Displaying publications 1 - 20 of 113 in total

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  1. Facial dimensions of Malay children with repaired unilateral cleft lip and palate: a three dimensional analysis
    Zreaqat M, Hassan R, Halim AS
    Int J Oral Maxillofac Surg, 2012 Jun;41(6):783-8.
    PMID: 22424709 DOI: 10.1016/j.ijom.2012.02.003
    This comparative cross-sectional study assessed the facial surface dimensions of a group of Malay children with unilateral cleft lip and palate (UCLP) and compared them with a control group. 30 Malay children with UCLP aged 8-10 years and 30 unaffected age-matched children were voluntarily recruited from the Orthodontic Specialist Clinic in Hospital Universiti Sains Malaysia (HUSM). For the cleft group, lip and palate were repaired and assessment was performed prior to alveolar bone grafting and orthodontic treatment. The investigation was carried out using 3D digital stereophotogrammetry. 23 variables and two ratios were compared three-dimensionally between both groups. Statistically significant dimensional differences (P<0.05) were found between the UCLP Malay group and the control group mainly in the nasolabial region. These include increased alar base and alar base root width, shorter upper lip length, and increased nose base/mouth width ratio in the UCLP group. There were significant differences between the facial surface morphology of UCLP Malay children and control subjects. Particular surgical procedures performed during primary surgeries may contribute to these differences and negatively affect the surgical outcome.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  2. Fulltext Automated identification of Monogeneans using digital image processing and K-nearest neighbour approaches
    Yousef Kalafi E, Tan WB, Town C, Dhillon SK
    BMC Bioinformatics, 2016 Dec 22;17(Suppl 19):511.
    PMID: 28155722 DOI: 10.1186/s12859-016-1376-z
    BACKGROUND: Monogeneans are flatworms (Platyhelminthes) that are primarily found on gills and skin of fishes. Monogenean parasites have attachment appendages at their haptoral regions that help them to move about the body surface and feed on skin and gill debris. Haptoral attachment organs consist of sclerotized hard parts such as hooks, anchors and marginal hooks. Monogenean species are differentiated based on their haptoral bars, anchors, marginal hooks, reproductive parts' (male and female copulatory organs) morphological characters and soft anatomical parts. The complex structure of these diagnostic organs and also their overlapping in microscopic digital images are impediments for developing fully automated identification system for monogeneans (LNCS 7666:256-263, 2012), (ISDA; 457-462, 2011), (J Zoolog Syst Evol Res 52(2): 95-99. 2013;). In this study images of hard parts of the haptoral organs such as bars and anchors are used to develop a fully automated identification technique for monogenean species identification by implementing image processing techniques and machine learning methods.

    RESULT: Images of four monogenean species namely Sinodiplectanotrema malayanus, Trianchoratus pahangensis, Metahaliotrema mizellei and Metahaliotrema sp. (undescribed) were used to develop an automated technique for identification. K-nearest neighbour (KNN) was applied to classify the monogenean specimens based on the extracted features. 50% of the dataset was used for training and the other 50% was used as testing for system evaluation. Our approach demonstrated overall classification accuracy of 90%. In this study Leave One Out (LOO) cross validation is used for validation of our system and the accuracy is 91.25%.

    CONCLUSIONS: The methods presented in this study facilitate fast and accurate fully automated classification of monogeneans at the species level. In future studies more classes will be included in the model, the time to capture the monogenean images will be reduced and improvements in extraction and selection of features will be implemented.

    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  3. Fusion of gamma scintigraphic and magnetic resonance images improves the anatomical delineation of radiotracer for the assessment of gastrointestinal transit
    Yeong CH, Abdullah BJ, Ng KH, Chung LY, Goh KL, Perkins AC
    Nucl Med Commun, 2013 Jul;34(7):645-51.
