Journal articles on the topic 'Dice Similarity Coefficient'
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Rahman, Zahid, Altaf Hussain, Hussain Shah, and Muhammad Arshad. "Urdu News Clustering Using K-Mean Algorithm On The Basis Of Jaccard Coefficient And Dice Coefficient Similarity." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 10, no. 4 (February 8, 2022): 381–99. http://dx.doi.org/10.14201/adcaij2021104381399.
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Clustering is the unsupervised machine learning process that group data objects into clusters such that objects within the same cluster are highly similar to one another. Every day the quantity of Urdu text is increasing at a high speed on the internet. Grouping Urdu news manually is almost impossible, and there is an utmost need to device a mechanism which cluster Urdu news documents based on their similarity. Clustering Urdu news documents with accuracy is a research issue and it can be solved by using similarity techniques i.e., Jaccard and Dice coefficient, and clustering k-mean algorithm. In this research, the Jaccard and Dice coefficient has been used to find the similarity score of Urdu News documents in python programming language. For the purpose of clustering, the similarity results have been loaded to Waikato Environment for Knowledge Analysis (WEKA), by using k-mean algorithm the Urdu news documents have been clustered into five clusters. The obtained cluster’s results were evaluated in terms of Accuracy and Mean Square Error (MSE). The Accuracy and MSE of Jaccard was 85% and 44.4%, while the Accuracy and MSE of Dice coefficient was 87% and 35.76%. The experimental result shows that Dice coefficient is better as compared to Jaccard similarity on the basis of Accuracy and MSE.
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Cahyapratama, Afrianda, Kelly Rosa Sungkono, and Riyanarto Sarno. "Gap analysis business process model by using structural similarity." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (April 1, 2020): 124. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp124-134.
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<span>Gap analysis process model is a study that can help an institution to determine differences between business process models, such as a model of Standard Operating Procedure and a model of activities in an event log. Gap analysis is used for finding incomplete processes and can be obtained by using structural similarity. Structural similarity measures the similarity of activities and relationships depicting in the models. This research introduces a graph-matching algorithm as the structural similarity algorithm and compares it with dice coefficient algorithms. Graph-matching algorithm notices parallel relationships and invisible tasks, on the contrary dice coefficient algorithms only measure closeness between activities and relationships. The evaluation shows that the graph-matching algorithm produces 76.76 percent similarity between an SOP model and a process model generating from an event log; while, dice coefficient algorithms produces 70 percent similarity. The ability in detecting parallel relationships and invisible tasks causes the graph-matching algorithm produces a higher similarity value than dice coefficient algorithms.</span>
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Warrens, Matthijs J., and Alexandra de Raadt. "Ordering Properties of the First Eigenvector of Certain Similarity Matrices." Journal of Mathematics 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/582731.
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It is shown for coefficient matrices of Russell-Rao coefficients and two asymmetric Dice coefficients that ordinal information on a latent variable model can be obtained from the eigenvector corresponding to the largest eigenvalue.
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GAWRON, JEAN MARK, and KELLEN STEPHENS. "Sparsity and normalization in word similarity systems." Natural Language Engineering 22, no. 3 (August 19, 2015): 351–95. http://dx.doi.org/10.1017/s1351324915000261.
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AbstractWe investigate the problem of improving performance in distributional word similarity systems trained on sparse data, focusing on a family of similarity functions we call Dice-family functions (Dice 1945Ecology26(3): 297–302), including the similarity function introduced in Lin (1998Proceedings of the 15th International Conference on Machine Learning, 296–304), and Curran (2004 PhD thesis, University of Edinburgh. College of Science and Engineering. School of Informatics), as well as a generalized version of Dice Coefficient used in data mining applications (Strehl 2000, 55). We propose a generalization of the Dice-family functions which uses a weight parameter α to make the similarity functions asymmetric. We show that this generalized family of functions (α systems) all belong to the class of asymmetric models first proposed in Tversky (1977Psychological Review84: 327–352), and in a multi-task evaluation of ten word similarity systems, we show that α systems have the best performance across word ranks. In particular, we show that α-parameterization substantially improves the correlations of all Dice-family functions with human judgements on three words sets, including the Miller–Charles/Rubenstein Goodenough word set (Miller and Charles 1991Language and Cognitive Processes6(1): 1–28; Rubenstein and Goodenough 1965Communications of the ACM8: 627–633).
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Wang, Hao-Jen, Li-Wei Chen, Hsin-Ying Lee, Yu-Jung Chung, Yan-Ting Lin, Yi-Chieh Lee, Yi-Chang Chen, Chung-Ming Chen, and Mong-Wei Lin. "Correction: Wang et al. Automated 3D Segmentation of the Aorta and Pulmonary Artery on Non-Contrast-Enhanced Chest Computed Tomography Images in Lung Cancer Patients. Diagnostics 2022, 12, 967." Diagnostics 12, no. 8 (August 2, 2022): 1867. http://dx.doi.org/10.3390/diagnostics12081867.
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Febriansyah, Luke Michael, and Shinta Estri Wahyuningrum. "ANALYSIS WINNOWING ALGORITHM FOR TEXT PLAGIARISM DETECTION USING THREE METHOD SIMILARITY." Proxies : Jurnal Informatika 2, no. 2 (March 10, 2021): 42. http://dx.doi.org/10.24167/proxies.v2i2.3208.
