algorithm was derived from logistic regression (Approach B); sensitivity was 77.69% and PPV = 67.61% (F-score = 72.30%). This algorithm was less sensitive for patients ≥80 years old, with Medicare follow-up time <3 months, or missing SEER data on stage, laterality, or site and less specific for patients with SEER primary site of main bronchus, SEER summary stage 2000 regional by direct extension only, or pre-index chronic pulmonary disease. Our study developed and validated a practical, 10-variable, point-based algorithm for identifying incident NSCLC cases in a US claims database based on a previously validated incident lung cancer algorithm.

3D Object Detection from Point Cloud Based on Deep Learning

In order to study the modern 3D object detection algorithm based on deep learning, this paper studies the point-based 3D object detection algorithm, that is, a 3D object detection algorithm that uses multilayer perceptron to extract point features. This paper proposes a method based on point RCNN. A three-stage 3D object detection algorithm improves the accuracy of the algorithm by fusing image information. The algorithm in this paper integrates the information and image information of the three stages well, which improves the information utilization of the whole algorithm. Compared with the traditional 3D target detection algorithm, the structure of the algorithm in this paper is more compact, which effectively improves the utilization of information.

A clustering-based simplification of massive automobile-bodies point cloud for lightweight design

Adaptive simplification for massive and large-scale automobile-bodies point cloud obtained by 3D laser-scanning has been proven to be an effective technology to conduct lightweight design. This paper introduces a point-based algorithm to simplify laser-scanning point cloud without any support of fitted surface. The intrinsic characteristic of laser-scanning data is investigated to produce a topological connectivity for adjacent points in scanlines. We explore an automatic normal-vector estimation framework through the relationship between normal-vector and its adjacent geometric elements. To retain more points in high-curvature areas and fewer points in planar regions efficiently, the local normal-vector variance is adopted to determine subdivision-decision condition. The boundary points are detected and then preserved before non-uniform subdivision. A relevant simplification system based on our algorithm is developed. Many simplification cases are implemented to validate the effectiveness of our method and demonstrate the feasibility for automobile-bodies point cloud. The comparison with other point-based methods is also performed to illustrate the superiority of our method.

CF Model: A Coarse-to-Fine Model Based on Two-Level Local Search for Image Copy-Move Forgery Detection

Copy-move forgery is the most predominant forgery technique in the field of digital image forgery. Block-based and interest-based are currently the two mainstream categories for copy-move forgery detection methods. However, block-based algorithm lacks the ability to resist affine transformation attacks, and interest point-based algorithm is limited to accurately locate the tampered region. To tackle these challenges, a coarse-to-fine model (CFM) is proposed. By extracting features, affine transformation matrix and detecting forgery regions, the localization of tampered areas from sparse to precise is realized. Specifically, in order to further exactly extract the forged regions and improve performance of the model, a two-level local search algorithm is designed in the refinement stage. In the first level, the image blocks are used as search units for feature matching, and the second level is to refine the edge of the region at pixel level. The method maintains a good balance between the complexity and effectiveness of forgery detection, and the experimental results show that it has a better detection effect than the traditional interest-based copy and move forgery detection method. In addition, CFM method has high robustness on postprocessing operations, such as scaling, rotation, noise, and JPEG compression.

A Non-convex Economic Load Dispatch Using Hybrid Salp Swarm Algorithm

In this paper, the economic load dispatch (ELD) problem with valve point effect is tackled using a hybridization between salp swarm algorithm (SSA) as a population-based algorithm and $$\beta $$ -hill climbing optimizer as a single point-based algorithm. The proposed hybrid SSA is abbreviated as HSSA. This is to achieve the right balance between the intensification and diversification of the ELD search space. ELD is an important problem in the power systems which is concerned with scheduling the generation units in active generators in optimal way to minimize the fuel cost in accordance with equality and inequality constraints. The proposed HSSA is evaluated using six real-world ELD systems: 3-unit generator, two cases of 13-unit generator, 40-unit generator, 80-unit generator, and 140-unit generator system. These ELD systems are well circulated in the previous literature. The comparative results against 66 well-regarded algorithms are conducted. The results show that the proposed HSSA is able to produce viable and competitive solutions for ELD problems.

