Journal articles on the topic 'Hausdorff distance measures'
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Beer, Gerald, and Luzviminda Villar. "Borel measures and Hausdorff distance." Transactions of the American Mathematical Society 307, no. 2 (February 1, 1988): 763. http://dx.doi.org/10.1090/s0002-9947-1988-0940226-0.
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Ali, Mehboob, Zahid Hussain, and Miin-Shen Yang. "Hausdorff Distance and Similarity Measures for Single-Valued Neutrosophic Sets with Application in Multi-Criteria Decision Making." Electronics 12, no. 1 (December 31, 2022): 201. http://dx.doi.org/10.3390/electronics12010201.
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Hausdorff distance is one of the important distance measures to study the degree of dissimilarity between two sets that had been used in various fields under fuzzy environments. Among those, the framework of single-valued neutrosophic sets (SVNSs) is the one that has more potential to explain uncertain, inconsistent and indeterminate information in a comprehensive way. And so, Hausdorff distance for SVNSs is important. Thus, we propose two novel schemes to calculate the Hausdorff distance and its corresponding similarity measures (SMs) for SVNSs. In doing so, we firstly develop the two forms of Hausdorff distance between SVNSs based on the definition of Hausdorff metric between two sets. We then use these new distance measures to construct several SMs for SVNSs. Some mathematical theorems regarding the proposed Hausdorff distances for SVNSs are also proven to strengthen its theoretical properties. In order to show the exact calculation behavior and distance measurement mechanism of our proposed methods in accordance with the decorum of Hausdorff metric, we utilize an intuitive numerical example that demonstrate the novelty and practicality of our proposed measures. Furthermore, we develop a multi-criteria decision making (MCDM) method under single-valued neutrosophic environment using the proposed SMs based on our defined Hausdorff distance measures, called as a single-valued neutrosophic MCDM (SVN-MCDM) method. In this connection, we employ our proposed SMs to compute the degree of similarity of each option with the ideal choice to identify the best alternative as well as to perform an overall ranking of the alternatives under study. We then apply our proposed SVN-MCDM scheme to solve two real world problems of MCDM under single-valued neutrosophic environment to show its effectiveness and application.
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Dong-Gyu Sim, Oh-Kyu Kwon, and Rae-Hong Park. "Object matching algorithms using robust Hausdorff distance measures." IEEE Transactions on Image Processing 8, no. 3 (March 1999): 425–29. http://dx.doi.org/10.1109/83.748897.
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XU, Z. S., and J. CHEN. "AN OVERVIEW OF DISTANCE AND SIMILARITY MEASURES OF INTUITIONISTIC FUZZY SETS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 04 (August 2008): 529–55. http://dx.doi.org/10.1142/s0218488508005406.
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The Intuitionistic Fuzzy Sets (IFSs), originated by Atanassov [1], is a useful tool to deal with vagueness and ambiguity. In the short time since their first appearance, many different distance and similarity measures of IFSs have been proposed, but they are scattered through the literature. In this paper, we give a comprehensive overview of distance and similarity measures of IFSs. Based on the weighted Hamming distance, the weighted Euclidean distance, and the weighted Hausdorff distance, respectively, we define some continuous distance and similarity measures for IFSs. We also utilize geometric distance model to define some continuous distance and similarity measures for IFSs, which are the various combinations and generalizations of the weighted Hamming distance, the weighted Euclidean distance and the weighted Hausdorff distance. Then we extend these distance and similarity measures for Interval-Valued Intuitionistic Fuzzy Sets (IVIFSs).
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Hung, Wen-Liang, and Miin-Shen Yang. "Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance." Pattern Recognition Letters 25, no. 14 (October 2004): 1603–11. http://dx.doi.org/10.1016/j.patrec.2004.06.006.
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Jee, Hana, Monica Tamariz, and Richard Shillcock. "Quantified Grapho-Phonemic Systematicity in Korean Hangeul." Asian Culture and History 15, no. 1 (February 2, 2023): 25. http://dx.doi.org/10.5539/ach.v15n1p25.
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Hangeul, the Korean orthography is well known for its scientific design that emphasizes the link between sounds and letter shapes. However, it hasn’t been asked so far ‘how systematic’ it is. We quantify, for the first time, the grapho-phonemic systematicity of hangeul. We defined Korean phonemes as binary vectors according to articulatory features and then measured the pairwise phonemic distance between phonemes using multiple methods. We measured the pairwise visual distance between letter shapes by (a) stroke share rate, which reflects the original principles of hangeul’s creation, and (b) Hausdorff distance (Huttenlocher et al., 1993), which measures topological difference between images. We then tested the correlation between the phonological distances and the corresponding orthographical distances. Positive correlations clearly indicated that similar letters tend to have similar pronunciations in Korean hangeul. Stroke share rate maximizes hangeul’s grapho-phonemic systematicity. Hausdorff distance, an initial step in the detailed quantifying of visual distance, allows similar calculations to be carried out with any hangeul font and with any other orthography (Jee, Tamariz, & Shillcock, 2021; 2022a; 2022b). Consciously designed to be phonologically transparent, hangeul can be considered as the gold standard of grapho-phonemic systematicity. We discuss the implications of this systematicity.
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Banaś, Józef, and Antonio Martinón. "Some properties of the Hausdorff distance in metric spaces." Bulletin of the Australian Mathematical Society 42, no. 3 (December 1990): 511–16. http://dx.doi.org/10.1017/s0004972700028677.
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Ren, Wenjuan, Zhanpeng Yang, and Xipeng Li. "Distance Measures Based on Metric Information Matrix for Atanassov’s Intuitionistic Fuzzy Sets." Axioms 12, no. 4 (April 14, 2023): 376. http://dx.doi.org/10.3390/axioms12040376.
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The metric matrix theory is an important research object of metric measure geometry and it can be used to characterize the geometric structure of a set. For intuitionistic fuzzy sets (IFS), we defined metric information matrices (MIM) of IFS by using the metric matrix theory. We introduced the Gromov–Hausdorff metric to measure the distance between any two MIMs. We then constructed a kind of metric information matrix distance knowledge measure for IFS. The proposed distance measures have the ability to measure the distance between two incomplete intuitionistic fuzzy sets. In order to reduce the information confusion caused by the disorder of MIM, we defined a homogenous metric information matrix distance by rearranging MIM. Some theorems are given to show the properties of the constructed distance measures. At the end of the paper, some numerical experiments are given to show that the proposed distances can recognize different patterns represented by IFS.
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Schwedler, Benjamin R. J., and Michael E. Baldwin. "Diagnosing the Sensitivity of Binary Image Measures to Bias, Location, and Event Frequency within a Forecast Verification Framework." Weather and Forecasting 26, no. 6 (December 1, 2011): 1032–44. http://dx.doi.org/10.1175/waf-d-11-00032.1.
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Abstract While the use of binary distance measures has a substantial history in the field of image processing, these techniques have only recently been applied in the area of forecast verification. Designed to quantify the distance between two images, these measures can easily be extended for use with paired forecast and observation fields. The behavior of traditional forecast verification metrics based on the dichotomous contingency table continues to be an area of active study, but the sensitivity of image metrics has not yet been analyzed within the framework of forecast verification. Four binary distance measures are presented and the response of each to changes in event frequency, bias, and displacement error is documented. The Hausdorff distance and its derivatives, the modified and partial Hausdorff distances, are shown only to be sensitive to changes in base rate, bias, and displacement between the forecast and observation. In addition to its sensitivity to these three parameters, the Baddeley image metric is also sensitive to additional aspects of the forecast situation. It is shown that the Baddeley metric is dependent not only on the spatial relationship between a forecast and observation but also the location of the events within the domain. This behavior may have considerable impact on the results obtained when using this measure for forecast verification. For ease of comparison, a hypothetical forecast event is presented to quantitatively analyze the various sensitivities of these distance measures.