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Cases of plagiarism in recent years has been an issues. Based on that issues, this research will create a system to detect similarity in a text. There is an aspect as reference of the research that is analyze the plagiarism algorithm. This research will analyze the accuracy one of plagiarism check algorithm, winnowing algorithm. Winnowing algorithm is a plagiarism detection algorithm based on document fingerprinting. To calculate percentage similarity of document fingerprinting in text, there are 3 methods to measure similarity that will be used in this research, which is jaccard similarity coefficient, sorensen dice similarity coefficient, and berg similarity coefficient.
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Chen, Wenjuan, Penggang Bai, Jianji Pan, Yuanji Xu, and Kaiqiang Chen. "Changes in Tumor Volumes and Spatial Locations Relative to Normal Tissues During Cervical Cancer Radiotherapy Assessed by Cone Beam Computed Tomography." Technology in Cancer Research & Treatment 16, no. 2 (January 4, 2017): 246–52. http://dx.doi.org/10.1177/1533034616685942.
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Purpose: To assess changes in the volumes and spatial locations of tumors and surrounding organs by cone beam computed tomography during treatment for cervical cancer. Materials and Methods: Sixteen patients with cervical cancer had intensity-modulated radiotherapy and off-line cone beam computed tomography during chemotherapy and/or radiation therapy. The gross tumor volume (GTV-T) and clinical target volumes (CTVs) were contoured on the planning computed tomography and weekly cone beam computed tomography image, and changes in volumes and spatial locations were evaluated using the volume difference method and Dice similarity coefficients. Results: The GTV-T was 79.62 cm3 at prior treatment (0f) and then 20.86 cm3 at the end of external-beam chemoradiation. The clinical target volume changed slightly from 672.59 cm3 to 608.26 cm3, and the uterine volume (CTV-T) changed slightly from 83.72 cm3 to 80.23 cm3. There were significant differences in GTV-T and CTV-T among the different groups ( P < .001), but the clinical target volume was not significantly different in volume ( P > .05). The mean percent volume changes ranged from 23.05% to 70.85% for GTV-T, 4.71% to 6.78% for CTV-T, and 5.84% to 9.59% for clinical target volume, and the groups were significantly different ( P < .05). The Dice similarity coefficient of GTV-T decreased during the course of radiation therapy ( P < .001). In addition, there were significant differences in GTV-T among different groups ( P < .001), and changes in GTV-T correlated with the radiotherapy ( P < .001). There was a negative correlation between volume change rate (DV) and Dice similarity coefficient in the GTV-T and organs at risk ( r < 0; P < .05). Conclusion: The volume, volume change rate, and Dice similarity coefficient of GTV-T were all correlated with increase in radiation treatment. Significant variations in tumor regression and spatial location occurred during radiotherapy for cervical cancer. Adaptive radiotherapy approaches are needed to improve the treatment accuracy for cervical cancer.
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Anantharajan, Shenbagarajan, Shenbagalakshmi Gunasekaran, and havasi Subramanian. "Brain Tumor Segmentation based on Red-Bellied Woodpecker Mating Optimization Algorithm." NeuroQuantology 20, no. 5 (May 18, 2022): 785–90. http://dx.doi.org/10.14704/nq.2022.20.5.nq22235.
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Earlier, many researchers proposed various segmentation algorithms to segment tumor from MRI Brain image. The method of a nature-inspired meta heuristic-based woodpecker characteristics approach is used to segment the tumored area of this proposed study. In this automated MRI brain tumor segmentation, the MRI brain image gets enhanced for improving the performance of the segmentation accompanied by the skull elimination phase to eliminate the morphological operations of all non-brain tissues. In the end, the RBWMOA (Red-Bellied Woodpecker Mating Optimization Algorithm) is suggested for the segmentation of tumor. An assessment of the experimental outcomes of the methodology suggested was focused on the coefficient of dice similarity, Hausdorff distance, Jaccard coefficient, Precision, Recall, Accuracy and F-measure. The experimental result of RBWMOA obtain better performance and shows0.845 Dice Similarity Coefficient, 7.231 Hausdorff distance in mm, 0.6981 Jaccard Coefficient, 95.67 % Precision, 94.72 % Recall, 98.29 % Accuracy and 95.19 % F-measure.
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Farzaneh, Negar, Craig A. Williamson, Cheng Jiang, Ashok Srinivasan, Jayapalli R. Bapuraj, Jonathan Gryak, Kayvan Najarian, and S. M. Reza Soroushmehr. "Automated Segmentation and Severity Analysis of Subdural Hematoma for Patients with Traumatic Brain Injuries." Diagnostics 10, no. 10 (September 30, 2020): 773. http://dx.doi.org/10.3390/diagnostics10100773.
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Detection and severity assessment of subdural hematoma is a major step in the evaluation of traumatic brain injuries. This is a retrospective study of 110 computed tomography (CT) scans from patients admitted to the Michigan Medicine Neurological Intensive Care Unit or Emergency Department. A machine learning pipeline was developed to segment and assess the severity of subdural hematoma. First, the probability of each point belonging to the hematoma region was determined using a combination of hand-crafted and deep features. This probability provided the initial state of the segmentation. Next, a 3D post-processing model was applied to evolve the initial state and delineate the hematoma. The recall, precision, and Dice similarity coefficient of the proposed segmentation method were 78.61%, 76.12%, and 75.35%, respectively, for the entire population. The Dice similarity coefficient was 79.97% for clinically significant hematomas, which compared favorably to an inter-rater Dice similarity coefficient. In volume-based severity analysis, the proposed model yielded an F1, recall, and specificity of 98.22%, 98.81%, and 92.31%, respectively, in detecting moderate and severe subdural hematomas based on hematoma volume. These results show that the combination of classical image processing and deep learning can outperform deep learning only methods to achieve greater average performance and robustness. Such a system can aid critical care physicians in reducing time to intervention and thereby improve long-term patient outcomes.
