Fill Algorithm Research Articles

Calculation Method Based on Flower Petal Area and Other Plant Leaf Spot Area

In order to calculate the area of irregular shapes such as plant leaves and diseased spots or flower petals, this paper presents a new method to calculate them by using flood fill algorithm, HSV color space, improved k-means algorithm and morphological operation. First, 501 butterfly petal images and pathological leaf images of Bauhinia and phyllotaxus were collected, and then flood was used Fill algorithm selects the disease-free area and records the selected pixel value. HSV color space conversion is applied to the image to facilitate the segmentation of leaves. Then, the improved k-means algorithm is used to extract the binary image of leaves and record the pixel value of the outer contour with morphological closed operation. Finally, the proportion and truth of the disease spots of plant leaves are obtained by calculating the pixel value and the real value of the rectangle in the sampling area Real area. Compared with the results of artificial labeling, the average accuracy of petal area and lesion area of Phalaenopsis was 96.3% and 96.61%, respectively. It can be seen that the program can calculate the area of irregular shape of plant surface accurately. In conclusion, this method can replace the artificial grid method to calculate the information of plant leaf area and disease proportion, and effectively reduce the work intensity of experimental personnel.

Minimum magnitude boundaries in probabilistic seismic hazard analysis: an insight from structural engineering

In order to systematically advance our understanding of the minimum magnitude limit (Mmin) in the probabilistic seismic hazard analysis (PSHA) calculations, a novel and useful approach utilising a broad range of Single-Degree-of-Freedom oscillators and hazard conditions is being developed and tested. We have determined the most reasonable Mmin value for a variety of structures by examining the impact of Mmin on the mean annual frequency (MAF) of various limit states (LSs) (including the collapse capacity). The originality of the suggested methodology in the current work, known as the MAF saturation strategy, is the recommended Mmin, which is the cut-off value at which lesser magnitude events do add to the hazard but do not significantly change the MAF. The current work is the first to offer the MAF saturation strategy methodology, which searches for the cut-off magnitude at which the MAF value essentially remains constant even when smaller values of this cut-off are utilised as Mmin for hazard assessments. Therefore, given a series of carefully chosen ground motions in each oscillator instance, an incremental dynamic analysis is carried out (by applying the Hunt and Fill algorithm), and the appropriate LS (including the collapse capacity defined as global instability) points are calculated. Thus, the relationship between the distribution of LSs and the Engineering Demand Parameter and intensity measure is found. A simple point source hazard curve is convoluted with this distribution, yielding the structure-specific MAF. In order to find the cut-off lower magnitude (Mmin), this convolution is repeated for several Mmin values. This cut-off is defined as the point at which, when lower values are utilised as Mmin in the PSHA computation, the MAF’s values do not change considerably (with a five per cent threshold). The acquired data were thoroughly discussed in relation to various structural features and seismic input factors. The primary findings showed that each of the structures under consideration requires a Mmin value in the range of 4–4.3. Put otherwise, the suggestions seen in technical literature, which range from 4.5 to 5, are not cautious, at least not when it comes to probabilistic structural limit state frequency. The derived Mmin value is mostly controlled by the natural period of the structure and is largely unaffected by other structural characteristics like ductility, damping ratio and overstrength factor.

Simulation method of asphalt mixture based on fracturable digital aggregate model

This study proposed an approach to construct a fracturable aggregate model to make the fracture patterns consistent with the real situation. Firstly, this study utilized three-dimensional structured light technology to obtain the geometric shell of aggregate and divide them into four categories of spherical, angular, elongated and flaky aggregates according to the minimum bounding box algorithm. Secondly, this study proposed a discrete-spheres filling algorithm based on the Bubble Pack Filling algorithm to fill the shell accurately. Thirdly, this study established the conversion equations between the macro and micro parameters of aggregate by using the linear parallel bonding model, and used it to establish the fracturable aggregate model. Through the indoor and virtual static fracture tests, this study found that the fracture patterns of the fracturable aggregate models are similar to the real situation and characterized the fracture patterns of four types of aggregates. Further analyzing the virtual static fracture tests, this study found that the bearing capacity of aggregates is determined by the compressive strength of host rocks and the shape of aggregate. Through the indoor and virtual uniaxial compression creep tests, this study investigated the behavior of the mixture specimens consisted by the fracturable aggregate model. The stress-strain curves of the fracturable and unfracturable aggregate model in this test were plotted in this study, and the results showed that the relative error between the results obtained by the fracturable model and those of the indoor test is much smaller than that of the unfracturable model. Considering that existing simulation tests on asphalt mixture often ignore aggregate deformation and fracture, so this study used the method mentioned above to obtain the fracturable aggregate model, which can improve the confidence of asphalt mixture simulation results.