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Meng, Lingyan, and Xiaoyan Wei. "Research on Evaluation of Sustainable Development of New Urbanization from the Perspective of Urban Agglomeration under the Pythagorean Fuzzy Sets." Discrete Dynamics in Nature and Society 2021 (August 16, 2021): 1–11. http://dx.doi.org/10.1155/2021/2445025.
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In this study, considering the traditional geometric operation laws and Pythagorean fuzzy information, we propose a variety of new distance measures of Pythagorean fuzzy sets such as generalized Pythagorean fuzzy geometric distance (GPFGD) measures and generalized Pythagorean fuzzy weighted geometric distance (GPFWGD) measures. Besides, some special issues including Hamming distance, Euclidean distance, and Hausdorff distance of these raised geometric distance measures are investigated. To testify the valid of these new presented distance measures, we build a decision-making model illustrated by a mathematical calculation example to evaluate the sustainable development of new urbanization from the perspective of urban agglomeration using Pythagorean fuzzy information.
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De, Sujit Kumar, and Shib Sankar Sana. "Two-layer supply chain model for Cauchy-type stochastic demand under fuzzy environment." International Journal of Intelligent Computing and Cybernetics 11, no. 2 (June 11, 2018): 285–308. http://dx.doi.org/10.1108/ijicc-10-2016-0037.
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Purpose The purpose of this paper is to deal with profit maximization problem of two-layer supply chain (SC) under fuzzy stochastic demand having finite mean and unknown variance. Buyback policy is employed from the retailer to supplier. The profit of the supplier solely depends on the order size of the retailers. However, the loss of shortage items is related to loss of profit and goodwill dependent. The authors develop the profit function separately for both the retailer and supplier, first, for a decentralized system and, second, joining them, the authors get a centralized system (CS) of decision making, in which one is giving more profit to both of them. The problem is solved analytically first, then the authors fuzzify the model and solve by fuzzy Hausdorff distance method. Design/methodology/approach The analytical models are formed for both centralized and decentralized systems under non-cooperative and cooperative environment with suitable constraints. A significant assumption on density function, namely Cauchy-type density function, is introduced for demand rate because of its wider range of the retailers’ satisfactions. Fuzzy Hausdorff metric is incorporated within the fuzzy components of the fuzzy sets itself. Using this method, the authors find out closure values of both centralized and decentralized policies, which is an essential part of any cooperative and non-cooperative two-layer SC models. Moreover, the authors take care of the profit values with corresponding ambiguities for both the systems explicitly. Findings It is found that the centralize policy of SC could only be able to maximize the profit of both the retailers and suppliers. All analytical results are illustrated numerically along with sensitivity analysis and side by side comparative studies between Hausdorff and Euclidean distance measure are done exclusively. Research limitations/implications The main focus of attention of the proposed model is given to usefulness of Hausdorff distance. Unlike other distances, Hausdorff distance can take special care on the similarity measures of different fuzzy sets. Researchers have been engaged to use Hausdorff distance on the different fuzzy sets but, in this study, the authors have used it within the components of a same fuzzy set to gain more closure values than other methods. Originality/value The use of this Hausdorff distance approach is totally new as per literature survey suggested yet. However, the Cauchy-type density function has not been introduced anywhere in SC management problems by modern researchers still now. In crisp model, the sensitivity on goodwill measures really provides a special attention also.
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Shen, Yonghong. "On the probabilistic Hausdorff distance and a class of probabilistic decomposable measures." Information Sciences 263 (April 2014): 126–40. http://dx.doi.org/10.1016/j.ins.2013.09.042.
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Schuhmann, Fabian, Leonie Ryvkin, James D. McLaren, Luca Gerhards, and Ilia A. Solov’yov. "Across atoms to crossing continents: Application of similarity measures to biological location data." PLOS ONE 18, no. 5 (May 15, 2023): e0284736. http://dx.doi.org/10.1371/journal.pone.0284736.
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Biological processes involve movements across all measurable scales. Similarity measures can be applied to compare and analyze these movements but differ in how differences in movement are aggregated across space and time. The present study reviews frequently-used similarity measures, such as the Hausdorff distance, Fréchet distance, Dynamic Time Warping, and Longest Common Subsequence, jointly with several measures less used in biological applications (Wasserstein distance, weak Fréchet distance, and Kullback-Leibler divergence), and provides computational tools for each of them that may be used in computational biology. We illustrate the use of the selected similarity measures in diagnosing differences within two extremely contrasting sets of biological data, which, remarkably, may both be relevant for magnetic field perception by migratory birds. Specifically, we assess and discuss cryptochrome protein conformational dynamics and extreme migratory trajectories of songbirds between Alaska and Africa. We highlight how similarity measures contrast regarding computational complexity and discuss those which can be useful in noise elimination or, conversely, are sensitive to spatiotemporal scales.
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Liu, Ling Yun, Min Luo, and Yue Min Wu. "Study of Pose Detection Algorithm for Specific Object Based on Monocular Vision." Applied Mechanics and Materials 651-653 (September 2014): 517–23. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.517.
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This paper brings forward a monocular vision detection algorithm in allusion to the specific object’s pose based on Hausdorff Distance. At first, according to the mathematic model which has been established, the 2D template sequence is generated by projecting the specific object in different poses into the image plane of a virtual camera. Then, in order to predigest calculations and accelerate the matching speed during the image matching, the algorithm adopts the local-mean Hausdorff Distance as matching estimate and adopts the search strategy based on hiberarchy which reduces the searching rang by the threshold method before accurate match. In the end of this paper, the experiment that measures the poses of the clamp via the different clamp images respectively is given to testify the validity and speediness of this algorithm.
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Hong, Yanran, Dongsheng Xu, Kaili Xiang, Han Qiao, Xiangxiang Cui, and Huaxiang Xian. "Multi-Attribute Decision-Making Based on Preference Perspective with Interval Neutrosophic Sets in Venture Capital." Mathematics 7, no. 3 (March 12, 2019): 257. http://dx.doi.org/10.3390/math7030257.
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Fuzzy information in venture capital can be well expressed by neutrosophic numbers, and TODIM method is an effective tool for multi-attribute decision-making. The distance measure is an essential step in TODIM method. The keystone of this paper is to define several new distance measures, in particular the improved interval neutrosophic Euclidean distance, and these measures are applied in the TODIM method for multi-attribute decision-making. Firstly, the normalized generalized interval neutrosophic Hausdorff distance is defined and proved to be valid in this paper. Secondly, we define a weighted parameter interval neutrosophic distance and discuss whether different weight parameters affect the decision result based on TODIM method. Thirdly, considering the preference perspective of decision-makers in behavioral economics, we define the improved interval neutrosophic Euclidean distance with the known parameter of risk preference. Finally, an application example is given to compare the effects of different parameters on the result and discuss the feasibility of these two distance measures in TODIM method.
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Hussain, Zahid, Sahar Abbas, and Miin-Shen Yang. "Distances and Similarity Measures of Q-Rung Orthopair Fuzzy Sets Based on the Hausdorff Metric with the Construction of Orthopair Fuzzy TODIM." Symmetry 14, no. 11 (November 21, 2022): 2467. http://dx.doi.org/10.3390/sym14112467.