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YOKOYAMA, E., and M. UCHIMURA. "Optimal settings of fingerprint-type analysing computer software for the analysis of enterohaemorrhagic Escherichia coli pulsed-field gel electrophoresis patterns." Epidemiology and Infection 134, no. 5 (March 28, 2006): 1004–14. http://dx.doi.org/10.1017/s0950268806006145.
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Settings of fingerprint-type analysing computer software were optimized for analysis of enterohaemorrhagic Escherichia coli (EHEC) pulsed-field gel electrophoresis (PFGE) patterns. Under the lowest values of parameters, maximum value of similarities calculated using the Dice coefficient were obtained between PFGE patterns from one EHEC strain on the same gel when reference lanes for calibration of distortions during electrophoresis were set to every fourth lane. PFGE patterns of 15 EHEC strains on different gels were investigated. Similarity values calculated using the Pearson product-moment correlation coefficient (Pearson correlation) were significantly higher than those using the Dice coefficient with optimal values of parameters determined by the program (P<0·01). When PFGE patterns of 45 EHEC strains were analysed by the computer program, EHEC strains from one mass outbreak and three intra-family outbreaks were each clustered and the similarity values within the clusters were >90% using Pearson correlation.
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Dencic, Srbislav, Ron Depauw, Vojislava Momcilovic, and Vladimir Acin. "Comparison of similarity coefficients used for cluster analysis based on SSR markers in sister line wheat cultivars." Genetika 48, no. 1 (2016): 219–32. http://dx.doi.org/10.2298/gensr1601219d.
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The objective of this study was to compared fourteen different similarity coefficients and their influence in sister line wheat cultivars clustering. Seventeen sister cultivars developed from two crosses were used and fingerprinted with 19 wheat microsatellite markers. Comparisons among the similarity coefficients were made using the Sperman correlation analysis, dendogram evaluation (visual inspection and consensus fork index - CIc), projection efficiency in a two-dimensional space, and groups formed by the Tocher optimization procedure. The Sperman correlation coefficients among the fourteen similarity coefficients were all high showing a strong association between them. The correlation coefficient between Dice and Kulczinski and Ochiai I as well as between Hamann and Simple matching and between Kulczinski and Ochiai I was equal to 1. Although visual estimation of the dendograms shows almost identical clustering structures, CIc indexes indicate that all coefficients are not identical.
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Lareyre, Fabien, Cédric Adam, Marion Carrier, and Juliette Raffort. "Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning." Journal of Clinical Medicine 10, no. 15 (July 29, 2021): 3347. http://dx.doi.org/10.3390/jcm10153347.
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Background: Computed tomography angiography (CTA) is one of the most commonly used imaging technique for the management of vascular diseases. Here, we aimed to develop a hybrid method combining a feature-based expert system with a supervised deep learning (DL) algorithm to enable a fully automatic segmentation of the abdominal vascular tree. Methods: We proposed an algorithm based on the hybridization of a data-driven convolutional neural network and a knowledge-based model dedicated to vascular system segmentation. By using two distinct datasets of CTA from patients to evaluate independence to training dataset, the accuracy of the hybrid method for lumen and thrombus segmentation was evaluated compared to the feature-based expert system alone and to the ground truth provided by a human expert. Results: The hybrid approach demonstrated a better accuracy for lumen segmentation compared to the expert system alone (volume similarity: 0.8128 vs. 0.7912, p = 0.0006 and Dice similarity coefficient: 0.8266 vs. 0.7942, p < 0.0001). The accuracy for thrombus segmentation was also enhanced using the hybrid approach (volume similarity: 0.9404 vs. 0.9185, p = 0.0027 and Dice similarity coefficient: 0.8918 vs. 0.8654, p < 0.0001). Conclusions: By enabling a robust and fully automatic segmentation, the method could be used to develop real-time decision support to help in the management of vascular diseases.
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Ali, Muhammad Haidar, and Faisal Rahutomo. "MANHATTAN DISTANCE AND DICE SIMILARITY EVALUATION ON INDONESIAN ESSAY EXAMINATION SYSTEM." JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 4, no. 2 (December 2, 2019): 156. http://dx.doi.org/10.29100/jipi.v4i2.1398.
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<span lang="EN-US">Each learning process requires an evaluation tool to measure the level of understanding of students. The type of evaluation can be multiple choice questions, short entries and essays. Some studies reveal essay exams better than other types of evaluations. An essay assessment is automatically needed to save teacher time in correcting answers. However, the development of essay assessments is still ongoing. The aim is to obtain a better accuracy value than the method used in the assessment. Based on these problems, this study proposes a comparative analysis of similarity methods for online essay exam assessment. The similarity method compared is Similarity Dice and Manhattan Distance. Both methods produce coefficient values which are then compared to the assessment of the system with manual scales with the same scale. The data used were 2162 data. This data was obtained from 50 students who answered 40 questions (politics, sports, lifestyle and technology). The data obtained in this study can be used to support other research that can be accessed at www.indonesian-ir.org. This research shows that the Dice similarity scheme is more accurate than Manhattan Distance</span>
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Colombo, Alberto, Giulia Saia, Alcide A. Azzena, Alice Rossi, Fabio Zugni, Paola Pricolo, Paul E. Summers, et al. "Semi-Automated Segmentation of Bone Metastases from Whole-Body MRI: Reproducibility of Apparent Diffusion Coefficient Measurements." Diagnostics 11, no. 3 (March 11, 2021): 499. http://dx.doi.org/10.3390/diagnostics11030499.