A male-ABCD algorithm for hepatocellular carcinoma risk prediction in HBsAg carriers.

ObjectiveHepatocellular carcinoma (HCC) development among hepatitis B surface antigen (HBsAg) carriers shows gender disparity, influenced by underlying liver diseases that display variations in laboratory tests. We aimed to construct a risk-stratified HCC prediction model for HBsAg-positive male adults.MethodsHBsAg-positive males of 35−69 years old (N=6,153) were included from a multi-center population-based liver cancer screening study. Randomly, three centers were set as training, the other three centers as validation. Within 2 years since initiation, we administrated at least two rounds of HCC screening using B-ultrasonography and α-fetoprotein (AFP). We used logistic regression models to determine potential risk factors, built and examined the operating characteristics of a point-based algorithm for HCC risk prediction.ResultsWith 2 years of follow-up, 302 HCC cases were diagnosed. A male-ABCD algorithm was constructed including participant’s age, blood levels of GGT (γ-glutamyl-transpeptidase), counts of platelets, white cells, concentration of DCP (des-γ-carboxy-prothrombin) and AFP, with scores ranging from 0 to 18.3. The area under receiver operating characteristic was 0.91 (0.90−0.93), larger than existing models. At 1.5 points of risk score, 26.10% of the participants in training cohort and 14.94% in validation cohort were recognized at low risk, with sensitivity of identifying HCC remained 100%. At 2.5 points, 46.51% of the participants in training cohort and 33.68% in validation cohort were recognized at low risk with 99.06% and 97.78% of sensitivity, respectively. At 4.5 points, only 20.86% of participants in training cohort and 23.73% in validation cohort were recognized at high risk, with positive prediction value of 22.85% and 12.35%, respectively.ConclusionsMale-ABCD algorithm identified individual’s risk for HCC occurrence within short term for their HCC precision surveillance.

Laboratory markers included in the Corona Score can identify false negative results on COVID-19 RT-PCR in the emergency room.

After December 2019 outbreak in China, the novel Coronavirus infection (COVID-19) has very quickly overflowed worldwide. Infection causes a clinical syndrome encompassing a wide range of clinical features, from asymptomatic or oligosymptomatic course to acute respiratory distress and death. In a very recent work we preliminarily observed that several laboratory tests have been shown as characteristically altered in COVID-19. We aimed to use the Corona score, a validated point-based algorithm to predict the likelihood of COVID-19 infection in patients presenting at the Emergency rooms. This approach combines chest images-relative score and several laboratory parameters to classify emergency room patients. Corona score accuracy was satisfactory, increasing the detection of positive patients’ rate.

Development and validation of coding algorithms to identify patients with incident lung cancer in United States healthcare claims data.

Our aim was to develop and validate a practical US healthcare claims algorithm for identifying incident lung cancer that improves on positive predictive value (PPV) and sensitivity observed in past studies. Patients newly diagnosed with lung cancer in Surveillance, Epidemiology, and End Results (SEER) (gold standard) were linked with Medicare claims. A 5% Medicare "other cancer" sample and noncancer sample served as controls. A split-sample validation approach was used. Rules-based, regression, and machine learning models for developing algorithms were explored. Algorithms were developed in the model building subset. Rules-based algorithms and those with the highest F scores were evaluated in the validation subset. F scores were compared for 1000 bootstrap samples. Misclassification was evaluated by calculating the odds of selection by the algorithm among true positives and true negatives. A practical single-score algorithm derived from a logistic regression model had sensitivity = 78.22% and PPV = 78.50% (F score: 78.36). The algorithm was most likely to misclassify older patients (ages ≥80 years) or with missing data in the SEER registry, shorter follow-up time in Medicare (<3 months), insurance through Veterans Affairs, >1 cancer in SEER, or certain Charlson comorbidities (dementia, chronic pulmonary disease, liver disease, or myocardial infarction). In this dataset, a practical point-based algorithm for identifying incident lung cancer demonstrated significant and substantial improvement (7.9% and 23.9% absolute improvement in sensitivity and PPV, respectively) compared with a current standard.

