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Mora G, Martín-Landrove M. Use of Zernike moments to characterize dose conformity for radiotherapy treatment plans. Appl Radiat Isot 2024; 209:111322. [PMID: 38642442 DOI: 10.1016/j.apradiso.2024.111322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 02/25/2024] [Accepted: 04/09/2024] [Indexed: 04/22/2024]
Abstract
Dose conformity is an essential parameter used in radiotherapy and radiosurgery that measures the correspondence of the dose distribution derived from a Treatment Planning System (TPS) with the actual volume to be treated, the Planning Treatment Volume (PTV). The present work uses a method based on the expansion of dose distributions and PTVs by three-dimensional Zernike polynomials and further comparison of their moments to define a general criterion of dose conformity. To carry on this study, data coming from 20 patients comprising 80 datasets exported from the TPS, which included imaging data (PTVs) and dose distributions corresponding to different treatment modalities: three-dimensional conformal radiotherapy, intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT), were used. The expansions in Zernike polynomials were obtained up to order 6 and reconstructed dose distributions and PTVs were obtained and compared, and several definitions for a general dose conformity index were proposed. Results indicate agreement between the proposed dose conformity index and the Conformation Number CN. The proposed method allows for a systematic approach to the analysis of dose distributions with further extensions in AI applications.
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Affiliation(s)
- Guido Mora
- Instituto Venezolano de Investigaciones Científicas, IVIC, Altos de Pipe, Venezuela
| | - Miguel Martín-Landrove
- Centre for Molecular and Medical Physics, Physics Department, Faculty of Science, Universidad Central de Venezuela, Caracas, Venezuela; Centre for Medical Visualization, National Institute for Bioengineering, INABIO, Universidad Central de Venezuela, Caracas, Venezuela; Centro de Diagnóstico Docente Las Mercedes, Caracas, Venezuela.
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2
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Banach M. Structural Outlier Detection and Zernike-Canterakis Moments for Molecular Surface Meshes-Fast Implementation in Python. Molecules 2023; 29:52. [PMID: 38202635 PMCID: PMC10779519 DOI: 10.3390/molecules29010052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/06/2023] [Accepted: 12/12/2023] [Indexed: 01/12/2024] Open
Abstract
Object retrieval systems measure the degree of similarity of the shape of 3D models. They search for the elements of the 3D model databases that resemble the query model. In structural bioinformatics, the query model is a protein tertiary/quaternary structure and the objective is to find similarly shaped molecules in the Protein Data Bank. With the ever-growing size of the PDB, a direct atomic coordinate comparison with all its members is impractical. To overcome this problem, the shape of the molecules can be encoded by fixed-length feature vectors. The distance of a protein to the entire PDB can be measured in this low-dimensional domain in linear time. The state-of-the-art approaches utilize Zernike-Canterakis moments for the shape encoding and supply the retrieval process with geometric data of the input structures. The BioZernike descriptors are a standard utility of the PDB since 2020. However, when trying to calculate the ZC moments locally, the issue of the deficiency of libraries readily available for use in custom programs (i.e., without relying on external binaries) is encountered, in particular programs written in Python. Here, a fast and well-documented Python implementation of the Pozo-Koehl algorithm is presented. In contrast to the more popular algorithm by Novotni and Klein, which is based on the voxelized volume, the PK algorithm produces ZC moments directly from the triangular surface meshes of 3D models. In particular, it can accept the molecular surfaces of proteins as its input. In the presented PK-Zernike library, owing to Numba's just-in-time compilation, a mesh with 50,000 facets is processed by a single thread in a second at the moment order 20. Since this is the first time the PK algorithm is used in structural bioinformatics, it is employed in a novel, simple, but efficient protein structure retrieval pipeline. The elimination of the outlying chain fragments via a fast PCA-based subroutine improves the discrimination ability, allowing for this pipeline to achieve an 0.961 area under the ROC curve in the BioZernike validation suite (0.997 for the assemblies). The correlation between the results of the proposed approach and of the 3D Surfer program attains values up to 0.99.