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Using semi-automated software simplifies quantitative analysis of the visible burden of disease on whole-body MRI diffusion-weighted images. To establish the intra- and inter-observer reproducibility of apparent diffusion coefficient (ADC) measures, we retrospectively analyzed data from 20 patients with bone metastases from breast (BCa; n = 10; aged 62.3 ± 14.8) or prostate cancer (PCa; n = 10; aged 67.4 ± 9.0) who had undergone examinations at two timepoints, before and after hormone-therapy. Four independent observers processed all images twice, first segmenting the entire skeleton on diffusion-weighted images, and then isolating bone metastases via ADC histogram thresholding (ADC: 650–1400 µm2/s). Dice Similarity, Bland-Altman method, and Intraclass Correlation Coefficient were used to assess reproducibility. Inter-observer Dice similarity was moderate (0.71) for women with BCa and poor (0.40) for men with PCa. Nonetheless, the limits of agreement of the mean ADC were just ±6% for women with BCa and ±10% for men with PCa (mean ADCs: 941 and 999 µm2/s, respectively). Inter-observer Intraclass Correlation Coefficients of the ADC histogram parameters were consistently greater in women with BCa than in men with PCa. While scope remains for improving consistency of the volume segmented, the observer-dependent variability measured in this study was appropriate to distinguish the clinically meaningful changes of ADC observed in patients responding to therapy, as changes of at least 25% are of interest.
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PEARL, D. L., M. LOUIE, L. CHUI, K. DORÉ, K. M. GRIMSRUD, S. W. MARTIN, P. MICHEL, L. W. SVENSON, and S. A. McEWEN. "The use of randomization tests to assess the degree of similarity in PFGE patterns of E. coli O157 isolates from known outbreaks and statistical space–time clusters." Epidemiology and Infection 135, no. 1 (June 2, 2006): 100–109. http://dx.doi.org/10.1017/s0950268806006650.
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Using isolates from reported cases of Escherichia coli O157 from Alberta, Canada in 2002, we applied randomization tests to determine if cases associated with an outbreak or statistical space–time cluster had more similar pulsed-field gel electrophoresis patterns, based on Dice coefficients, than expected by chance alone. Within each outbreak and space–time cluster, we assessed the mean, median, 25th percentile, 75th percentile, standard deviation, coefficient of variation, and interquartile range of the Dice coefficients of each pairwise comparison among the isolates. To assess the statistical significance of measures of location (e.g. mean) and variation (e.g. standard deviation) we created randomization distributions using all isolates or only isolates from sporadic cases. We determined that randomization tests are an appropriate tool for evaluating the similarity among isolates from cases that have been linked epidemiologically or statistically. We found little difference between using all cases or only sporadic cases when creating our randomization distributions.
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Lalitha, S., N. Shanthi, and S. Gopinath. "A Detection of Amblyopia Medical Condition in Biomedical Datasets Using Image Segmentation and Detection Processing." Journal of Medical Imaging and Health Informatics 11, no. 11 (November 1, 2021): 2814–21. http://dx.doi.org/10.1166/jmihi.2021.3880.
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The recent past, the data volume in a media field is growing at a rapid rate, and conventional methods fail to manage such a large volume of data in healthcare systems, biomedical field, medical diagnostic systems etc. The main challenges associated with biomedical computation are the problems associated with management, storage, and analysis on extensive biomedical data. To play a significant role over such extensive data, the machine learning approach provides faster access to medical data with an improved framework. The main objective involves the detection of amblyopia condition from input images and comparing it with conventional image detection methods. The proposed method is examined in terms of detection accuracy, sensitivity, specificity, Hausdorff distance computation and Dice Coefficient. Also, the detection of an Amblyopic or Lazy Eye diseased images is still not prevalent in the field of image segmentation and detection. In this paper, we introduce a framework to process the Amblyopia image datasets using machine learning, and similarity comparison approach. The proposed image processing involves the segmentation of eye images using Recurrent Neural Networks (RNN), and the detection of Amblyopia disease is carried out with Hausdorff Distance computation and Dice coefficient similarity comparison on the segmented image. The initial subset points and threshold values are calculated from a set of 50 normal eye images. A set of 100 Amblyopic diseased image dataset is used for testing the proposed system, out of which 70 images are used for training the system. To evaluate the experimental results shows that proposed method obtains improved detection than existing Deeply-Learned Gaze Shifting Path (DLGSP), Cascade Regression Framework (CRF) and Mobile Iris Recognition System (MIRS) methods. The presence of Hausdorff Distance computation and Dice coefficient similarity comparison is used for reducing the overhead in the proposed method, and this can be used for computing large sets of images.
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Broggi, Sara, Elisa Scalco, Maria Luisa Belli, Gerlinde Logghe, Dirk Verellen, Stefano Moriconi, Anna Chiara, et al. "A Comparative Evaluation of 3 Different Free-Form Deformable Image Registration and Contour Propagation Methods for Head and Neck MRI: The Case of Parotid Changes During Radiotherapy." Technology in Cancer Research & Treatment 16, no. 3 (February 7, 2017): 373–81. http://dx.doi.org/10.1177/1533034617691408.