    PMID: 23612704 DOI: 10.1097/MNM.0b013e32836141e4
    This paper describes the use of gamma scintigraphic and magnetic resonance (MR) fusion images for improving the anatomical delineation of orally administered radiotracers used in gastrointestinal (GI) transit investigations.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  4. Fulltext Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter
    Wan Ahmad WS, Zaki WM, Ahmad Fauzi MF
    Biomed Eng Online, 2015;14:20.
    PMID: 25889188 DOI: 10.1186/s12938-015-0014-8
    Unsupervised lung segmentation method is one of the mandatory processes in order to develop a Content Based Medical Image Retrieval System (CBMIRS) of CXR. The purpose of the study is to present a robust solution for lung segmentation of standard and mobile chest radiographs using fully automated unsupervised method.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  5. Automated diagnosis of celiac disease by video capsule endoscopy using DAISY Descriptors
    Vicnesh J, Wei JKE, Ciaccio EJ, Oh SL, Bhagat G, Lewis SK, et al.
    J Med Syst, 2019 Apr 26;43(6):157.
    PMID: 31028562 DOI: 10.1007/s10916-019-1285-6
    Celiac disease is a genetically determined disorder of the small intestine, occurring due to an immune response to ingested gluten-containing food. The resulting damage to the small intestinal mucosa hampers nutrient absorption, and is characterized by diarrhea, abdominal pain, and a variety of extra-intestinal manifestations. Invasive and costly methods such as endoscopic biopsy are currently used to diagnose celiac disease. Detection of the disease by histopathologic analysis of biopsies can be challenging due to suboptimal sampling. Video capsule images were obtained from celiac patients and controls for comparison and classification. This study exploits the use of DAISY descriptors to project two-dimensional images onto one-dimensional vectors. Shannon entropy is then used to extract features, after which a particle swarm optimization algorithm coupled with normalization is employed to select the 30 best features for classification. Statistical measures of this paradigm were tabulated. The accuracy, positive predictive value, sensitivity and specificity obtained in distinguishing celiac versus control video capsule images were 89.82%, 89.17%, 94.35% and 83.20% respectively, using the 10-fold cross-validation technique. When employing manual methods rather than the automated means described in this study, technical limitations and inconclusive results may hamper diagnosis. Our findings suggest that the computer-aided detection system presented herein can render diagnostic information, and thus may provide clinicians with an important tool to validate a diagnosis of celiac disease.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  6. Fulltext Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism
    Usman OL, Muniyandi RC, Omar K, Mohamad M
    PLoS One, 2021;16(2):e0245579.
    PMID: 33630876 DOI: 10.1371/journal.pone.0245579
    Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia's neural-biomarkers from MRI datasets sourced from publicly available open databases. The proposed system utilized a modified histogram normalization (MHN) method to improve dyslexia neural-biomarker interpretations by mapping the pixels' intensities of low-quality input neuroimages to range between the low-intensity region of interest (ROIlow) and high-intensity region of interest (ROIhigh) of the high-quality image. This was achieved after initial image smoothing using the Gaussian filter method with an isotropic kernel of size 4mm. The performance of the proposed smoothing and normalization methods was evaluated based on three image post-processing experiments: ROI segmentation, gray matter (GM) tissues volume estimations, and deep learning (DL) classifications using Computational Anatomy Toolbox (CAT12) and pre-trained models in a MATLAB working environment. The three experiments were preceded by some pre-processing tasks such as image resizing, labelling, patching, and non-rigid registration. Our results showed that the best smoothing was achieved at a scale value, σ = 1.25 with a 0.9% increment in the peak-signal-to-noise ratio (PSNR). Results from the three image post-processing experiments confirmed the efficacy of the proposed methods. Evidence emanating from our analysis showed that using the proposed MHN and Gaussian smoothing methods can improve comparability of image features and neural-biomarkers of dyslexia with a statistically significantly high disc similarity coefficient (DSC) index, low mean square error (MSE), and improved tissue volume estimations. After 10 repeated 10-fold cross-validation, the highest accuracy achieved by DL models is 94.7% at a 95% confidence interval (CI) level. Finally, our finding confirmed that the proposed MHN method significantly outperformed the normalization method of the state-of-the-art histogram matching.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  7. Fulltext QuickCount®: a novel automated software for rapid cell detection and quantification
    Tiong KH, Chang JK, Pathmanathan D, Hidayatullah Fadlullah MZ, Yee PS, Liew CS, et al.