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Affiliation(s)
- Mateusz Banach
- Department of Bioinformatics and Telemedicine, Faculty of Medicine, Jagiellonian University Medical College, Medyczna 7, 30-688 Kraków, Poland
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3
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Chen Y, Chen T, Duan W, Liu J, Si Y, Dong Z. Rapid measurement of brown tide algae using Zernike moments and ensemble learning based on excitation-emission matrix fluorescence. Spectrochim Acta A Mol Biomol Spectrosc 2023; 294:122547. [PMID: 36870184 DOI: 10.1016/j.saa.2023.122547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 01/27/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Accurate real-time prediction of microalgae density has great practical significance for taking countermeasures before the advent of Harmful algal blooms (HABs), and the non-destructive and sensitive property of excitation-emission matrix fluorescence (EEMF) spectroscopy makes it applicable to online monitoring and control. In this study, an efficient image preprocessing algorithm based on Zernike moments (ZMs) was proposed to extract compelling features from EEM intensities images. The determination of the highest order of ZMs considered both reconstruction error and computational cost, then the optimal subset of preliminarily extracted 36 ZMs was screened via the BorutaShap algorithm. Aureococcus anophagefferens concentration prediction models were developed by combining BorutaShap and ensemble learning models (random forest (RF), gradient boosting decision tree (GBDT), and XGBoost). The experimental results show that BorutaShap_GBDT preserved the superior subset of ZMs, and the integration of BorutaShap_GBDT and XGBoost achieved the highest prediction accuracy. This research provides a new and promising strategy for rapidly measuring microalgae cell density.
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Affiliation(s)
- Ying Chen
- Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China.
| | - Ting Chen
- Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Weiliang Duan
- Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Junfei Liu
- Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Yu Si
- Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
| | - Zhiyang Dong
- Hebei Province Key Laboratory of Test/Measurement Technology and Instrument, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
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Tang R, Chen W, Wu Y, Xiong H, Yan B. A Comparative Study of Structural Deformation Test Based on Edge Detection and Digital Image Correlation. Sensors (Basel) 2023; 23:3834. [PMID: 37112175 PMCID: PMC10146399 DOI: 10.3390/s23083834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/01/2023] [Accepted: 04/06/2023] [Indexed: 06/19/2023]
Abstract
Digital image-correlation (DIC) algorithms rely heavily on the accuracy of the initial values provided by whole-pixel search algorithms for structural displacement monitoring. When the measured displacement is too large or exceeds the search domain, the calculation time and memory consumption of the DIC algorithm will increase greatly, and even fail to obtain the correct result. The paper introduced two edge-detection algorithms, Canny and Zernike moments in digital image-processing (DIP) technology, to perform geometric fitting and sub-pixel positioning on the specific pattern target pasted on the measurement position, and to obtain the structural displacement according to the change of the target position before and after deformation. This paper compared the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests. The study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability. As the search domain of the DIC algorithm becomes larger, its calculation speed decreases sharply, and is obviously slower than the Canny and Zernike moment algorithms.
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Affiliation(s)
- Ruixiang Tang
- School of Civil Engineering, Hunan University, Changsha 410082, China
| | - Wenbing Chen
- School of Civil Engineering, Hunan University, Changsha 410082, China
| | - Yousong Wu
- School of Civil Engineering, Hunan University, Changsha 410082, China
| | - Hongbin Xiong
- School of Civil Engineering, Hunan University, Changsha 410082, China
| | - Banfu Yan
- School of Civil Engineering, Hunan University, Changsha 410082, China
- College of Civil and Architectural Engineering, Guangxi University, Nanning 530004, China
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Vankayalapati HD, Kuchibhotla S, Chadalavada MSK, Dargar SK, Anne KR, Kyandoghere K. A Novel Zernike Moment-Based Real-Time Head Pose and Gaze Estimation Framework for Accuracy-Sensitive Applications. Sensors (Basel) 2022; 22:8449. [PMID: 36366147 PMCID: PMC9658879 DOI: 10.3390/s22218449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/02/2022] [Accepted: 10/28/2022] [Indexed: 06/16/2023]
Abstract
A real-time head pose and gaze estimation (HPGE) algorithm has excellent potential for technological advancements either in human-machine or human-robot interactions. For example, in high-accuracy advent applications such as Driver's Assistance System (DAS), HPGE plays a crucial role in omitting accidents and road hazards. In this paper, the authors propose a new hybrid framework for improved estimation by combining both the appearance and geometric-based conventional methods to extract local and global features. Therefore, the Zernike moments algorithm has been prominent in extracting rotation, scale, and illumination invariant features. Later, conventional discriminant algorithms were used to classify the head poses and gaze direction. Furthermore, the experiments were performed on standard datasets and real-time images to analyze the accuracy of the proposed algorithm. As a result, the proposed framework has immediately estimated the range of direction changes under different illumination conditions. We obtained an accuracy of ~85%; the average response time was 21.52 and 7.483 ms for estimating head poses and gaze, respectively, independent of illumination, background, and occlusion. The proposed method is promising for future developments of a robust system that is invariant even to blurring conditions and thus reaching much more significant performance enhancement.