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In recent years, q-rung orthopair fuzzy sets (q-ROFSs), a novel and rigorous generalization of the fuzzy set (FS) coined by Yager in 2017, have been used to manage inexplicit and indefinite information in daily life with a high precision and greater accuracy than intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets (PFSs). The characterization of a measure of similarity between q-ROFSs is important, as they have applications in different areas, including pattern recognition, clustering, image segmentation and decision making. Therefore, this article is dedicated to the construction of a measure of similarity between q-ROFSs based on the Hausdorff metric. This is a very useful tool for establishing the similarity between two objects. Furthermore, some axiomatic definitions of the distances and similarity measures of q-ROFSs are also presented. In this article, we first present a novel method to calculate the distance between q-ROFSs based on the Hausdorff metric. We then utilize our proposed distance measure to construct the degree of similarity between q-ROFSs. We provide some properties for the proposed similarity measures. We offer several numerical examples related to pattern recognition and characterization linguistic variables to demonstrate the usefulness of the proposed similarity measures. We construct an algorithm for orthopair fuzzy TODIM (interactive and multi-criteria decision making, in Portuguese) based on our proposed methods. Finally, we use the constructed orthopair fuzzy TODIM method to address problems related to daily life settings involving multi-criteria decision making (MCDM). The numerical results show that the proposed similarity measures are suitable, applicable and well-suited to the contexts of pattern recognition, queries with fuzzy linguistic variables and MCDM.
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Santos, Thiago, and S. Xavier. "A Convergence Indicator for Multi-Objective Optimisation Algorithms." TEMA (São Carlos) 19, no. 3 (December 17, 2018): 437. http://dx.doi.org/10.5540/tema.2018.019.03.437.
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The algorithms of multi-objective optimisation had a relative growth in the last years. Thereby, it's requires some way of comparing the results of these. In this sense, performance measures play a key role. In general, it's considered some properties of these algorithms such as capacity, convergence, diversity or convergence-diversity. There are some known measures such as generational distance (GD), inverted generational distance (IGD), hypervolume (HV), Spread($\Delta$), Averaged Hausdorff distance ($\Delta_p$), R2-indicator, among others. In this paper, we focuses on proposing a new indicator to measure convergence based on the traditional formula for Shannon entropy. The main features about this measure are: 1) It does not require tho know the true Pareto set and 2) Medium computational cost when compared with Hypervolume.
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PINTO, MARTIN CAMPOS, ALBERT COHEN, WOLFGANG DAHMEN, and RONALD DEVORE. "ON THE STABILITY OF NONLINEAR CONSERVATION LAWS IN THE HAUSDORFF METRIC." Journal of Hyperbolic Differential Equations 02, no. 01 (March 2005): 25–38. http://dx.doi.org/10.1142/s0219891605000348.
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The mapping properties of the time evolution operator E(t) of nonlinear hyperbolic scalar conservation laws is investigated. It is shown that this operator is Lipschitz in the Hausdorff metric in one space dimension whenever the flux is convex and one of the initial conditions satisfies a one-sided Lipschitz condition. The Hausdorff distance between the graphs of the solutions measures the closeness in L∞ in the regions where the solutions are smooth, as well as the closeness between the locations of shocks. A similar result on Hausdorff stability is proved with respect to a perturbation of the flux function. These results complement the well known L1 contractivity of the solution operator. They are used in a subsequent paper to prove new smoothness results for solutions to such conservation laws. Negative results are proved in the case of non-convex and genuinely multidimensional fluxes.
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Peng, Juan-Juan, Jian-Qiang Wang, and Xiao-Hui Wu. "Novel Multi-criteria Decision-making Approaches Based on Hesitant Fuzzy Sets and Prospect Theory." International Journal of Information Technology & Decision Making 15, no. 03 (May 2016): 621–43. http://dx.doi.org/10.1142/s0219622016500152.
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Hesitant fuzzy sets (HFSs), an extension of fuzzy sets, are considered to be useful in solving decision making problems where decision makers are unable to choose between several values when expressing their preferences. The purpose of this paper is to develop two hesitant fuzzy multi-criteria decision making (MCDM) methods based on prospect theory (PT). First, the novel component-wise ordering method for two hesitant fuzzy numbers (HFNs) is defined; however, this method does not consider the length of the two HFNs. Second, by utilizing the directed Hausdorff distance between two imprecise point sets, the generalized hesitant Hausdorff distance is developed, which overcomes the shortcomings of the existing distance measures. Third, based on the proposed comparison method and distance, as well as PT, the extended TODIM and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) approaches are developed in order to solve MCDM problems with hesitant fuzzy information. Finally, a practical example is provided to illustrate the pragmatism and effectiveness of the proposed approaches. Sensitivity and comparison analyses are also conducted using the same example. The findings indicate that the proposed methods do not require complicated computation procedures, yet still yield a reasonable and credible solution.
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Mohamad, Daud, Noorlisa Sara Adlene Ramlan, and Sharifah Aniza Sayed Ahmad. "An improvised similarity measure for generalized fuzzy numbers." Bulletin of Electrical Engineering and Informatics 8, no. 4 (December 1, 2019): 1232–38. http://dx.doi.org/10.11591/eei.v8i4.1629.
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Similarity measure between two fuzzy sets is an important tool for comparing various characteristics of the fuzzy sets. It is a preferred approach as compared to distance methods as the defuzzification process in obtaining the distance between fuzzy sets will incur loss of information. Many similarity measures have been introduced but most of them are not capable to discriminate certain type of fuzzy numbers. In this paper, an improvised similarity measure for generalized fuzzy numbers that incorporate several essential features is proposed. The features under consideration are geometric mean averaging, Hausdorff distance, distance between elements, distance between center of gravity and the Jaccard index. The new similarity measure is validated using some benchmark sample sets. The proposed similarity measure is found to be consistent with other existing methods with an advantage of able to solve some discriminant problems that other methods cannot. Analysis of the advantages of the improvised similarity measure is presented and discussed. The proposed similarity measure can be incorporated in decision making procedure with fuzzy environment for ranking purposes.
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JIANG, MINGHUI, YING XU, and BINHAI ZHU. "PROTEIN STRUCTURE–STRUCTURE ALIGNMENT WITH DISCRETE FRÉCHET DISTANCE." Journal of Bioinformatics and Computational Biology 06, no. 01 (February 2008): 51–64. http://dx.doi.org/10.1142/s0219720008003278.
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Matching two geometric objects in two-dimensional (2D) and three-dimensional (3D) spaces is a central problem in computer vision, pattern recognition, and protein structure prediction. In particular, the problem of aligning two polygonal chains under translation and rotation to minimize their distance has been studied using various distance measures. It is well known that the Hausdorff distance is useful for matching two point sets, and that the Fréchet distance is a superior measure for matching two polygonal chains. The discrete Fréchet distance closely approximates the (continuous) Fréchet distance, and is a natural measure for the geometric similarity of the folded 3D structures of biomolecules such as proteins. In this paper, we present new algorithms for matching two polygonal chains in two dimensions to minimize their discrete Fréchet distance under translation and rotation, and an effective heuristic for matching two polygonal chains in three dimensions. We also describe our empirical results on the application of the discrete Fréchet distance to protein structure–structure alignment.
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Hussian, Zahid, and Miin‐Shen Yang. "Distance and similarity measures of Pythagorean fuzzy sets based on the Hausdorff metric with application to fuzzy TOPSIS." International Journal of Intelligent Systems 34, no. 10 (August 21, 2019): 2633–54. http://dx.doi.org/10.1002/int.22169.