Treatment Planning Methods for Dose Painting by Numbers Treatment in Gamma Knife Radiosurgery

PURPOSEDose painting radiotherapy delivers a non-uniform dose to tumours to account for heterogeneous radiosensitivity. With recent and ongoing development of Gamma Knife (GK) machines making large-volume brain tumour treatments more practical, it is increasingly feasible to deliver dose painting treatments. The increased prescription complexity means automated treatment planning is greatly beneficial, and the impact of dose painting on SRS plan quality has not yet been studied. This research investigates the plan quality achievable for GK SRS dose painting treatments when using optimisation techniques and automated isocentre placement in treatment planning. METHODS & MATERIALSDose painting prescription functions with varying parameters are applied to convert voxel image intensities to prescriptions for ten sample cases. To study achievable plan quality and optimisation, clinically-placed isocentres are used with each dose painting prescription, and optimised using a semi-infinite linear programming formulation. To study automated isocentre placement, a grassfire sphere-packing algorithm and a clinically available Leksell Gamma Plan isocentre fill algorithm are used. Plan quality for each optimised treatment plan is measured with dose painting SRS metrics. RESULTSOptimisation can be used to find high quality dose painting plans, and plan quality is affected by the dose painting prescription method. Polynomial function prescriptions show better achievable plan quality than sigmoid function prescriptions even with high mean dose boost. Automated isocentre placement is shown as a feasible method for dose painting SRS treatment, and increasing the number of isocentres improves plan quality. The computational solve time for optimisation is within 5 minutes in most cases, which is suitable for clinical planning. CONCLUSIONSThe impact of dose painting prescription method on achievable plan quality is quantified in this study. Optimisation and automated isocentre placement are shown as possible treatment planning methods to obtain high quality plans.

Detection of large-scale Spartina alterniflora removal in coastal wetlands based on Sentinel-2 and Landsat 8 imagery on Google Earth Engine

The invasion of Spartina alterniflora has posed significant threats to the ecosystem health and biodiversity in coastal wetlands in China. In recent years, China has enacted large-scale S. alterniflora removal projects. Accurate monitoring of the S. alterniflora removal dynamics is crucial for evaluating the effectiveness of the removal projects and coastal wetland management. In this study, we presented a novel method for automatic and rapid detection of S. alterniflora removal events and removal timing at large scale based on time series Sentinel-2 and Landsat 8 imagery on Google Earth Engine. The method first detected and reconstructed the tidal-inundation-related Normalized Difference Vegetation Index (NDVI) using a newly proposed Tide Gap Filling algorithm, aiming to alleviate the impact of tidal inundation on S. alterniflora removal detection; then, the potential removal period were extracted based on the reconstructed NDVI time series; finally, the removal event and the corresponding removal timing were identified and mapped within the potential removal period by considering the phenological characteristics of the undisturbed S. alterniflora. We took the Shandong Province in northern coastal China and Fujian Province in southern coastal China as our study areas, where large-scale S. alterniflora removal projects were carried out in 2021 and 2022, respectively. Accuracy assessment based on field surveys and high-spatial-resolution PlanetScope imagery showed that the method achieved good accuracies for removal detection, with overall accuracies of 88.89 % for Shandong and 81.33 % for Fujian, respectively. The mean absolute error day (MAED) between the detected removal timing and the reference removal timing was 6.77 days in Shandong and 13.75 days in Fujian. Both provinces have successfully implemented the S. alterniflora removal projects. In Shandong, 82.32 % (7,243.94 ha) of S. alterniflora were removed during July to December in 2021; in Fujian, 88.81 % (6,649.21 ha) of S. alterniflora were removed during July to December in 2022. We expect that the method has great potential to be applied at national scale to provide baseline data for coastal wetland restoration and management.