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Affiliation(s)
- Hima Deepthi Vankayalapati
- Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankovil 626126, India
| | - Swarna Kuchibhotla
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India
| | - Mohan Sai Kumar Chadalavada
- Department of Electronics and Communication Engineering, VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India
| | - Shashi Kant Dargar
- Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Krishnankovil 626126, India
| | - Koteswara Rao Anne
- Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankovil 626126, India
| | - Kyamakya Kyandoghere
- Institute for Smart Systems Technologies, University Klagenfurt, 9020 Klagenfurt am Wörthersee, Austria
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Bar O, Bibrzycki Ł, Niedźwiecki M, Piekarczyk M, Rzecki K, Sośnicki T, Stuglik S, Frontczak M, Homola P, Alvarez-Castillo DE, Andersen T, Tursunov A. Zernike Moment Based Classification of Cosmic Ray Candidate Hits from CMOS Sensors. Sensors (Basel) 2021; 21:s21227718. [PMID: 34833793 PMCID: PMC8618806 DOI: 10.3390/s21227718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/12/2021] [Accepted: 11/14/2021] [Indexed: 11/16/2022]
Abstract
Reliable tools for artefact rejection and signal classification are a must for cosmic ray detection experiments based on CMOS technology. In this paper, we analyse the fitness of several feature-based statistical classifiers for the classification of particle candidate hits in four categories: spots, tracks, worms and artefacts. We use Zernike moments of the image function as feature carriers and propose a preprocessing and denoising scheme to make the feature extraction more efficient. As opposed to convolution neural network classifiers, the feature-based classifiers allow for establishing a connection between features and geometrical properties of candidate hits. Apart from basic classifiers we also consider their ensemble extensions and find these extensions generally better performing than basic versions, with an average recognition accuracy of 88%.
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Affiliation(s)
- Olaf Bar
- Institute of Computer Science, Pedagogical University of Krakow, 30-084 Kraków, Poland
| | - Łukasz Bibrzycki
- Institute of Computer Science, Pedagogical University of Krakow, 30-084 Kraków, Poland
| | - Michał Niedźwiecki
- Department of Computer Science, Cracow University of Technology, 31-155 Kraków, Poland
| | - Marcin Piekarczyk
- Institute of Computer Science, Pedagogical University of Krakow, 30-084 Kraków, Poland
| | - Krzysztof Rzecki
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Kraków, Poland
| | - Tomasz Sośnicki
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Kraków, Poland
| | - Sławomir Stuglik
- Institute of Nuclear Physics, Polish Academy of Sciences, 31-342 Kraków, Poland
| | - Michał Frontczak
- Institute of Computer Science, Pedagogical University of Krakow, 30-084 Kraków, Poland
| | - Piotr Homola
- Institute of Nuclear Physics, Polish Academy of Sciences, 31-342 Kraków, Poland
| | | | | | - Arman Tursunov
- Institute of Physics, Silesian University in Opava, Bezručovo nám 13, 74601 Opava, Czech Republic
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Renshaw DT, Christian JA. Subpixel Localization of Isolated Edges and Streaks in Digital Images. J Imaging 2020; 6:jimaging6050033. [PMID: 34460735 PMCID: PMC8321028 DOI: 10.3390/jimaging6050033] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/06/2020] [Accepted: 05/14/2020] [Indexed: 11/16/2022] Open
Abstract
Many modern sensing systems rely on the accurate extraction of measurement data from digital images. The localization of edges and streaks in digital images is an important example of this type of measurement, with these techniques appearing in many image processing pipelines. Several approaches attempt to solve this problem at both the pixel level and subpixel level. While the subpixel methods are often necessary for applications requiring best-possible accuracy, they are often susceptible to noise, use iterative methods, or require pre-processing. This work investigates a unified framework for subpixel edge and streak localization using Zernike moments with ramp-based and wedge-based signal models. The method described here is found to outperform the current state-of-the-art for digital images with common signal-to-noise ratios. Performance is demonstrated on both synthetic and real images.