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Purpose: To validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approaches—the commercial MIM, the open-source Elastix software, and an optimized version of it. Materials and Methods: Twelve patients with head and neck cancer previously treated with radiotherapy were considered. Deformable image registration and parotid contour propagation were evaluated by considering the magnetic resonance images acquired before and after the end of the treatment. Deformable image registration, based on free-form deformation method, and contour propagation available on MIM were compared to Elastix. Two different contour propagation approaches were implemented for Elastix software, a conventional one (DIR_Trx) and an optimized homemade version, based on mesh deformation (DIR_Mesh). The accuracy of these 3 approaches was estimated by comparing propagated to manual contours in terms of average symmetric distance, maximum symmetric distance, Dice similarity coefficient, sensitivity, and inclusiveness. Results: A good agreement was generally found between the manual contours and the propagated ones, without differences among the 3 methods; in few critical cases with complex deformations, DIR_Mesh proved to be more accurate, having the lowest values of average symmetric distance and maximum symmetric distance and the highest value of Dice similarity coefficient, although nonsignificant. The average propagation errors with respect to the reference contours are lower than the voxel diagonal (2 mm), and Dice similarity coefficient is around 0.8 for all 3 methods. Conclusion: The 3 free-form deformation approaches were not significantly different in terms of deformable image registration accuracy and can be safely adopted for the registration and parotid contour propagation during radiotherapy on magnetic resonance imaging. More optimized approaches (as DIR_Mesh) could be preferable for critical deformations.
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Hassan, Wail M., Shiao Y. Wang, and Rudolph D. Ellender. "Methods To Increase Fidelity of Repetitive Extragenic Palindromic PCR Fingerprint-Based Bacterial Source Tracking Efforts." Applied and Environmental Microbiology 71, no. 1 (January 2005): 512–18. http://dx.doi.org/10.1128/aem.71.1.512-518.2005.
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ABSTRACT The goal of the study was to determine which similarity coefficient and statistical method to use to produce the highest rate of correct assignment (RCA) in repetitive extragenic palindromic PCR-based bacterial source tracking. In addition, the use of standards for deciding whether to accept or reject source assignments was investigated. The use of curve-based coefficients Cosine Coefficient and Pearson's Product Moment Correlation yielded higher RCAs than the use of band-based coefficients Jaccard, Dice, Jeffrey's x, and Ochiai. When enterococcal and Escherichia coli isolates from known sources were used in a blind test, the use of maximum similarity produced consistently higher RCAs than the use of average similarity. We also found that the use of a similarity value threshold and/or a quality factor threshold (the ratio of the average fingerprint similarity within a source to the average similarity of this source's isolates to an unknown) to decide whether to accept source assignments of unknowns increases the reliability of source assignments. Applying a similarity value threshold improved the overall RCA (ORCA) by 15 to 27% when enterococcal fingerprints were used and 8 to 29% when E. coli fingerprints were used. Applying the quality factor threshold resulted in a 22 to 32% improvement in the ORCA, depending on the fingerprinting technique used. This increase in reliability was, however, achieved at the expense of decreased numbers of isolates that were assigned a source.
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Chebrolu, Venkata V., Daniel Saenz, Dinesh Tewatia, William A. Sethares, George Cannon, and Bhudatt R. Paliwal. "Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours." Radiology Research and Practice 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/547075.
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Purpose. To achieve rapid automated delineation of gross target volume (GTV) and to quantify changes in volume/position of the target for radiotherapy planning using four-dimensional (4D) CT.Methods and Materials. Novel morphological processing and successive localization (MPSL) algorithms were designed and implemented for achieving autosegmentation. Contours automatically generated using MPSL method were compared with contours generated using state-of-the-art deformable registration methods (usingElastix©and MIMVista software). Metrics such as the Dice similarity coefficient, sensitivity, and positive predictive value (PPV) were analyzed. The target motion tracked using the centroid of the GTV estimated using MPSL method was compared with motion tracked using deformable registration methods.Results. MPSL algorithm segmented the GTV in 4DCT images in27.0±11.1seconds per phase (512×512resolution) as compared to142.3±11.3seconds per phase for deformable registration based methods in 9 cases. Dice coefficients between MPSL generated GTV contours and manual contours (considered as ground-truth) were0.865±0.037. In comparison, the Dice coefficients between ground-truth and contours generated using deformable registration based methods were 0.909 ± 0.051.Conclusions. The MPSL method achieved similar segmentation accuracy as compared to state-of-the-art deformable registration based segmentation methods, but with significant reduction in time required for GTV segmentation.
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Setiawan, Rudi. "Similarity Checking Similarity Checking of Source Code Module Using Running Karp Rabin Greedy String Tiling." Science Proceedings Series 1, no. 2 (April 24, 2019): 43–46. http://dx.doi.org/10.31580/sps.v1i2.624.
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Similarity checking of source code module, required a long process if it is done manually. Based on that problem, this research designed a software with structure-based approach using string matching technique with Running Karp-Rabin Greedy String Tiling (RKR-GST) Algorithm to check the similarity and using Dice Coefficient method to measure the level of similarity from 2 results source code modules.
The result of the experiments show that RKRGST which applied in this system capable of recognizing the changing of statement and the changing statement order, and be able to recognize the syntax procedure testing that has been taken from its comparison module. Modification by adding the comment on source code module and changing of procedure name which is called in body of procedure can also be recognized by system. Processing time needed to produce output depends on the number of program code row that contained in source code module.
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Desku, Astrit, Bujar Raufi, Artan Luma, and Besnik Selimi. "Recommender Model for Secure Software Engineering using Cosine Similarity Measures." International Journal of Engineering and Advanced Technology 11, no. 5 (June 30, 2022): 144–48. http://dx.doi.org/10.35940/ijeat.e3628.0611522.