    Biotechniques, 2018 12;65(6):322-330.
    PMID: 30477327 DOI: 10.2144/btn-2018-0072
    We describe a novel automated cell detection and counting software, QuickCount® (QC), designed for rapid quantification of cells. The Bland-Altman plot and intraclass correlation coefficient (ICC) analyses demonstrated strong agreement between cell counts from QC to manual counts (mean and SD: -3.3 ± 4.5; ICC = 0.95). QC has higher recall in comparison to ImageJauto, CellProfiler and CellC and the precision of QC, ImageJauto, CellProfiler and CellC are high and comparable. QC can precisely delineate and count single cells from images of different cell densities with precision and recall above 0.9. QC is unique as it is equipped with real-time preview while optimizing the parameters for accurate cell count and needs minimum hands-on time where hundreds of images can be analyzed automatically in a matter of milliseconds. In conclusion, QC offers a rapid, accurate and versatile solution for large-scale cell quantification and addresses the challenges often faced in cell biology research.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  8. Lung disease stratification using amalgamation of Riesz and Gabor transforms in machine learning framework
    Than JCM, Saba L, Noor NM, Rijal OM, Kassim RM, Yunus A, et al.
    Comput Biol Med, 2017 10 01;89:197-211.
    PMID: 28825994 DOI: 10.1016/j.compbiomed.2017.08.014
    Lung disease risk stratification is important for both diagnosis and treatment planning, particularly in biopsies and radiation therapy. Manual lung disease risk stratification is challenging because of: (a) large lung data sizes, (b) inter- and intra-observer variability of the lung delineation and (c) lack of feature amalgamation during machine learning paradigm. This paper presents a two stage CADx cascaded system consisting of: (a) semi-automated lung delineation subsystem (LDS) for lung region extraction in CT slices followed by (b) morphology-based lung tissue characterization, thereby addressing the above shortcomings. LDS primarily uses entropy-based region extraction while ML-based lung characterization is mainly based on an amalgamation of directional transforms such as Riesz and Gabor along with texture-based features comprising of 100 greyscale features using the K-fold cross-validation protocol (K = 2, 3, 5 and 10). The lung database consisted of 96 patients: 15 normal and 81 diseased. We use five high resolution Computed Tomography (HRCT) levels representing different anatomy landmarks where disease is commonly seen. We demonstrate the amalgamated ML stratification accuracy of 99.53%, an increase of 2% against the conventional non-amalgamation ML system that uses alone Riesz-based feature embedded with feature selection based on feature strength. The robustness of the system was determined based on the reliability and stability that showed a reliability index of 0.99 and the deviation in risk stratification accuracies less than 5%. Our CADx system shows 10% better performance when compared against the mean of five other prominent studies available in the current literature covering over one decade.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  9. Fulltext A Fuzzy-C-Means-Clustering Approach: Quantifying Chromatin Pattern of Non-Neoplastic Cervical Squamous Cells
    Tang JR, Mat Isa NA, Ch'ng ES
    PLoS One, 2015;10(11):e0142830.