Rapid identification of SARS-CoV-2-infected patients at the emergency department using routine testing.

The novel coronavirus disease 19 (COVID-19), caused by SARS-CoV-2, spreads rapidly across the world. The exponential increase in the number of cases has resulted in overcrowding of emergency departments (ED). Detection of SARS-CoV-2 is based on an RT-PCR of nasopharyngeal swab material. However, RT-PCR testing is time-consuming and many hospitals deal with a shortage of testing materials. Therefore, we aimed to develop an algorithm to rapidly evaluate an individual's risk of SARS-CoV-2 infection at the ED. In this multicenter retrospective study, routine laboratory parameters (C-reactive protein, lactate dehydrogenase, ferritin, absolute neutrophil and lymphocyte counts), demographic data and the chest X-ray/CT result from 967 patients entering the ED with respiratory symptoms were collected. Using these parameters, an easy-to-use point-based algorithm, called the corona-score, was developed to discriminate between patients that tested positive for SARS-CoV-2 by RT-PCR and those testing negative. Computational sampling was used to optimize the corona-score. Validation of the model was performed using data from 592 patients. The corona-score model yielded an area under the receiver operating characteristic curve of 0.91 in the validation population. Patients testing negative for SARS-CoV-2 showed a median corona-score of 3 vs. 11 (scale 0-14) in patients testing positive for SARS-CoV-2 (p<0.001). Using cut-off values of 4 and 11 the model has a sensitivity and specificity of 96 and 95%, respectively. The corona-score effectively predicts SARS-CoV-2 RT-PCR outcome based on routine parameters. This algorithm provides the means for medical professionals to rapidly evaluate SARS-CoV-2 infection status of patients presenting at the ED with respiratory symptoms.

LAP-SLAM: A Line-Assisted Point-Based Monocular VSLAM

While the performance of the state-of-the-art point-based VSLAM (vision simultaneous localization and mapping) systems in well textured sequences is impressive, their performance in poorly textured situations is not satisfactory enough. A sensible alternative or addition is to consider lines. In this paper, we propose a novel line-assisted point-based VSLAM algorithm (LAP-SLAM). Our algorithm uses lines without descriptor matching, and the lines are used to assist the computation conducted by points. To the best of our knowledge, this paper proposes a new way to include line information in VSLAM. The basic idea is to use the collinear relationship of points to optimize the current point-based VSLAM algorithm. In LAP-SLAM, we propose a practical algorithm to match lines and compute the collinear relationship of points, a line-assisted bundle adjustment approach and a modified perspective-n-point (PnP) approach. We built our system based on the architecture and pipeline of ORB-SLAM. We evaluate the proposed method on a diverse range of indoor sequences in the TUM dataset and compare with point-based and point-line-based methods. The results show that the accuracy of our algorithm is close to point-line-based VSLAM systems with a much faster speed.

A corner point-based algorithm to solve constrained multi-objective optimization problems

The search ability of an algorithm in terms of convergence and diversity can be improved with the help of corner points. A corner point-based algorithm (CPA) based on a differential evolution (DE) algorithm is proposed to solve constrained multi-objective optimization problems. The evolutionary algorithm consists of two stages. The first stage is to find corner points by the proposed method. The second stage is to approach the real Pareto front. A novel diversity and convergence mechanism is implemented in the second stage. The performance of the proposed algorithm is evaluated on nineteen test functions. Compared with the constrained handling techniques and latest optimization algorithms, the numerical results have indicated that the proposed algorithm is effective. At last, the algorithm is used to solve resource schedule in emergency management to further validate its effectiveness.

3차원 정보 추출을 위한 다평면 피사계 심도 확장 알고리즘 연구

The extended depth of focus (EDF) algorithm has been investigated by many researchers. Many algorithms exist to solve EDF problems, such as the Point-based algorithm and Area-based algorithm. However the existing algorithms suffer inaccurate measurement of object depths inherent to the filters they use. To solve this problem and to improve measurement accuracy, we propose an algorithm that utilizes more than one filter applied to the whole 3-dimensional input image data set. First, a set of filters that have the property of finding the focused pixel plane are collected. Then, three separate filters, one applied to each direction of the 3-dimensional input image data set, are selected from the filter set. Through extensive simulated experiments, we found that some filters being applied to a certain direction of the 3-dimensional data set results in improved measurement accuracy compared to that of conventional algorithms.