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One of the essential components of Recommender Systems in Software Engineering is a static analysis that is answerable for producing recommendations for users. There are different techniques for how static analysis is carried out in recommender systems. This paper drafts a technique for the creation of recommendations using Cosine Similarity. Evaluation of such a system is done by using precision, recall, and so-called Dice similarity coefficient. Ground truth evaluations consisted of using experienced software developers for testing the recommendations. Also, statistical T-test has been applied in comparing the means of the two evaluated approaches. These tests point out the significant difference between the two compared sets.
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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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Rekha, T., Kottackal Poulose Martin, V. B. Sreekumar, and Joseph Madassery. "Genetic Diversity Assessment of Rarely Cultivated Traditional Indica Rice (Oryza sativa L.) Varieties." Biotechnology Research International 2011 (July 12, 2011): 1–7. http://dx.doi.org/10.4061/2011/784719.
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Random amplified polymorphic DNA fingerprinting was performed to assess the genetic diversity among rarely cultivated traditional indica rice (Oryza sativa L.) varieties collected from a tribal hamlet of Kerala State, India. A total of 664 DNA bands amplified by 15 primers exhibited 72.9% polymorphism (an average of 32.3 polymorphic bands per primer). The varieties Jeerakasala and Kalladiyaran exhibited the highest percent (50.19%) polymorphism, while Thondi and Adukkan showed the lowest (9.85%). Adukkan (78 bands) and Jeerakasala (56 bands) yielded the highest and the lowest number of amplicons, respectively. Unweighted Pair Group Method with Arithmetic mean analysis using the Dice similarity coefficient showed the highest value of similarity coefficient between the varieties Adukkan and Thondi, both shared higher level of similarity (0.81), followed by Kanali and Thondi (0.88). Of the three subclusters, the varieties of Adukkan, Thondi, Kanali, Mannuveliyan, Thonnuranthondi, and Chennellu grouped together with a similarity of 0.77. The second group represented by Navara, Gandhakasala, and Jeerakasala with a similarity coefficient of 0.76 formed a cohesive group. The variety Kalladiyaran formed an isolated position that joined the second cluster. The Principal Coordinate Analysis also showed separation of Kalladiyaran from the other varieties.
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Stafne, Eric T., John R. Clark, and Kim S. Lewers. "MOLECULAR MARKER-DERIVED GENETIC SIMILARITY ANALYSIS OF A SEGREGATING BLACKBERRY POPULATION." HortScience 40, no. 3 (June 2005): 874b—874. http://dx.doi.org/10.21273/hortsci.40.3.874b.
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A tetraploid blackberry population that segregates for two important morphological traits, thornlessness and primocane fruiting, was tested with molecular marker analysis. Both randomly amplified polymorphic DNA (RAPD) and simple sequence repeat (SSR) markers were used to screen a population of 98 genotypes within the population plus the two parents, `Arapaho' and `Prime-Jim' (APF-12). RAPD analysis averaged 3.4 markers per primer, whereas SSR analysis yielded 3.0 markers per primer pair. Similarity coefficient derived from the Dice index averaged over all individuals was 63% for RAPD markers, 73% for SSR markers, and 66% for RAPD and SSR markers together. The average similarity coefficients ranged from a high of 72% to a low of 38% for RAPD markers, 80% to 57% for SSR markers, and 73% to 55% for both. Comparison of the parents indicated a similarity of 67% for RAPD markers, 62% for SSR markers, and 67% for both. This is similar to a previous study that reported the similarity coefficient at 66%. Although inbreeding exists within the population, the level of heterozygosity is high. Also, evidence of tetrasomic inheritance was uncovered within the molecular marker analysis. This population will be used to identify potential markers linked to both morphological traits of interest. Further genetic linkage analysis and mapping is needed to identify any putative markers.
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Ribeiro, Márcia de Nazaré Oliveira, Samuel Pereira de Carvalho, João Bosco dos Santos, and Rafaela Priscila Antonio. "Genetic variability among cassava accessions based on SSR markers." Crop Breeding and Applied Biotechnology 11, no. 3 (September 2011): 263–69. http://dx.doi.org/10.1590/s1984-70332011000300009.
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The aim of this study was to characterize and estimate the genetic similarity among 93 cassava accessions. The DNA amplification was performed with 14 microsatellite primers. The amplification products were separated by a polyacrylamide gel electrophoresis, showing a polymorphism formation, through which the accessions were discriminated against. The genetic similarity among accessions of cassava was estimated by the Dice coefficient. Cluster analysis was carried out using the UPGMA method. The polymorphic primers amplified a total of 26 alleles with 2-4 alleles per loci. The genetic similarity ranged from 0.16 to 0.96. The average values for observed and expected heterozygosity were 0.18 and 0.46, respectively. Twenty genetic similarity clusters were determined, demonstrating diversity among accessions, suggesting the possibility of heterotic hybrid generation.
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Souza, Renata Janaína Carvalho de, Suzene Izídio da Silva, and Antonio Fernando Morais de Oliveira. "Chemical similarity among domesticated and wild genotypes of peanut based on n-alkanes profiles." Pesquisa Agropecuária Brasileira 45, no. 11 (November 2010): 1321–23. http://dx.doi.org/10.1590/s0100-204x2010001100013.