A New Comprehensive Algorithm for the Cutting and Packing Problem with Position Constraints

<p>The cutting and packing (CP) is a widespread problem in the production processes of garment, shoes, board, and in other manufacturing industries. It is closely related to the production and management costs. This problem refers to the placement of pieces, without overlapping, in a limited space by maximizing the space utilization. In this work, a CP variant with position constraints, namely the horizontal striped fabric layout problem (HSFL), is investigated. We propose a solution approach for it, where a top-left fill (TLF) algorithm based on key points positioning strategy is used to deal with position constraints in the stripe alignment problem. This algorithm establishes a key points sequence for every piece, when determining the placement of pieces, compare the key points sequence of pieces. This algorithm also eliminates the error often occurred in the rectangle bounding box algorithm at the time of simplifying the layout process. The TLF algorithm based on key points positioning strategy and genetic algorithm (GA) are combined for obtaining the optimum layout. The datasets used in the computational experiment are collected from an apparel industry as well as from other publications. The comparison of our experimental results with the best known results shows that the proposed approach could improve the average computing speed and achieve higher utilization in 10 out of 16 instances. It can be concluded that the proposed approach is potential in balancing the computing speed and utilization rate. The algorithm has been successfully applied in an apparel company in China.</p> <p> </p>

Prevention of enclosed voids in topology optimization using a cumulative sum flood fill algorithm

Topology optimization has seen increased interest with the rise of additive manufacturing (AM) as a fabrication method, because of its ability to exploit the geometric complexity that AM offers. However, AM still imposes some geometric restrictions on the design, most notably on minimum feature size, overhang angles, and enclosed voids. Enclosed voids are problematic because for many AM methods it is impossible to remove supports, unmelted powder or uncured liquid from them. This paper introduces a filter based on a cumulative sum flood fill algorithm to alleviate this issue in a flexible manner. This filter produces a density field where every enclosed void element is rendered solid. It successfully eliminates enclosed voids in both 2D and 3D problems, with low computational cost due to its geometric nature. In addition we demonstrate direct control over the location, amount, and size of powder removal features by varying boundary conditions for the filter, running additional flood fills, and adding morphology operators, respectively.

Automatic vessel segmentation in MR angiography

Abstract Funding Acknowledgements Type of funding sources: None. Background Vessel-to-background contrast (VBC), the ratio of signal intensities (SI) within a vessel segment compared to the surrounding tissue, is an essential objective parameter for assessing image quality in contrast-enhanced MR angiography (CE-MRA) of peripheral arteries. (1–3) Manual evaluation by the measurement of SIs within the vessel and in the surrounding area is labour-intensive and tedious. (2) An algorithm for automatic segmentation of the vessel lumen and background signal reduces the workload significantly. It has already been successfully applied to CE-MRA in peripheral arterial disease (PAD). (3) However, whether this algorithm is equal to a manual evaluation is not yet known. Purpose To compare the manual and automatic evaluation of VBC in CE-MRA in patients with PAD. Methods A patient with suspected peripheral arterial disease underwent first-pass subtraction MRA on a 3T scanner. Region-of-interests (ROIs) were drawn on predefined vessel segments (Figure 1), manually inside and outside the vessel lumen and additionally a comprehensive ROI with the vessel under investigation in the centre. The algorithm segmented the comprehensive ROI into the vessel and background by identifying a seed point for the vessel and then using a flood fill algorithm. The remaining vessel branches were removed in order to approximate the true background signal. SI of the manual and the automatic segmentation were compared to define the VBC using the formula SI lumen - SI background divided by the standard deviation (SD) of the background for both methods. Results 43 vessel segments were evaluated. An example is given in Figure 2 with A) showing an axial view of the right thigh with a comprehensive ROI with the superficial femoral artery in the centre (green), and manual outlining of the lumen (red) and background (blue). Background signal (B1) and vessel lumen (B2) were used to evaluate the manuall values. Automated segmentation with C1) shows the ROI with the femoral artery in the centre. C2) shows the full extent of the vessel as identified by the flood-fill algorithm, and C3) the resulting mask. Background signal was calculated from SIs remaining in C4 after subtraction of the local maximas shown in C5). The mean SI in the vessel lumen was 4171 (standard deviation, SD 954) for the manual evaluation and 5014 (SD 830) for the automatic evaluation (p = 0.001) and in the background 601 (SD 542) for the manual evaluation and 493 (SD 229) for the automatic evaluation ((p = 0.231). The VBC was 10.3 (SD 5.9) for manual evaluation and 12.2 (SD 5.6) for automatic evaluation (p = 0.139). Conclusion Automatic segmentation of vessel lumen and background signal allows assessment of the VBC in CE-MRA in patients with PAD. The mean SI of the vessel lumen tends to be overestimated about 20% by the automatic algorithm. Higher patient numbers are required to further clarify the applicability of this algorithm for image quality evaluation.