    PMID: 26560331 DOI: 10.1371/journal.pone.0142830
    Despite the effectiveness of Pap-smear test in reducing the mortality rate due to cervical cancer, the criteria of the reporting standard of the Pap-smear test are mostly qualitative in nature. This study addresses the issue on how to define the criteria in a more quantitative and definite term. A negative Pap-smear test result, i.e. negative for intraepithelial lesion or malignancy (NILM), is qualitatively defined to have evenly distributed, finely granular chromatin in the nuclei of cervical squamous cells. To quantify this chromatin pattern, this study employed Fuzzy C-Means clustering as the segmentation technique, enabling different degrees of chromatin segmentation to be performed on sample images of non-neoplastic squamous cells. From the simulation results, a model representing the chromatin distribution of non-neoplastic cervical squamous cell is constructed with the following quantitative characteristics: at the best representative sensitivity level 4 based on statistical analysis and human experts' feedbacks, a nucleus of non-neoplastic squamous cell has an average of 67 chromatins with a total area of 10.827 μm2; the average distance between the nearest chromatin pair is 0.508 μm and the average eccentricity of the chromatin is 0.47.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  10. Automated extraction of retinal vasculature
    Tan JH, Acharya UR, Chua KC, Cheng C, Laude A
    Med Phys, 2016 May;43(5):2311.
    PMID: 27147343 DOI: 10.1118/1.4945413
    The authors propose an algorithm that automatically extracts retinal vasculature and provides a simple measure to correct the extraction. The output of the method is a network of salient points, and blood vessels are drawn by connecting the salient points using a centripetal parameterized Catmull-Rom spline.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  11. A comparative study between xerographic, computer-assisted overlay generation and animated-superimposition methods in bite mark analyses
    Tai MW, Chong ZF, Asif MK, Rahmat RA, Nambiar P
    Leg Med (Tokyo), 2016 Sep;22:42-8.
    PMID: 27591538 DOI: 10.1016/j.legalmed.2016.07.009
    This study was to compare the suitability and precision of xerographic and computer-assisted methods for bite mark investigations. Eleven subjects were asked to bite on their forearm and the bite marks were photographically recorded. Alginate impressions of the subjects' dentition were taken and their casts were made using dental stone. The overlays generated by xerographic method were obtained by photocopying the subjects' casts and the incisal edge outlines were then transferred on a transparent sheet. The bite mark images were imported into Adobe Photoshop® software and printed to life-size. The bite mark analyses using xerographically generated overlays were done by comparing an overlay to the corresponding printed bite mark images manually. In computer-assisted method, the subjects' casts were scanned into Adobe Photoshop®. The bite mark analyses using computer-assisted overlay generation were done by matching an overlay and the corresponding bite mark images digitally using Adobe Photoshop®. Another comparison method was superimposing the cast images with corresponding bite mark images employing the Adobe Photoshop® CS6 and GIF-Animator©. A score with a range of 0-3 was given during analysis to each precision-determining criterion and the score was increased with better matching. The Kruskal Wallis H test showed significant difference between the three sets of data (H=18.761, p<0.05). In conclusion, bite mark analysis using the computer-assisted animated-superimposition method was the most accurate, followed by the computer-assisted overlay generation and lastly the xerographic method. The superior precision contributed by digital method is discernible despite the human skin being a poor recording medium of bite marks.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  12. Application of wavelet techniques for cancer diagnosis using ultrasound images: A Review
    Sudarshan VK, Mookiah MR, Acharya UR, Chandran V, Molinari F, Fujita H, et al.
    Comput Biol Med, 2016 Feb 1;69:97-111.
    PMID: 26761591 DOI: 10.1016/j.compbiomed.2015.12.006
    Ultrasound is an important and low cost imaging modality used to study the internal organs of human body and blood flow through blood vessels. It uses high frequency sound waves to acquire images of internal organs. It is used to screen normal, benign and malignant tissues of various organs. Healthy and malignant tissues generate different echoes for ultrasound. Hence, it provides useful information about the potential tumor tissues that can be analyzed for diagnostic purposes before therapeutic procedures. Ultrasound images are affected with speckle noise due to an air gap between the transducer probe and the body. The challenge is to design and develop robust image preprocessing, segmentation and feature extraction algorithms to locate the tumor region and to extract subtle information from isolated tumor region for diagnosis. This information can be revealed using a scale space technique such as the Discrete Wavelet Transform (DWT). It decomposes an image into images at different scales using low pass and high pass filters. These filters help to identify the detail or sudden changes in intensity in the image. These changes are reflected in the wavelet coefficients. Various texture, statistical and image based features can be extracted from these coefficients. The extracted features are subjected to statistical analysis to identify the significant features to discriminate normal and malignant ultrasound images using supervised classifiers. This paper presents a review of wavelet techniques used for preprocessing, segmentation and feature extraction of breast, thyroid, ovarian and prostate cancer using ultrasound images.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  13. Fulltext A chaotic cryptosystem for images based on Henon and Arnold cat map
    Soleymani A, Nordin MJ, Sundararajan E
    ScientificWorldJournal, 2014;2014:536930.