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The objective of this work was to analyze the epicuticular n-alkane profile of domesticated and wild peanut genotypes. Foliar epicuticular n-alkanes of four Arachis hypogaea genotypes and two wild species - A. monticola and A. stenosperma - were analyzed by gas chromatography. Chemical relationships between them were evaluated using the Dice coefficient and UPGMA method. Two clusters were formed: one with four A. hypogaea genotypes and the other with the two wild species. There is more similarity between the BR1 and LIGO-PE06 genotypes and between the BRS 151 L-7 and BRS Havana genotypes.
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Sakira Kamaruddin, Siti, Yuhanis Yusof, Nur Azzah Abu Bakar, Mohamed Ahmed Tayie, and Ghaith Abdulsattar A.Jabbar Alkubaisi. "Graph-based Representation for Sentence Similarity Measure : A Comparative Analysis." International Journal of Engineering & Technology 7, no. 2.14 (April 6, 2018): 32. http://dx.doi.org/10.14419/ijet.v7i2.14.11149.
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Textual data are a rich source of knowledge; hence, sentence comparison has become one of the important tasks in text mining related works. Most previous work in text comparison are performed at document level, research suggest that comparing sentence level text is a non-trivial problem. One of the reason is two sentences can convey the same meaning with totally dissimilar words. This paper presents the results of a comparative analysis on three representation schemes i.e. term frequency inverse document frequency, Latent Semantic Analysis and Graph based representation using three similarity measures i.e. Cosine, Dice coefficient and Jaccard similarity to compare the similarity of sentences. Results reveal that the graph based representation and the Jaccard similarity measure outperforms the others in terms of precision, recall and F-measures.
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Takhtawala, Ruquaiyah, Nataly Tapia Negrete, Madeleine Shaver, Turkay Kart, Yang Zhang, Vivian Youngjean Park, Min Jung Kim, Min-Ying Su, Daniel S. Chow, and Peter Chang. "Automated artificial intelligence quantification of fibroglandular tissue on breast MRI." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e12071-e12071. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e12071.
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e12071 Background: The objective of this study is to examine if a convolutional neural network can be utilized to automate breast fibroglandular tissue segmentation, a risk factor for breast cancer, on MRIs. Methods: This institutional review board approved study assessed retrospectively acquired MRI T1 pre-contrast image data for 238 patients. Ground truth parameters were derived through manual segmentation. A hybrid 3D/2D U-Net architecture was developed for fibroglandular tissue segmentation. The network was trained with T1 pre-contrast MRI data and their corresponding ground-truth labels. The analysis was started with image pre-processing. Each MRI volume was re-sampled and normalized using z-scores. Convolution operations reduced 3D volumes into a 2D slice in the contracting arm of the U-Net architecture. Results: A 5-fold cross validation was performed and the Dice similarity coefficient was used to assess the accuracy of fibroglandular tissue segmentation. Cross-validation results showed that the automated hybrid CNN approach resulted in a Dice similarity coefficient of 0.848 and a Pearson correlation of 0.961 in comparison to the ground-truth for fibroglandular breast tissue segmentation, which demonstrates high accuracy. Conclusions: The results demonstrate significant application of deep learning in accurately automating segmentation of breast fibroglandular tissue.
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Rukavina, Ivana, Sonja Petrovic, Tihomir Cupic, Sonja Vila, Suncica Guberac, and Luka Drenjancevic. "Genetic variability of wheat germplasm represented in the south Pannonian region." Genetika 49, no. 3 (2017): 831–42. http://dx.doi.org/10.2298/gensr1703831r.
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In this study, genetic variability was investigated among 50 winter wheat varieties (Triticum aestivum L.) which are grown in parts of Croatia, Hungary, Serbia and Slovenia according to 22 morphological characteristics used for DUS (distinctness, uniformity and stability) testing. The average Dice similarity coefficient was 0.371. The determined similarity coefficient was in range 0.083 - 0.776. A significant variability of 6.21% in the breeding programs according to period was determined as well as significant variability of 3.10% between breeding programs. The UPGMA clustering divided investigated varieties into four main clusters. Based on data analysis, most distant varieties with best morphological characteristics were found which will provide valuable resource of new parent's combinations in future breeding programs. This paper also provided valuable assessment of morphological characteristics to define distinctness criteria in the DUS examination of wheat.
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Aghaali, Zahra, Morteza Ghadmizadeh, Babak Abdollahi-Mandoulakani, and Iraj Bernousi. "IRAP and REMAP-based assessment of genetic diversity in chickpea collection from Iran." Genetika 46, no. 3 (2014): 731–44. http://dx.doi.org/10.2298/gensr1403731a.
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Retrotransposons (RTN) make a significant contribution to the size, organization and genetic diversity of their host genomes. Several RTN families have been identified in chickpea (Cicer arietinum L.) and other closely related species. In the current research, integration activity and insertional polymorphism of the RTNs CARE1, Tms1Ret1, TPS and LORE were studied in 64 chickpea accessions collected in Iran using inter retrotransposon amplified polymorphism (IRAP) and retrotransposon-microsatellite amplified polymorphism (REMAP) techniques. Results indicated that all RTNs studied, are transpositionally active in chickpea genome and amplified scorable and polymorphic banding pattern. Among the RTN families used, the highest percentage of polymorphic loci (PPL) was produced by TPS family (81.82%). Totally, 129 loci were amplified using 18 IRAP and REMAP primers which 83 (64.34%) were polymorphic. The Dice genetic similarity coefficients among accessions ranged from 0.84 (accessions Tj48 and Ba4) to 0.98 (accessions Ka30 and Urm61), averaging 0.93. The parameters of expected heterozygosity (He), Shannon?s information index (I) and number of effective alleles (Ne) were the highest for Urmia accessions. Cluster analysis based on UPGMA algorithm and Dice similarity coefficient categorized the 64 accessions in 7 main groups. The mean Fst values of all groups except for groups IV and VII, were lower than 0.20, demonstrating no clear differentiation among the groups, no genetic structure of the studied chickpea collection and probably occurrences of gene flow among the origins. In conclusion, although RTN-based markers were able to differentiate the chickpea accessions but the measured relative genetic similarity among accessions were not correlated with geographical distances between places of their origins.