An unsupervised image segmentation algorithm for coronary angiography

Computer visual systems can rapidly obtain a large amount of data and automatically process them with ease. These characteristics constitute advantages for the application of such systems in the automatic analysis of medical images, as well as in processing technology. The precision of image segmentation, which plays a critical role in computer visual systems, directly affects the quality of processing results. Coronary angiographs feature various background colors, complex patterns, and blurry edges. The image areas containing blood vessels cannot be precisely segmented through regular methods. Therefore, this study proposed an unsupervised learning algorithm that uses regional parameter expansion (RPE). This method was derived from the flood fill algorithm, which can effectively segment image areas containing blood vessels despite a complex background or uneven light and shadow. An optimal cover tree (OCT) algorithm was proposed for the establishment of coronary arteries and the estimation of vessel diameter. Through the region growing method, spanning trees were used to record the cover length of adjacent connections, thereby establishing vessel paths, and the length can be used to track changes in vessel diameter.

Metaheuristics-based nesting of parts in sheet metal cutting operation

Nesting of regular and irregular shaped parts in a sheet metal having constrained boundary so as to maximize effective utilization of material with minimum wastage imposes a challenging task to the metal cutting industries. To resolve the problem, this paper presents the applications of six popular metaheuristics, i.e. artificial bee colony, ant colony optimization, particle swarm optimization, firefly algorithm, differential evolution and teaching-learning-based optimization (TLBO) algorithm with an objective to maximize effective utilization ratio during metal cutting operation. For all the metaheuristics, the considered parts are optimally allocated in the given sheet metal based on bottom left fill algorithm to minimize the corresponding nested height. It is observed that TLBO algorithm supersedes the others with respect to effective utilization ratio, nested height and computational effort. A comparative analysis using values of t-statistic also proves the uniqueness of this algorithm over the others in efficiently solving the nesting problems for regular and irregular shaped parts during sheet metal cutting operation.

Effects of the Digital Elevation Model and Hydrological Processing Algorithms on the Geomorphological Parameterization

Hydrological cycle research requires a detailed analysis of the involved parameters to understand watershed behavior comprehensively. In recent decades, both Geographic Information Systems (GIS) and Digital Elevation Models (DEMs) were implemented and took a substantial role in watershed geomorphological parameterization; however, the variability of these instruments remains a challenge, together with high-resolution DEMs being unavailable, requiring digital processing to improve resolution. This research aims to merge DEMs and evaluate GIS geoprocessing algorithms to determine drainage networks and the geomorphological parametrization of a semiarid watershed. DEMs with resolutions of 1.5, 5, 12.5, and 30 m, the Jenson/Domingue (J/D) and Wang/Liu (W/L) fill algorithms; and D8, D, KRA, and MFD flow routing algorithms were used. One of the research findings proved that the divergences of the drainage networks are mainly attributed to filling algorithms and not flow routing algorithms; the shifts between the networks obtained in the processes reach horizontal distances up to 300 m. Since the water movement within the watershed depends on geomorphological characteristics, it is suggested that DEM-based hydrological studies specify both the resolution and the algorithms used in the parametrization to validate the rigidity of the research, improving estimate areas of high hydrological risk.

Evaluating alternative methods of comparing antihypertensive treatment intensity.