Nonrigid Medical Image Registration Based on Curves

Medical image registration is the process of aligning two or more images in such a way that its anatomical structures properly overlap each other in a common spatial domain and resultant 3D images can be used for diagnosis and therapy by surgeons. A number of nonlinear methods have been developed for inter-subject and intra-subject 3D medical image registration. This paper is a part of research experiments which uses the Fast Radial Basis Function (RBF) technique for nonrigid registration of 3D medical images. The technique is a point-based registration evaluation algorithm which registers MR or CT images in less than a second with no compromise on accuracy as compared to standard RBF-based methods. Further we demonstrate that the accuracy of the registration improves when using increasingly more salient feature points (i.e. point landmarks and a few external curves) without affecting the speed of the algorithm. External curves are extracted using a combined watershed and active contours algorithm. Our results show that both accuracy and speed of the Fast Radial Basis algorithm is improved on intra-subject registration of MR image datasets obtained from the Vanderbilt Database as compared to the standard competing methods.

Extension of an iterative closest point algorithm for simultaneous localization and mapping in corridor environments

Three-dimensional (3-D) simultaneous localization and mapping (SLAM) is a crucial technique for intelligent robots to navigate autonomously and execute complex tasks. It can also be applied to shape measurement, reverse engineering, and many other scientific or engineering fields. A widespread SLAM algorithm, named KinectFusion, performs well in environments with complex shapes. However, it cannot handle translation uncertainties well in highly structured scenes. This paper improves the KinectFusion algorithm and makes it competent in both structured and unstructured environments. 3-D line features are first extracted according to both color and depth data captured by Kinect sensor. Then the lines in the current data frame are matched with the lines extracted from the entire constructed world model. Finally, we fuse the distance errors of these line-pairs into the standard KinectFusion framework and estimate sensor poses using an iterative closest point-based algorithm. Comparative experiments with the KinectFusion algorithm and one state-of-the-art method in a corridor scene have been done. The experimental results demonstrate that after our improvement, the KinectFusion algorithm can also be applied to structured environments and has higher accuracy. Experiments on two open access datasets further validated our improvements.

Lighting effects rendering in three-dimensional computer-generated holographic display

We present a technique for generating three-dimensional (3-D) computer-generated holograms (CGHs) with realistic lighting effects based on a phase-only spatial light modulator (SLM). Phong reflection model is employed in the calculation of reflectance distribution for CGH synthesizing. Directional point-based algorithm produces realistic lighting effects of the 3-D scenes in processing the ambient, diffuse and specular reflections. A phase-only SLM is used to perform the optical experiments, and the results show that the proposed technique can perform quality reconstructions of the 3-D scenes with high optical efficiency and efficient utilization of the system space-bandwidth product.

Multimodality liver registration of Open-MR and CT scans.

Multimodality registration of liver CT and MRI scans is challenging due to large initial misalignment, non-uniform MR signal intensity in the liver parenchyma, incomplete liver shapes in Open-MR scans and non-rigid deformations of the organ. An automated method was developed to register liver CT and open-MRI scans. A hybrid registration algorithm was developed which incorporates both rigid and non-rigid methods. First, large misalignment of input CT and Open-MR images was compensated by intensity-based registration. Maximum intensity projections (MIPs) of CT and MR data were registered in 2D, and the corresponding rigid transform parameters were used to align 3D images in axial, coronal and sagittal planes. Use of MIP projections compensates for intensity inhomogeneities inherent in the Open-MR data. A bounding box of MIP images defines an ROI which removes outliers and copes with incomplete MR data. Next, principal components analysis (PCA) was used to align MR and CT data datasets. The corresponding translation and rotation parameters were then used to increase the global registration accuracy. A modified TPS-RPM point-based non-rigid algorithm was used to accommodate local liver deformations. Surface points on the liver and branching points of the portal veins were input as landmarks to TPS-RPM method. Incorporating vascular branching points improves registration since tumors are usually found near vessels, so greater weight was given to branching points compared with surface points. The automated registration algorithm was compared with both rigid and non-rigid methods. Quantitative evaluation was performed using modified Hausdorff distance and overlap measure. The mean modified Hausdorff distances of liver and tumor were decreased from 23.53 and 40.03 mm to 9.38 and 8.88 mm, respectively. The mean overlap measures of liver and tumor were increased from 39 and 0 % to 78 and 27 %, respectively. Statistical analysis of the outcomes resulted in a p value less than 5 %. MIP-PCA-based rigid multimodality CT-MRI registration of liver scans compensates for large misalignment of input images even when the data are incomplete. A modified TPS-RPM algorithm, in which vascular points are emphasized over surface points, successfully handled local deformations.