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Li, Yimin, Shyam Rao, Wen Chen, Soheila F. Azghadi, Ky Nam Bao Nguyen, Angel Moran, Brittni M. Usera, et al. "Evaluating Automatic Segmentation for Swallowing-Related Organs for Head and Neck Cancer." Technology in Cancer Research & Treatment 21 (January 2022): 153303382211057. http://dx.doi.org/10.1177/15330338221105724.
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Purpose: To evaluate the accuracy of deep-learning-based auto-segmentation of the superior constrictor, middle constrictor, inferior constrictor, and larynx in comparison with a traditional multi-atlas-based method. Methods and Materials: One hundred and five computed tomography image datasets from 83 head and neck cancer patients were retrospectively collected and the superior constrictor, middle constrictor, inferior constrictor, and larynx were analyzed for deep-learning versus multi-atlas-based segmentation. Eighty-three computed tomography images (40 diagnostic computed tomography and 43 planning computed tomography) were used for training the convolutional neural network, and for atlas-based model training. The remaining 22 computed tomography datasets were used for validation of the atlas-based auto-segmentation versus deep-learning-based auto-segmentation contours, both of which were compared with the corresponding manual contours. Quantitative measures included Dice similarity coefficient, recall, precision, Hausdorff distance, 95th percentile of Hausdorff distance, and mean surface distance. Dosimetric differences between the auto-generated contours and manual contours were evaluated. Subjective evaluation was obtained from 3 clinical observers to blindly score the autosegmented structures based on the percentage of slices that require manual modification. Results: The deep-learning-based auto-segmentation versus atlas-based auto-segmentation results were compared for the superior constrictor, middle constrictor, inferior constrictor, and larynx. The mean Dice similarity coefficient values for the 4 structures were 0.67, 0.60, 0.65, and 0.84 for deep-learning-based auto-segmentation, whereas atlas-based auto-segmentation has Dice similarity coefficient results at 0.45, 0.36, 0.50, and 0.70, respectively. The mean 95th percentile of Hausdorff distance (cm) for the 4 structures were 0.41, 0.57, 0.59, and 0.54 for deep-learning-based auto-segmentation, but 0.78, 0.95, 0.96, and 1.23 for atlas-based auto-segmentation results, respectively. Similar mean dose differences were obtained from the 2 sets of autosegmented contours compared to manual contours. The dose–volume discrepancies and the average modification rates were higher with the atlas-based auto-segmentation contours. Conclusion: Swallowing-related structures are more accurately generated with DL-based versus atlas-based segmentation when compared with manual contours.
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Sahu, Rekha, Satya R. Dash, and Sujit Das. "Career selection of students using hybridized distance measure based on picture fuzzy set and rough set theory." Decision Making: Applications in Management and Engineering 4, no. 1 (March 15, 2021): 104–26. http://dx.doi.org/10.31181/dmame2104104s.
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Since the future of the society depends upon the role of students, so suitable career selection methods for the students are considered to be an important problem to explore. It is assumed that if a student has the required capability and positive attitudes towards a subject, then the student will achieve more in that subject. To consider the uncertain issues involved with students’ career selection, picture fuzzy set (PFS) and rough set based approaches are proposed in this study as they are found to be appropriate due to their inherent characteristics to deal with incomplete and imprecise information. For the purpose of selecting a suitable career, the article analyzes student's features in terms of career, memory, interest, knowledge, environment and attitude. We propose two hybridized distance measures using Hausdorff, Hamming and Euclidian distances under picture fuzzy environment where the evaluating information regarding students, subjects and student's features are given in picture fuzzy numbers. Then we present an algorithmic approach using the proposed distance measures and rough set theory. We apply rough set theory to determine whether a particular subject is suitable for a student even if there is controversy to select a stream. Lower and higher approximation with boundary region of rough set theory is used to manage the inconsistent situations. Finally, two case studies are demonstrated to validate the applicability of the proposed idea.
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Roy, J., and A. Abdel-Dayem. "Segmentation of the Gastrointestinal Tract MRI Using Deep Learning." International Journal of Artificial Intelligence & Applications 14, no. 04 (July 27, 2023): 41–56. http://dx.doi.org/10.5121/ijaia.2023.14404.
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This paper proposes a deep learning-based model to segment gastrointestinal tract (GI) magnetic resonance images (MRI). The application of this model will be useful in potentially accelerating treatment times and possibly improve the quality of the treatments for the patients who must undergo radiation treatments in cancer centers. The proposed model employs the U-net architecture, which provides outstanding overall performance in medical image segmentation tasks. The model that was developed through this project has a score of 81.86% using a combination of the dice coefficient and the Hausdorff distance measures, rendering it highly accurate in segmenting and contouring organs in the gastrointestinal system.
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Gao, Chang, and Juliana Y. Leung. "Techniques for Fast Screening of 3D Heterogeneous Shale Barrier Configurations and Their Impacts on SAGD Chamber Development." SPE Journal 26, no. 04 (February 23, 2021): 2114–38. http://dx.doi.org/10.2118/199906-pa.
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Summary The steam-assisted gravity drainage (SAGD) recovery process is strongly impacted by the spatial distributions of heterogeneous shale barriers. Though detailed compositional flow simulators are available for SAGD recovery performance evaluation, the simulation process is usually quite computationally demanding, rendering their use over a large number of reservoir models for assessing the impacts of heterogeneity (uncertainties) to be impractical. In recent years, data-driven proxies have been widely proposed to reduce the computational effort; nevertheless, the proxy must be trained using a large data set consisting of many flow simulation cases that are ideally spanning the model parameter spaces. The question remains: is there a more efficient way to screen a large number of heterogeneous SAGD models? Such techniques could help to construct a training data set with less redundancy; they can also be used to quickly identify a subset of heterogeneous models for detailed flow simulation. In this work, we formulated two particular distance measures, flow-based and static-based, to quantify the similarity among a set of 3D heterogeneous SAGD models. First, to formulate the flow-based distance measure, a physics-based particle-tracking model is used: Darcy’s law and energy balance are integrated to mimic the steam chamber expansion process; steam particles that are located at the edge of the chamber would release their energy to the surrounding cold bitumen, while detailed fluid displacements are not explicitly simulated. The steam chamber evolution is modeled, and a flow-based distance between two given reservoir models is defined as the difference in their chamber sizes over time. Second, to formulate the static-based distance, the Hausdorff distance (Hausdorff 1914) is used: it is often used in image processing to compare two images according to their corresponding spatial arrangement and shapes of various objects. A suite of 3D models is constructed using representative petrophysical properties and operating constraints extracted from several pads in Suncor Energy’s Firebag project. The computed distance measures are used to partition the models into different groups. To establish a baseline for comparison, flow simulations are performed on these models to predict the actual chamber evolution and production profiles. The grouping results according to the proposed flow- and static-based distance measures match reasonably well to those obtained from detailed flow simulations. Significant improvement in computational efficiency is achieved with the proposed techniques. They can be used to efficiently screen a large number of reservoir models and facilitate the clustering of these models into groups with distinct shale heterogeneity characteristics. It presents a significant potential to be integrated with other data-driven approaches for reducing the computational load typically associated with detailed flow simulations involving multiple heterogeneous reservoir realizations.