To change blood pressure treatment, clinicians can modify medication count or dose. However, existing studies have measured count modification, which may miss clinically important dose change in the absence of count change. This research demonstrates how dose modification captures more information about management than medication count alone. Retrospective cohort study. We included patients 65 years and older with established primary care at the Veterans Health Administration (July 2011-June 2013). We captured medication count and standardized dose change over 90 to 120 days using a validated pharmacy fill algorithm. We determined frequency of dose change without count change (and vice versa), no change in either, change in same direction ("concordant"), and change in opposite direction ("discordant"). We compared change according to systolic blood pressure (SBP) and compared concordance using a minimum threshold definition of dose change of at least 50% (instead of any change) of baseline dose modification. Among 440,801 patients, 64.2% had dose change; 22.0%, count change; 35.6%, no change in either; 42.4%, dose change without count modification; and 0.2%, count change without dose modification. Discordance occurred in 2.1% of observations. Using the minimum threshold definition of change, 68.7% had no change in either dose or count. Treatment was more frequently changed at SBP greater than 140 mm Hg. Measuring change in antihypertensive treatment using medication count frequently missed an isolated dose change in treatment modification and less often misclassified regimen modifications where there was no modification in total dose. In future research, measuring dose modification using our new algorithm would capture change in hypertension treatment intensity more precisely than current methods.

Detection of Cherry Quality Using YOLOV5 Model Based on Flood Filling Algorithm.

Presently, the quality of cherries in the market is uneven, because human senses are used to distinguish cherry quality, which consumes a lot of time and energy and does not achieve good results in terms of accuracy. If the internal quality indices, such as the PH value, sugar–acid ratio, and vitamin C content, of cherries are extracted using chemical methods, the detection speed will decrease. With the development of artificial intelligence (AI), image processing by AI algorithms has attracted broad attention. The YOLOv5 model in the YOLO series has many advantages, such as high detection accuracy, fast speed, small size, and so on, and has been used in face recognition, image recognition and other fields. However, owing to the influence of seasonal weather, the environment and other factors, the dataset used in the training model decreases the accuracy of image recognition. To improve the accuracy, a large amount of data must be used for model training, but this will decrease the model training speed. Because it is impossible to use all data in training, there will inevitably be recognition errors in the detection process. In this study, the cherry images in a dataset were extracted by the flooding filling algorithm. The extracted cherry images were used as a new dataset for training and recognition, and the results were compared to those obtained with non-extracted images. The dataset generated by the flooding filling algorithm was used for model training. After 20 training epochs, the accuracy rate reached 99.6%. Without using the algorithm to extract images, the accuracy rate was only 78.6% after 300 training epochs.

Design of Multi-Agent Pathfinding Robot Using Improved Flood Fill Algorithm in Maze Exploration

Design of Multi-Agent Pathfinding Robot Using Improved Flood Fill Algorithm in Maze Exploration

Research on Cell Counting Method Based on Flood Fill Algorithm

Cell counting has a wide range of applications in cytology research and clinical practice. Cell counts are widely used in the early diagnosis and treatment of serious diseases and the screening of effective drugs in the later stage. The method of studying cell counts is of great significance. This paper proposes a method for cell counting based on the highlight area of the cell center as a live cell marker. This method uses algorithms bilateral filtering to smooth noise in the original image; after adaptive threshold segmentation, an algorithm based on flood filling is used. Accurate segmentation of the highlighted area in the center of the cell; finally, counting is achieved through the connected domain labeling algorithm. The experimental results show that the accuracy of the cell counting method proposed in this paper is above 98%, which verifies the feasibility and practicability of the method proposed in this paper.

Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC).

Hepatocellular carcinoma (HCC) is considered as a complex liver disease and ranked as the eighth-highest mortality rate with a prevalence of 2.4% in Malaysia. Magnetic resonance imaging (MRI) has been acknowledged for its advantages, a gold technique for diagnosing HCC, and yet the false-negative diagnosis from the examinations is inevitable. In this study, 30 MR images from patients diagnosed with HCC is used to evaluate the robustness of semi-automatic segmentation using the flood fill algorithm for quantitative features extraction. The relevant features were extracted from the segmented MR images of HCC. Four types of features extraction were used for this study, which are tumour intensity, shape feature, textural feature and wavelet feature. A total of 662 radiomic features were extracted from manual and semi-automatic segmentation and compared using intra-class relation coefficient (ICC). Radiomic features extracted using semi-automatic segmentation utilized flood filling algorithm from 3D-slicer had significantly higher reproducibility (average ICC = 0.952 ± 0.009, p < 0.05) compared with features extracted from manual segmentation (average ICC = 0.897 ± 0.011, p > 0.05). Moreover, features extracted from semi-automatic segmentation were more robust compared to manual segmentation. This study shows that semi-automatic segmentation from 3D-Slicer is a better alternative to the manual segmentation, as they can produce more robust and reproducible radiomic features.