Image-guided radiation therapy for prostate cancer: A computed tomography–based assessment of fiducial marker migration between placement and 7 days

PurposeThis study was conducted to determine whether clinically significant fiducial marker migration occurs immediately after prostatic implantation. Methods and materialsOne hundred patients with transperineal (n = 39) or transrectal (n = 61) placement of 3 gold fiducial markers underwent computed tomography scans on day 0 (after placement) and day 7 (at radiation planning). Each marker was marked as a point of interest in a treatment planning system. An automated point-based algorithm was then used to coregister the day 0 and day 7 images by matching the markers through rigid translations and rotations. The mean distance between fiducial pairs (d¯) was recorded to assess the degree of seed migration. Prostate contours were delineated, and the day 0 prostate volumes were uniformly expanded by 1, 3, and 5 mm. The percentage of the day 7 prostate volume covered by each day 0 prostate with expansion was calculated to assess whether prostate contours, if performed on day 0, would adequately cover the prostate on day 7. ResultsThe average d¯ for all patients was 0.78 ± 0.45 mm; only 1 patient had d¯ > 2 mm. Placement technique, hormonal therapy, prostate size, and marker distance from the capsule were not associated with d¯ (P > .05). The mean percentages of day 7 prostate volumes covered by the day 0 prostate plus 1, 3, and 5 mm were 98.3%, 99.8%, and 100%, respectively. With an expansion of 3 mm, 98% of men had >95% of day 0 volume covered; with an expansion of 5 mm, 100% of men had 100% of the day 0 volume covered. ConclusionsThere is minimal change in the relative positions of fiducial markers (average d¯ < 1.0 mm) 1 week after placement. A 1- to 3-mm expansion would account for the variation in seed position for the vast majority of cases. These results suggest that planning could be performed on the day of implantation without adverse consequence.

A Model-Based Factored Bayesian Reinforcement Learning Approach

Bayesian reinforcement learning has turned out to be an effective solution to the optimal tradeoff between exploration and exploitation. However, in practical applications, the learning parameters with exponential growth are the main impediment for online planning and learning. To overcome this problem, we bring factored representations, model-based learning, and Bayesian reinforcement learning together in a new approach. Firstly, we exploit a factored representation to describe the states to reduce the size of learning parameters, and adopt Bayesian inference method to learn the unknown structure and parameters simultaneously. Then, we use an online point-based value iteration algorithm to plan and learn. The experimental results show that the proposed approach is an effective way for improving the learning efficiency in large-scale state spaces.

A Novel Point-Based Incremental Pruning Algorithm for POMDP

Partially Observable Markov Decision Processes (POMDP) provides piecewise-linear a natural and principled framework for sequential decision-making under uncertainty. However, large-scale POMDP suffers from the exponential growth of the belief points and policy trees space. We present a new point-based incremental pruning algorithm based on the piecewise linearity and convexity of the value function. Instead of reasoning about the whole belief space when pruning the cross-sums in POMDP policy construction, our algorithm uses belief points to perform approximate pruning by generating policy trees, and get the optimal policy in real-time belief states. The empirical results indicate that point-based incremental pruning for heuristic search methods can handle large POMDP domains efficiently.