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Abstract
Visual memory is crucial to navigation in many animals, including insects. Here, we focus on the problem of visual homing, that is, using comparison of the view at a current location with a view stored at the home location to control movement towards home by a novel shortcut. Insects show several visual specializations that appear advantageous for this task, including almost panoramic field of view and ultraviolet light sensitivity, which enhances the salience of the skyline. We discuss several proposals for subsequent processing of the image to obtain the required motion information, focusing on how each might deal with the problem of yaw rotation of the current view relative to the home view. Possible solutions include tagging of views with information from the celestial compass system, using multiple views pointing towards home, or rotation invariant encoding of the view. We illustrate briefly how a well-known shape description method from computer vision, Zernike moments, could provide a compact and rotation invariant representation of sky shapes to enhance visual homing. We discuss the biological plausibility of this solution, and also a fourth strategy, based on observed behaviour of insects, that involves transfer of information from visual memory matching to the compass system.
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Affiliation(s)
- Thomas Stone
- School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK
| | - Michael Mangan
- Sheffield Robotics, Department of Computer Science, University of Sheffield, Regent Court, Sheffield S1 4DP, UK
| | - Antoine Wystrach
- CNRS, Université Paul Sabatier, Toulouse, 31062 cedex 09, France
| | - Barbara Webb
- School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK
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Abstract
PURPOSE Shoeprint recognition has been widely used as forensic evidence in criminal cases. The purpose of this study is to propose a shoeprint retrieval method based on core point alignment for pattern analysis. METHOD The proposed method firstly detects contour points in a black-and-white shoeprint image. These reliable contour points are selected to simulate the left and right sidelines of the shoeprint by a curve fitting method. Subsequently, the most concave points along the left and right sidelines can determine the core point of the shoeprint, thereby partitioning the shoeprint into circular regions. Next, the Zernike moments of the circular regions are calculated for pattern descriptions of each region. Finally, the Euclidean distance is measured to match the shoeprints with the same pattern. RESULT The highest APR=0.726 is obtained from the first four Zernike moments with a radius of 90pixels and three baselines. The experimental results also show that the Zernike method in any order always outperforms the compared moment invariant and GLCM method. The experimental results also indicate that the core point is more stable than the gravity center in the both sets, because the standard deviation values of the core point are less than that of the gravity center. CONCLUSIONS This study has verified that the proposed method can effectively align shoeprints for pattern comparison.
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Affiliation(s)
- Chih-Ying Gwo
- Department of Information Management, Chien Hsin University of Science and Technology, Taiwan.
| | - Chia-Hung Wei
- Department of Information Management, Chien Hsin University of Science and Technology, Taiwan; Graduate Institute of Biomedical Informatics, Taipei Medical University, Taiwan.
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Broggio D, Moignier A, Ben Brahim K, Gardumi A, Grandgirard N, Pierrat N, Chea M, Derreumaux S, Desbrée A, Boisserie G, Aubert B, Mazeron JJ, Franck D. Comparison of organs' shapes with geometric and Zernike 3D moments. Comput Methods Programs Biomed 2013; 111:740-754. [PMID: 23846154 DOI: 10.1016/j.cmpb.2013.06.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2012] [Revised: 06/07/2013] [Accepted: 06/13/2013] [Indexed: 06/02/2023]
Abstract
The morphological similarity of organs is studied with feature vectors based on geometric and Zernike 3D moments. It is particularly investigated if outliers and average models can be identified. For this purpose, the relative proximity to the mean feature vector is defined, principal coordinate and clustering analyses are also performed. To study the consistency and usefulness of this approach, 17 livers and 76 hearts voxel models from several sources are considered. In the liver case, models with similar morphological feature are identified. For the limited amount of studied cases, the liver of the ICRP male voxel model is identified as a better surrogate than the female one. For hearts, the clustering analysis shows that three heart shapes represent about 80% of the morphological variations. The relative proximity and clustering analysis rather consistently identify outliers and average models. For the two cases, identification of outliers and surrogate of average models is rather robust. However, deeper classification of morphological feature is subject to caution and can only be performed after cross analysis of at least two kinds of feature vectors. Finally, the Zernike moments contain all the information needed to re-construct the studied objects and thus appear as a promising tool to derive statistical organ shapes.
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Affiliation(s)
- D Broggio
- Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PRP-HOM/SDI/LEDI, BP-17, F92262 Fontenay-aux-Roses, France.
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