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du Moulin, W., M. Bourne, L. Diamond, J. Konrath, C. Vertullo, D. Lloyd, and D. Saxby. "SHAPE DIFFERENCES IN THE SEMITENDINOSUS FOLLOWING TENDON HARVESTING FOR ANTERIOR CRUCIATE LIGAMENT RECONSTRUCTION." Orthopaedic Proceedings 105-B, SUPP_8 (April 11, 2023): 135. http://dx.doi.org/10.1302/1358-992x.2023.8.135.
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Following anterior cruciate ligament reconstruction (ACLR) using a semitendinosus (ST) autograft measures such as length, cross-sectional area, and volume may not fully describe the effects of tendon harvest on muscle morphology as these discrete measures cannot characterize three-dimensional muscle shape. This study aimed to determine between-limb ST shape similarity and regional morphology in individuals with a unilateral history of ACLR using a ST graft, and healthy controls.A secondary analysis of magnetic resonance imaging was undertaken from 18 individuals with unilateral history of ST ACLR and 18 healthy controls. ST muscles were manually segmented, and shape similarity were assessed between limbs and groups using Jaccard index (0-1) and Hausdorff distance (mm). ST length (cm), peak cross-sectional area (CSA) (cm2), and volume (cm3) was compared between surgically reconstructed and uninjured contralateral limbs, and between the left and right limbs of control participants with no history of injury. Cohen's d was reported as a measure of effect size.Compared to healthy controls, the ACLR group had significantly (p<0.001, d= −2.33) lower bilateral ST shape similarity. Furthermore, the deviation in muscle shape was significantly (p<0.001, d= 2.12) greater in the ACLR group. Within the ACLR group, maximum Hausdorff distance indicated ST from the ACLR limb deviated (23.1±8.68 mm) from the shape of the healthy contralateral ST, this was observed particularly within the distal region of the muscle. Compared to the uninjured contralateral limb and healthy controls, deficits in peak cross-sectional area and volume in ACLR group were largest in proximal (p<0.001, d= −2.52 to −1.28) and middle (p<0.001, d= −1.81 to −1.04) regions.Findings highlight morphological features in distal ST not identified by traditional discrete morphology measures. ST shape was most different in the distal region of the muscle, despite deficits in CSA and volume being most pronounced in proximal and middle regions. ST shape following ACLR may affect force transmission and distribution within the hamstrings and contribute to persistent deficits in knee flexor and internal rotator strength.
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Yang, Miin-Shen, and Zahid Hussain. "Distance and similarity measures of hesitant fuzzy sets based on Hausdorff metric with applications to multi-criteria decision making and clustering." Soft Computing 23, no. 14 (May 23, 2018): 5835–48. http://dx.doi.org/10.1007/s00500-018-3248-0.
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Yang, Miin-Shen, Zahid Hussain, and Mehboob Ali. "Belief and Plausibility Measures on Intuitionistic Fuzzy Sets with Construction of Belief-Plausibility TOPSIS." Complexity 2020 (August 12, 2020): 1–12. http://dx.doi.org/10.1155/2020/7849686.
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Belief and plausibility measures in Dempster–Shafer theory (DST) and fuzzy sets are known as different approaches for representing partial, uncertainty, and imprecise information. There are several generalizations of DST to fuzzy sets proposed in the literature. But, less generalization of DST to intuitionistic fuzzy sets (IFSs), that can somehow present imprecise information better than fuzzy sets, was proposed. In this paper, we first propose a simple and intuitive way to construct a generalization of DST to IFSs with degrees of belief and plausibility in terms of degrees of membership and nonmembership, respectively. We then give belief and plausibility measures on IFSs and construct belief-plausibility intervals (BPIs) of IFSs. Based on the constructed BPIs, we first use Hausdorff metric to define the distance between two BPIs and then establish similarity measures in the generalized context of DST to IFSs. By employing the techniques of ordered preference similarity to ideal solution (TOPSIS), the proposed belief and plausibility measures on IFSs in the framework of DST enable us to construct a belief-plausibility TOPSIS for solving multicriteria decision-making problems. Some examples are presented to manifest that the proposed method is reasonable, applicable, and well suited in the environment of IFSs in the framework of generalization of DST.
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MOHIDEEN, ABUBACKER KAJA, and KUTTIANNAN THANGAVEL. "REGION-BASED CONTRAST ENHANCEMENT OF DIGITAL MAMMOGRAMS USING AN IMPROVED WATERSHED SEGMENTATION." International Journal of Image and Graphics 13, no. 01 (January 2013): 1350007. http://dx.doi.org/10.1142/s0219467813500071.
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A simple edge-based preprocessing scheme is proposed in this paper for contrast enhancement of digital mammogram images while preserving the edges more accurately. This proposed method has three steps: (i) initially the breast region is segmented from the mammogram images by removing the film artifacts, (ii) the pectoral muscle region is identified and excluded from the breast region using a novel adaptive thresholding method, and (iii) an Improved Watershed Segmentation (IWS) is applied to segment the breast profile, and each region is enhanced with simple histogram equalization. The segmentation is performed in order to achieve adaptive contrast enhancement. The performance of this proposed pectoral removal method is analyzed with two measures: Hausdorff Distance (HD) and Mean of Absolute Error Distance (MAED), and the proposed contrast enhancement approach is been analyzed with the five diverse parameters along with the classification accuracy. The experiments and results show the potential performance of our proposed algorithm over the existing approaches with optimum results on all the performance measure and the classification performance is been evaluated with a hybrid neural network, our proposed method proves the better performance with the achievement of 92% accuracy.
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Novák, Jiří, David Hoksza, Tomáš Skopal, and Oliver Kohlbacher. "On Comparison of SimTandem with State-of-the-Art Peptide Identification Tools, Efficiency of Precursor Mass Filter and Dealing with Variable Modifications." Journal of Integrative Bioinformatics 10, no. 3 (December 1, 2013): 1–15. http://dx.doi.org/10.1515/jib-2013-228.
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Summary The similarity search in theoretical mass spectra generated from protein sequence databases is a widely accepted approach for identification of peptides from query mass spectra produced by shotgun proteomics. Growing protein sequence databases and noisy query spectra demand database indexing techniques and better similarity measures for the comparison of theoretical spectra against query spectra. We employ a modification of previously proposed parameterized Hausdorff distance for comparisons of mass spectra. The new distance outperforms the original distance, the angle distance and state-of-the-art peptide identification tools OMSSA and X!Tandem in the number of identified peptides even though the q-value is only 0.001. When a precursor mass filter is used as a database indexing technique, our method outperforms OMSSA in the speed of search. When variable modifications are not searched, the search time is similar to X!Tandem. We show that the precursor mass filter is an efficient database indexing technique for high-accuracy data even though many variable modifications are being searched. We demonstrate that the number of identified peptides is bigger when variable modifications are searched separately by more search runs of a peptide identification engine. Otherwise, the false discovery rates are affected by mixing unmodified and modified spectra together resulting in a lower number of identified peptides. Our method is implemented in the freely available application SimTandem which can be used in the framework TOPP based on OpenMS.
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Maman, S. Ghanbari, A. Shalbaf, H. Behnam, Z. Alizadeh Sani, and M. Shojaei Fard. "FULLY AUTOMATIC SEGMENTATION OF LEFT VENTRICLE IN A SEQUENCE OF ECHOCARDIOGRAPHY IMAGES OF ONE CARDIAC CYCLE BY DYNAMIC DIRECTIONAL VECTOR FIELD CONVOLUTION (DDVFC) METHOD AND MANIFOLD LEARNING." Biomedical Engineering: Applications, Basis and Communications 25, no. 02 (April 2013): 1350022. http://dx.doi.org/10.4015/s1016237213500221.