    PMID: 25258724 DOI: 10.1155/2014/536930
    The rapid evolution of imaging and communication technologies has transformed images into a widespread data type. Different types of data, such as personal medical information, official correspondence, or governmental and military documents, are saved and transmitted in the form of images over public networks. Hence, a fast and secure cryptosystem is needed for high-resolution images. In this paper, a novel encryption scheme is presented for securing images based on Arnold cat and Henon chaotic maps. The scheme uses Arnold cat map for bit- and pixel-level permutations on plain and secret images, while Henon map creates secret images and specific parameters for the permutations. Both the encryption and decryption processes are explained, formulated, and graphically presented. The results of security analysis of five different images demonstrate the strength of the proposed cryptosystem against statistical, brute force and differential attacks. The evaluated running time for both encryption and decryption processes guarantee that the cryptosystem can work effectively in real-time applications.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  14. Diagnostic ability of computed tomography using DentaScan software in endodontics: case reports
    Siotia J, Gupta SK, Acharya SR, Saraswathi V
    Int J Comput Dent, 2011;14(4):321-34.
    PMID: 22324223
    Radiographic examination is essential in diagnosis and treatment planning in endodontics. Conventional radiographs depict structures in two dimensions only. The ability to assess the area of interest in three dimensions is advantageous. Computed tomography is an imaging technique which produces three-dimensional images of an object by taking a series of two-dimensional sectional X-ray images. DentaScan is a computed tomography software program that allows the mandible and maxilla to be imaged in three planes: axial, panoramic, and cross-sectional. As computed tomography is used in endodontics, DentaScan can play a wider role in endodontic diagnosis. It provides valuable information in the assessment of the morphology of the root canal, diagnosis of root fractures, internal and external resorptions, pre-operative assessment of anatomic structures etc. The aim of this article is to explore the clinical usefulness of computed tomography and DentaScan in endodontic diagnosis, through a series of four cases of different endodontic problems.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods
  15. Signal-to-noise ratio estimation on SEM images using cubic spline interpolation with Savitzky-Golay smoothing
    Sim KS, Kiani MA, Nia ME, Tso CP
    J Microsc, 2014 Jan;253(1):1-11.
    PMID: 24164248 DOI: 10.1111/jmi.12089
    A new technique based on cubic spline interpolation with Savitzky-Golay noise reduction filtering is designed to estimate signal-to-noise ratio of scanning electron microscopy (SEM) images. This approach is found to present better result when compared with two existing techniques: nearest neighbourhood and first-order interpolation. When applied to evaluate the quality of SEM images, noise can be eliminated efficiently with optimal choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  16. Fulltext Reducing scanning electron microscope charging by using exponential contrast stretching technique on post-processing images
    Sim KS, Tan YY, Lai MA, Tso CP, Lim WK
    J Microsc, 2010 Apr 1;238(1):44-56.
    PMID: 20384837 DOI: 10.1111/j.1365-2818.2009.03328.x
    An exponential contrast stretching (ECS) technique is developed to reduce the charging effects on scanning electron microscope images. Compared to some of the conventional histogram equalization methods, such as bi-histogram equalization and recursive mean-separate histogram equalization, the proposed ECS method yields better image compensation. Diode sample chips with insulating and conductive surfaces are used as test samples to evaluate the efficiency of the developed algorithm. The algorithm is implemented in software with a frame grabber card, forming the front-end video capture element.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  17. Single image signal-to-noise ratio estimation for magnetic resonance images
    Sim KS, Lai MA, Tso CP, Teo CC
    J Med Syst, 2011 Feb;35(1):39-48.