A novel energy management strategy to reduce gird dependency using electric vehicles storage in coordination with solar power

The effective coordination of solar photovoltaic (solar PV) with Electrical Vehicles (EV) can substantially improve the micro grid(MG) stability and economic benefits. This paper presents a novel Energy Management System (EMS) that synchronizes EV storage with Solar PV and load variability. Reducing grid dependency and energy cost of the MGs are the key objectives of the proposed EMS. A smart EV prioritization based control strategy is developed using fuzzy controller. Probabilistic approach is designed to estimate the EV usage expectancy in the near time zone that helps smart decision on choosing EVs. Minimizing battery degradation and maximizing EV storage exploitation are the key objectives of EV prioritization. On the other hand, Water Filling Algorithm (WFA) is used for Optimal Storage Distribution (OSD) in each zone of energy need for load flattening. The proposed EMS is implemented in a real time on-grid MG scenario and different case studies have been investigated to realize the impact of proposed EMS. A comprehensive cost analysis has been conducted and the efficacy of the proposed EMS is analysed.

Extracting a Bounded Region from a Map Using Flood Fill Algorithm

Extracting the needed portion from a bounded region is an important task in image processing. Editing a map and extracting a region from the map is challenging. It is useful in some contexts to have a region in a separate sheet. In this image processing, we have used the Flood Fill algorithm to extract a region from the image map. To achieve that goal, we had worked in our study to separate a bounded region on a map. Usually, a scanned map may contain a lot of useless information. So we have to process the image to remove useless information from the map. We had quantized the image to a binary one. In the second phase, we have applied a gray color to separate the desired position from a map. Our main objective of the study to extract a bounded region from mapping an image that contains useless information and removes it. We have experimented with several maps and it works successfully.

Real-Time Data Filling and Automatic Retrieval Algorithm of Road Traffic Based on Deep-Learning Method

In order to enhance the real-time and retrieval performance of road traffic data filling, a real-time data filling and automatic retrieval algorithm based on the deep-learning method is proposed. In image detection, the depth representation is extracted according to the detection target area of a general object. The local invariant feature is extracted to describe local attributes in the region, and it is fused with depth representation to complete the real-time data filling of road traffic. According to the results of the database enhancement, the retrieval results of the deep representation level are reordered. In the index stage, unsupervised feature updating is realized by neighborhood information to improve the performance of a feature retrieval. The experimental results show that the proposed method has high recall and precision, a short retrieval time and a low running cost.

Multiuser collaborative illustration and visualization for volumetric scientific data

AbstractMultiuser can collaboratively complete complex visualization tasks that cannot be completed by a single user. Although multiuser collaboration system has made great progress, there are many challenges in the collaborative visualization of 3D volumetric scientific data due to the difficulties in multiuser collaboration, and collaborative slice analysis. This article proposes a client‐server based collaborative visualization system, which consists of a 3D volume explorer and a 2D slice analyzer, to help domain experts to fully utilize their background domain knowledge to illustrate different parts of the data. For example, the brain surgeon expert, pulmonologist, and cardiologist can visualize and analyze the different subsets of the volumetric scientific data, that is, the corresponding subvolumes of the brain, heart, lungs, and blood vessels. It also allows taking full advantage of the hardware resources, because all the computation intensive tasks especially for the whole data rendering can be allocated to the powerful server while the light‐weight tasks can be allocated to the portable clients. Besides, we design a seed point tracing algorithm based on flood fill algorithm to illustrate the slice more efficiently. We evaluate the system by collecting the feedback from domain experts and the people who are unfamiliar with data computation or visualization. The evaluation shows that the 3D volume explorer and the 2D slice analyzer are capable of supporting peer‐expert discussion and medical case teaching, respectively.