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In this paper, an automatic method for segmentation of the left ventricle in two-dimensional (2D) echocardiography images of one cardiac cycle is proposed. In the first step of this method, using a mean image of a sequence of echocardiography images and its statistical properties the approximate region of left ventricle (LV) is extracted. Then the coordinate of extracted rectangular (ROI) is applied on all frames of sequences automatically. The mean image extracted ROI is used for defining the initial contour by scanning from the center point in polar coordinate. In the next step, all the extracted ROIs from the frames are mapped in a 2D space using the nonlinear dimension reduction manifold learning method. Using the properties of the manifold map end diastole (ED) and end systole (ES) frames are determined. Segmentation of the frames begins from ES frame, utilizing the dynamic directional vector field convolution (DDVFC) level set method with the initial contour as mentioned above. Final contour of each segmented frame is used as the initial contour of the next frame. Maximum range of the active contour motion is limited by a percent of the Euclidean distance between the point corresponds the current frame and the previous one in the resultant manifold. The results obtained from our method are quantitatively evaluated to those obtained by the gold contours drawn by a cardiologist on 489 echocardiographic images of seven volunteers using four distance measures: Hausdorff distance, average distance, area difference and area coverage error. We have also compared our results with the results of applying only DDVFC method. Comparing the implementation of only the DDVFC method, the results show final contours by proposed method are more close to contours drawn by a cardiologist.
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Vania, Malinda, and Deukhee Lee. "Intervertebral disc instance segmentation using a multistage optimization mask-RCNN (MOM-RCNN)." Journal of Computational Design and Engineering 8, no. 4 (June 18, 2021): 1023–36. http://dx.doi.org/10.1093/jcde/qwab030.
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Abstract Lower back pain is one of the major global challenges in health problems. Medical imaging is rapidly taking a predominant position for the diagnosis and treatment of lower back abnormalities. Magnetic resonance imaging (MRI) is a primary tool for detecting anatomical and functional abnormalities in the intervertebral disc (IVD) and provides valuable data for both diagnosis and research. Deep learning methods perform well in computer visioning when labeled general image training data are abundant. In the practice of medical images, the labeled data or the segmentation data are produced manually. However, manual medical image segmentation leads to two main issues: much time is needed for delineation, and reproducibility is called into question. To handle this problem, we developed an automated approach for IVD instance segmentation that can utilize T1 and T2 images during this study to handle data limitation problems and computational time problems and improve the generalization of the algorithm. This method builds upon mask-RCNN; we proposed a multistage optimization mask-RCNN (MOM-RCNN) for deep learning segmentation networks. We used a multi-optimization training system by utilizing stochastic gradient descent and adaptive moment estimation (Adam) with T1 and T2 in MOM-RCNN. The proposed method showed a significant improvement in processing time and segmentation results compared to previous commonly used segmentation methods. We evaluated the results using several different key performance measures. We obtain the Dice coefficient (99%). Our method can define the IVD’s segmentation as much as 88% (sensitivity) and recognize the non-IVD as much as 98% (specificity). The results also obtained increasing precision (92%) with a low global consistency error (0.03), approaching 0 (the best possible score). On the spatial distance measures, the results show a promising reduction from 0.407 ± 0.067 mm in root mean square error to 0.095 ± 0.026 mm, Hausdorff distance from 12.313 ± 3.015 to 5.155 ± 1.561 mm, and average symmetric surface distance from 1.944 ± 0.850 to 0.49 ± 0.23 mm compared to other state-of-the-art methods. We used MRI images from 263 patients to demonstrate the efficiency of our proposed method.
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Henry, Christopher, and James F. Peters. "Arthritic Hand-Finger Movement Similarity Measurements: Tolerance Near Set Approach." Computational and Mathematical Methods in Medicine 2011 (2011): 1–14. http://dx.doi.org/10.1155/2011/569898.
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The problem considered in this paper is how to measure the degree of resemblance between nonarthritic and arthritic hand movements during rehabilitation exercise. The solution to this problem stems from recent work on a tolerance space view of digital images and the introduction of image resemblance measures. The motivation for this work is both to quantify and to visualize differences between hand-finger movements in an effort to provide clinicians and physicians with indications of the efficacy of the prescribed rehabilitation exercise. The more recent introduction of tolerance near sets has led to a useful approach for measuring the similarity of sets of objects and their application to the problem of classifying image sequences extracted from videos showing finger-hand movement during rehabilitation exercise. The approach to measuring the resemblance between hand movement images introduced in this paper is based on an application of the well-known Hausdorff distance measure and a tolerance nearness measure. The contribution of this paper is an approach to measuring as well as visualizing the degree of separation between images in arthritic and nonarthritic hand-finger motion videos captured during rehabilitation exercise.
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Olveres, Jimena, Erik Carbajal-Degante, Boris Escalante-Ramírez, Enrique Vallejo, and Carla María García-Moreno. "Deformable Models for Segmentation Based on Local Analysis." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/1646720.
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Segmentation tasks in medical imaging represent an exhaustive challenge for scientists since the image acquisition nature yields issues that hamper the correct reconstruction and visualization processes. Depending on the specific image modality, we have to consider limitations such as the presence of noise, vanished edges, or high intensity differences, known, in most cases, as inhomogeneities. New algorithms in segmentation are required to provide a better performance. This paper presents a new unified approach to improve traditional segmentation methods as Active Shape Models and Chan-Vese model based on level set. The approach introduces a combination of local analysis implementations with classic segmentation algorithms that incorporates local texture information given by the Hermite transform and Local Binary Patterns. The mixture of both region-based methods and local descriptors highlights relevant regions by considering extra information which is helpful to delimit structures. We performed segmentation experiments on 2D images including midbrain in Magnetic Resonance Imaging and heart’s left ventricle endocardium in Computed Tomography. Quantitative evaluation was obtained with Dice coefficient and Hausdorff distance measures. Results display a substantial advantage over the original methods when we include our characterization schemes. We propose further research validation on different organ structures with promising results.
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Zhang, Dinghuang, Baoli Lu, Jing Guo, Yu He, and Honghai Liu. "Assessment of Visual Motor Integration via Hand-Drawn Imitation: A Pilot Study." Electronics 12, no. 13 (June 22, 2023): 2776. http://dx.doi.org/10.3390/electronics12132776.
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Copious evidence shows that impaired visual–motor integration (VMI) is intrinsically linked to the core deficits of autism spectrum disorder (ASD) and associated with an anomalous social capability. Therefore, an effective evaluation method of visual–motor behaviour can provide meaningful insight into the evaluation of VMI towards social capability. The current pilot study aims to explore the appropriate quantified metrics for evaluating VMI ability based on a hand-drawn imitation protocol. First, a simple and interesting hand-drawn protocol was designed, and six healthy participants were recruited to perform the task. Then, based on the collected hand–eye behaviour data, several metrics were applied to infer the participant’s social capability and VMI in engagement and visual–motor complexity based on hand–eye properties with Hausdorff distance and cross-recurrence quantification analysis (CRQA). Finally, those quantified metrics were verified through statistical significance. This study proposed a set of quantitative metrics to construct a comprehensive VMI evaluation, including outcome and progress measures. The results revealed the proposed method as a directly interpretable indicator providing a promising computational framework and biomarker for VMI evaluation, paving the way for its future use in ASD diagnosis and guiding intervention.
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Mauricaite, Radvile, Ella Mi, Jiarong Chen, Andrew Ho, Lillie Pakzad-Shahabi, and Matthew Williams. "Fully automated deep learning system for detecting sarcopenia on brain MRI in glioblastoma." Neuro-Oncology 23, Supplement_4 (October 1, 2021): iv13. http://dx.doi.org/10.1093/neuonc/noab195.031.