    PMID: 20703587 DOI: 10.1007/s10916-009-9339-9
    A novel technique to quantify the signal-to-noise ratio (SNR) of magnetic resonance images is developed. The image SNR is quantified by estimating the amplitude of the signal spectrum using the autocorrelation function of just one single magnetic resonance image. To test the performance of the quantification, SNR measurement data are fitted to theoretically expected curves. It is shown that the technique can be implemented in a highly efficient way for the magnetic resonance imaging system.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  18. Fulltext Improvement to the scanning electron microscope image colorization by adaptive tuning
    Sim KS, Ting HY, Lai MA, Tso CP
    J Microsc, 2009 Jun;234(3):243-50.
    PMID: 19493101 DOI: 10.1111/j.1365-2818.2009.03167.x
    An improvement to the previously proposed Canny optimization technique for scanning electron microscope image colorization is reported. The additional process is adaptive tuning, where colour tuning is performed adaptively, based on comparing the original luminance values with calculated luminance values. The complete adaptive Canny optimization technique gives significantly better mechanical contrast on scanning electron microscope grey-scale images than do existing methods.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  19. Fulltext Lagrange time delay estimation for scanning electron microscope image magnification
    Sim KS, Thong LW, Ting HY, Tso CP
    J Microsc, 2010 Feb;237(2):111-8.
    PMID: 20096041 DOI: 10.1111/j.1365-2818.2009.03325.x
    Interpolation techniques that are used for image magnification to obtain more useful details of the surface such as morphology and mechanical contrast usually rely on the signal information distributed around edges and areas of sharp changes and these signal information can also be used to predict missing details from the sample image. However, many of these interpolation methods tend to smooth or blur out image details around the edges. In the present study, a Lagrange time delay estimation interpolator method is proposed and this method only requires a small filter order and has no noticeable estimation bias. Comparing results with the original scanning electron microscope magnification and results of various other interpolation methods, the Lagrange time delay estimation interpolator is found to be more efficient, more robust and easier to execute.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
  20. FPGA implementation of real-time SENSE reconstruction using pre-scan and Emaps sensitivities
    Siddiqui MF, Reza AW, Shafique A, Omer H, Kanesan J
    Magn Reson Imaging, 2017 12;44:82-91.
    PMID: 28855113 DOI: 10.1016/j.mri.2017.08.005
    Sensitivity Encoding (SENSE) is a widely used technique in Parallel Magnetic Resonance Imaging (MRI) to reduce scan time. Reconfigurable hardware based architecture for SENSE can potentially provide image reconstruction with much less computation time. Application specific hardware platform for SENSE may dramatically increase the power efficiency of the system and can decrease the execution time to obtain MR images. A new implementation of SENSE on Field Programmable Gate Array (FPGA) is presented in this study, which provides real-time SENSE reconstruction right on the receiver coil data acquisition system with no need to transfer the raw data to the MRI server, thereby minimizing the transmission noise and memory usage. The proposed SENSE architecture can reconstruct MR images using receiver coil sensitivity maps obtained using pre-scan and eigenvector (E-maps) methods. The results show that the proposed system consumes remarkably less computation time for SENSE reconstruction, i.e., 0.164ms @ 200MHz, while maintaining the quality of the reconstructed images with good mean SNR (29+ dB), less RMSE (<5×10-2) and comparable artefact power (<9×10-4) to conventional SENSE reconstruction. A comparison of the center line profiles of the reconstructed and reference images also indicates a good quality of the reconstructed images. Furthermore, the results indicate that the proposed architectural design can prove to be a significant tool for SENSE reconstruction in modern MRI scanners and its low power consumption feature can be remarkable for portable MRI scanners.
    Matched MeSH terms: Image Processing, Computer-Assisted/methods*
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