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Abstract Aims Glioblastoma multiforme (GBM) is an aggressive brain malignancy. Performance status is an important prognostic factor but is subjectively evaluated, resulting in inaccuracy. Objective markers of frailty/physical condition, such as measures of skeletal muscle mass can be evaluated on cross-sectional imaging and is associated with cancer survival. In GBM, temporalis muscle has been identified as a skeletal muscle mass surrogate and a prognostic factor. However, current manual muscle quantification is time consuming, limiting clinical adoption. We previously developed a deep learning system for automated temporalis muscle quantification, with high accuracy (Dice coefficient 0.912), and showed muscle cross-sectional area is independently significantly associated with survival in GBM (HR 0.380). However, it required manual selection of the temporalis muscle-containing MRI slice. Thus, in this work we aimed to develop a fully automatic deep-learning system, using the eyeball as an anatomic landmark for automatic slice selection, to quantify temporalis and validate on independent datasets. Method 3D brain MRI scans were obtained from four datasets: our in-house glioblastoma patient dataset, TCGA-GBM, IVY-GAP and REMBRANDT. Manual eyeball and temporalis segmentations were performed on 2D MRI images by two experienced readers. Two neural networks (2D U-Nets) were trained, one to automatically segment the eyeball and the other to segment the temporalis muscle on 2D MRI images using Dice loss function. The cross sectional area of eyeball segmentations were quantified and thresholded, to select the superior orbital MRI slice from each scan. This slice underwent temporalis segmentation, whose cross sectional area was then quantified. Accuracy of automatically predicted eyeball and temporalis segmentations were compared to manual ground truth segmentations on metrics of Dice coefficient, precision, recall and Hausdorff distance. Accuracy of MRI slice selection (by the eyeball segmentation model) for temporalis segmentation was determined by comparing automatically selected slices to slices selected manually by a trained neuro-oncologist. Results 398 images from 185 patients and 366 images from 145 patients were used for the eyeball and temporalis segmentation models, respectively. 61 independent TCGA-GBM scans formed a validation cohort to assess the performance of the full pipeline. The model achieved high accuracy in eyeball segmentation, with test set Dice coefficient of 0.9029 ± 0.0894, precision of 0.8842 ± 0.0992, recall of 0.9297 ± 0.6020 and Hausdorff distance of 2.8847 ± 0.6020. High segmentation accuracy was also achieved by the temporalis segmentation model, with Dice coefficient of 0.8968 ± 0.0375, precision of 0.8877 ± 0.0679, recall of 0.9118 ± 0.0505 and Hausdorff distance of 1.8232 ± 0.3263 in the test set. 96.1% of automatically selected slices for temporalis segmentation were within 2 slices of the manually selected slice. Conclusion Temporalis muscle cross-sectional area can be rapidly and accurately assessed from 3D MRI brain scans using a deep learning-based system in a fully automated pipeline. Combined with our and others’ previous results that demonstrate the prognostic significance of temporalis cross-sectional area and muscle width, our findings suggest a role for deep learning in muscle mass and sarcopenia screening in GBM, with the potential to add significant value to routine imaging. Possible clinical applications include risk profiling, treatment stratification and informing interventions for muscle preservation. Further work will be to validate the prognostic value of temporalis muscle cross sectional area measurements generated by our fully automatic deep learning system in the multiple in-house and external datasets.
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Sreelakshmy, R., Anita Titus, N. Sasirekha, E. Logashanmugam, R. Benazir Begam, G. Ramkumar, and Raja Raju. "An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images." BioMed Research International 2022 (June 16, 2022): 1–13. http://dx.doi.org/10.1155/2022/8342767.
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Cerebellum measures taken from routinely obtained ultrasound (US) images have been frequently employed to determine gestational age and identify developing central nervous system’s anatomical abnormalities. Standardized cerebellar assessments from large-scale clinical datasets are required to investigate correlations between the growing cerebellum and postnatal neurodevelopmental results. These studies could uncover structural abnormalities that could be employed as indicators to forecast neurodevelopmental and growth consequences. To achieve this, higher-throughput, precise, and impartial measures must be used to replace the existing human, semiautomatic, and advanced algorithms, which seem to be time-consuming and inaccurate. In this article, we presented an innovative deep learning (DL) technique for automatic fetal cerebellum segmentation from 2-dimensional (2D) US brain images. We present ReU-Net, a semantic segmentation network tailored to the anatomy of the fetal cerebellum. Moreover, we use U-Net as a foundation models with the incorporation of residual blocks and Wiener filter over the last 2 layers to segregate the cerebellum (c) from the noisy US data. 590 images for training and 150 images for testing were taken; also, we employed a 5-fold cross-assessment method. Our ReU-Net scored 91%, 92%, 25.42, 98%, 92%, and 94% for Dice Score Coefficient (DSC), F1-score, Hausdorff Distance (HD), accuracy, recall, and precision, correspondingly. The suggested method outperforms the other U-Net predicated techniques by a quantitatively significant margin ( p 0.001 ). Our presented approach can be used to allow high bandwidth imaging techniques in medical study fetal US images as well as biometric evaluation on a broader scale in fetal US images.
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Biedrzycki, Adam H., Hannah C. Kistler, Erik E. Perez-Jimenez, and Alison J. Morton. "Use of Hausdorff Distance and Computer Modelling to Evaluate Virtual Surgical Plans with Three-Dimensional Printed Guides against Freehand Techniques for Navicular Bone Repair in Equine Orthopaedics." Veterinary and Comparative Orthopaedics and Traumatology 34, no. 01 (January 2021): 009–16. http://dx.doi.org/10.1055/s-0040-1721846.
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AbstractObjective The aim of this study was to evaluate the surgical execution of a virtual surgical plan (VSP) with three-dimensional (3D) guides against a freehand approach in the equine navicular bone using an automated in silico computer analysis technique.Study Design Eight pairs of cadaveric forelimb specimens of adult horses were used in an ex vivo experimental study design with in silico modelling. Limbs received either a 3.5 mm cortical screw according to a VSP or using an aiming device. Using computed tomography and computer segmentation, a comparison was made between the executed screw and the planned screw using the Hausdorff distance (HD).Results Navicular bone mean HD registration error was –0.06 ± 0.29 mm. The VSP with 3D printing demonstrated significantly superior accuracy with a mean deviation of 1.19 ± 0.42 mm compared with aiming device group (3.53 ± 1.24 mm, p = 0.0018). The VSP group was 5.0 times more likely to result in a mean aberration of less than 1.0 mm (95% confidence interval, 0.62–33.4). A 3.5 mm screw with an optimal entry point can have a maximum deviation angle of 3.23 ± 0.07, 2.70 ± 0.06 and 2.37 ± 0.10 degrees in a proximal, dorsal and palmar direction respectively, prior to violating one of the cortical surfaces.Conclusion Procedures performed using the 3D guides have a high degree of accuracy, with minimal mean deviations (<1 mm and <1 degree) of a VSP compared with those using the conventional aiming device. The use of VSP and the HD for evaluation of orthopaedic surgeries and outcome measures shows promise for simplifying and improving surgical accuracy.
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Golkar, Ehsan, Hossein Rabbani, and Ashrani Aizzuddin Abd. Rahni. "Inter-subject Registration-based Segmentation of Thoracic-Abdominal Organs in 4 Dimensional Magnetic Resonance Imaging." Jurnal Kejuruteraan 33, no. 4 (November 30, 2021): 1045–51. http://dx.doi.org/10.17576/jkukm-2021-33(4)-26.
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4 Dimensional Magnetic Resonance Imaging (4D MRI) is currently gaining attention as an imaging modality which is able to capture inter-cycle variability of respiratory motion. Such information is beneficial for example in radiotherapy planning and delivery. In the latter case, there may be a need for organ segmentation, however 4D MRI are of low contrast, which complicates automated organ segmentation. This paper proposes a multi-subject thoracic-abdominal organ segmentation propagation scheme for 4D MRI. The proposed scheme is registration based, hence different combinations of deformation and similarity measures are used. For deformation we used either just an affine transformation or additionally free form deformation on top of an affine transform. For similarity measure, either the sum of squared intensity differences or normalised mutual information is used. Segmentations from multiple subjects are registered to a target MRI and the average segmentation is found. The result of the method is compared with the ground truth which is generated from a semi-automated segmentation method. The results are quantified using the Jaccard index and Hausdorff distance. The results show that using free form deformation with a sum of squared intensity differences similarity measure produces an acceptable segmentation of the organs with an overall Jaccard index of over 0.5. Hence, the proposed scheme can be used as a basis for automated organ segmentation in 4D MRI.
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P, Vivek E., and N. Sudha. "Robust Hausdorff distance measure for face recognition." Pattern Recognition 40, no. 2 (February 2007): 431–42. http://dx.doi.org/10.1016/j.patcog.2006.04.019.
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Qiang, He Qun, Chun Hua Qian, and Sheng Rong Gong. "Similarity Measure for Image Retrieval Based on Hausdorff Distance." Applied Mechanics and Materials 635-637 (September 2014): 1039–44. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1039.
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In general, it is difficult to segment accurately image regions or boundary contours and represent them by feature vectors for shape-based image query. Therefore, the object similarity is often computed by their boundaries. Hausdorff distance is nonlinear for computing distance, it can be used to measure the similarity between two patterns of points of edge images. Classical Hausdorff measure need to express image as a feature matrix firstly, then calculate feature values or feature vectors, so it is time-consuming. Otherwise, it is difficult for part pattern matching when shadow and noise existed. In this paper, an algorithm that use Hausdorff distance on the image boundaries to measure similarity is proposed. Experimental result has showed that the proposed algorithm is robust.
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Vivek, E. P., and N. Sudha. "Gray Hausdorff Distance Measure for Comparing Face Images." IEEE Transactions on Information Forensics and Security 1, no. 3 (September 2006): 342–49. http://dx.doi.org/10.1109/tifs.2006.879294.
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Alvi, Hafiz Muhammad Usama Hassan, Muhammad Shahid Farid, Muhammad Hassan Khan, and Marcin Grzegorzek. "Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation." Applied Sciences 11, no. 6 (March 17, 2021): 2666. http://dx.doi.org/10.3390/app11062666.
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Emerging 3D-related technologies such as augmented reality, virtual reality, mixed reality, and stereoscopy have gained remarkable growth due to their numerous applications in the entertainment, gaming, and electromedical industries. In particular, the 3D television (3DTV) and free-viewpoint television (FTV) enhance viewers’ television experience by providing immersion. They need an infinite number of views to provide a full parallax to the viewer, which is not practical due to various financial and technological constraints. Therefore, novel 3D views are generated from a set of available views and their depth maps using depth-image-based rendering (DIBR) techniques. The quality of a DIBR-synthesized image may be compromised for several reasons, e.g., inaccurate depth estimation. Since depth is important in this application, inaccuracies in depth maps lead to different textural and structural distortions that degrade the quality of the generated image and result in a poor quality of experience (QoE). Therefore, quality assessment DIBR-generated images are essential to guarantee an appreciative QoE. This paper aims at estimating the quality of DIBR-synthesized images and proposes a novel 3D objective image quality metric. The proposed algorithm aims to measure both textural and structural distortions in the DIBR image by exploiting the contrast sensitivity and the Hausdorff distance, respectively. The two measures are combined to estimate an overall quality score. The experimental evaluations performed on the benchmark MCL-3D dataset show that the proposed metric is reliable and accurate, and performs better than existing 2D and 3D quality assessment metrics.
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Garlapati, Revanth Reddy, Aditi Roy, Grand Roman Joldes, Adam Wittek, Ahmed Mostayed, Barry Doyle, Simon Keith Warfield, et al. "More accurate neuronavigation data provided by biomechanical modeling instead of rigid registration." Journal of Neurosurgery 120, no. 6 (June 2014): 1477–83. http://dx.doi.org/10.3171/2013.12.jns131165.
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It is possible to improve neuronavigation during image-guided surgery by warping the high-quality preoperative brain images so that they correspond with the current intraoperative configuration of the brain. In this paper, the accuracy of registration results obtained using comprehensive biomechanical models is compared with the accuracy of rigid registration, the technology currently available to patients. This comparison allows investigation into whether biomechanical modeling provides good-quality image data for neuronavigation for a larger proportion of patients than rigid registration. Preoperative images for 33 neurosurgery cases were warped onto their respective intraoperative configurations using both the biomechanics-based method and rigid registration. The Hausdorff distance–based evaluation process, which measures the difference between images, was used to quantify the performance of both registration methods. A statistical test for difference in proportions was conducted to evaluate the null hypothesis that the proportion of patients for whom improved neuronavigation can be achieved is the same for rigid and biomechanics-based registration. The null hypothesis was confidently rejected (p < 10−4). Even the modified hypothesis that fewer than 25% of patients would benefit from the use of biomechanics-based registration was rejected at a significance level of 5% (p = 0.02). The biomechanics-based method proved particularly effective in cases demonstrating large craniotomy-induced brain deformations. The outcome of this analysis suggests that nonlinear biomechanics-based methods are beneficial to a large proportion of patients and can be considered for use in the operating theater as a possible means of improving neuronavigation and surgical outcomes.
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Baudrier, E., G. Millon, F. Nicolier, R. Seulin, and S. Ruan. "Hausdorff distance-based multiresolution maps applied to image similarity measure." Imaging Science Journal 55, no. 3 (September 2007): 164–74. http://dx.doi.org/10.1179/174313107x166884.
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Hasselblatt, Boris. "A new construction of the Margulis measure for Anosov flows." Ergodic Theory and Dynamical Systems 9, no. 3 (September 1989): 465–68. http://dx.doi.org/10.1017/s0143385700005101.
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Chen, Wufan. "Feature-based nonrigid image registration using a Hausdorff distance matching measure." Optical Engineering 46, no. 5 (May 1, 2007): 057201. http://dx.doi.org/10.1117/1.2737438.
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Zou, Yaobin, Fangmin Dong, Bangjun Lei, Lulu Fang, and Shuifa Sun. "Image thresholding based on template matching with arctangent Hausdorff distance measure." Optics and Lasers in Engineering 51, no. 5 (May 2013): 600–609. http://dx.doi.org/10.1016/j.optlaseng.2012.12.016.
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QU Sheng-jie, 曲圣杰, 潘泉 PAN Quan, 程咏梅 CHENG Yong-mei, 赵春晖 ZHAO Chun-hui, and 凌志刚 LING Zhi-gang. "A Weighted Hausdorff Distance Algorithm Based on Multi-scale Edge Measure Fusion." ACTA PHOTONICA SINICA 40, no. 10 (2011): 1560–65. http://dx.doi.org/10.3788/gzxb20114010.1560.
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