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Ko, Yen-Fen, and Kuo-Sheng Cheng. "Semi-Siamese U-Net for separation of lung and heart bioimpedance images: A simulation study of thorax EIT." PLOS ONE 16, no. 2 (February 2, 2021): e0246071. http://dx.doi.org/10.1371/journal.pone.0246071.
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Electrical impedance tomography (EIT) is widely used for bedside monitoring of lung ventilation status. Its goal is to reflect the internal conductivity changes and estimate the electrical properties of the tissues in the thorax. However, poor spatial resolution affects EIT image reconstruction to the extent that the heart and lung-related impedance images are barely distinguishable. Several studies have attempted to tackle this problem, and approaches based on decomposition of EIT images using linear transformations have been developed, and recently, U-Net has become a prominent architecture for semantic segmentation. In this paper, we propose a novel semi-Siamese U-Net specifically tailored for EIT application. It is based on the state-of-the-art U-Net, whose structure is modified and extended, forming shared encoder with parallel decoders and has multi-task weighted losses added to adapt to the individual separation tasks. The trained semi-Siamese U-Net model was evaluated with a test dataset, and the results were compared with those of the classical U-Net in terms of Dice similarity coefficient and mean absolute error. Results showed that compared with the classical U-Net, semi-Siamese U-Net exhibited performance improvements of 11.37% and 3.2% in Dice similarity coefficient, and 3.16% and 5.54% in mean absolute error, in terms of heart and lung-impedance image separation, respectively.
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Bahadure, Nilesh Bhaskarrao, Arun Kumar Ray, and Har Pal Thethi. "Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM." International Journal of Biomedical Imaging 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/9749108.
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The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.
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Diakogiannis, Foivos I., François Waldner, and Peter Caccetta. "Looking for Change? Roll the Dice and Demand Attention." Remote Sensing 13, no. 18 (September 16, 2021): 3707. http://dx.doi.org/10.3390/rs13183707.
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Change detection, i.e., the identification per pixel of changes for some classes of interest from a set of bi-temporal co-registered images, is a fundamental task in the field of remote sensing. It remains challenging due to unrelated forms of change that appear at different times in input images. Here, we propose a deep learning framework for the task of semantic change detection in very high-resolution aerial images. Our framework consists of a new loss function, a new attention module, new feature extraction building blocks, and a new backbone architecture that is tailored for the task of semantic change detection. Specifically, we define a new form of set similarity that is based on an iterative evaluation of a variant of the Dice coefficient. We use this similarity metric to define a new loss function as well as a new, memory efficient, spatial and channel convolution Attention layer: the FracTAL. We introduce two new efficient self-contained feature extraction convolution units: the CEECNet and FracTALResNet units. Further, we propose a new encoder/decoder scheme, a network macro-topology, that is tailored for the task of change detection. The key insight in our approach is to facilitate the use of relative attention between two convolution layers in order to fuse them. We validate our approach by showing excellent performance and achieving state-of-the-art scores (F1 and Intersection over Union-hereafter IoU) on two building change detection datasets, namely, the LEVIRCD (F1: 0.918, IoU: 0.848) and the WHU (F1: 0.938, IoU: 0.882) datasets.
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Patel, Jaydeep, Adam Round, Johan Bielecki, Katerina Doerner, Henry Kirkwood, Romain Letrun, Joachim Schulz, et al. "Towards real-time analysis of liquid jet alignment in serial femtosecond crystallography." Journal of Applied Crystallography 55, no. 4 (August 1, 2022): 944–52. http://dx.doi.org/10.1107/s1600576722005891.
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Liquid sample delivery systems are used extensively for serial femtosecond crystallography at X-ray free-electron lasers (XFELs). However, misalignment of the liquid jet and the XFEL beam leads to the X-rays either partially or completely missing the sample, resulting in sample wastage and a loss of experiment time. Implemented here is an algorithm to analyse optical images using machine vision to determine whether there is overlap of the X-ray beam and liquid jet. The long-term goal is to use the output from this algorithm to implement an automated feedback mechanism to maintain constant alignment of the X-ray beam and liquid jet. The key elements of this jet alignment algorithm are discussed and its performance is characterized by comparing the results with a manual analysis of the optical image data. The success rate of the algorithm for correctly identifying hits is quantified via a similarity metric, the Dice coefficient. In total four different nozzle designs were used in this study, yielding an overall Dice coefficient of 0.98.
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Jiang, Shaofeng, Yu Wang, Xuxin Zhou, Zhen Chen, and Suhua Yang. "Brain Extraction Using Active Contour Neighborhood-Based Graph Cuts Model." Symmetry 12, no. 4 (April 4, 2020): 559. http://dx.doi.org/10.3390/sym12040559.
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The extraction of brain tissue from brain MRI images is an important pre-procedure for the neuroimaging analyses. The brain is bilaterally symmetric both in coronal plane and transverse plane, but is usually asymmetric in sagittal plane. To address the over-smoothness, boundary leakage, local convergence and asymmetry problems in many popular methods, we developed a brain extraction method using an active contour neighborhood-based graph cuts model. The method defined a new asymmetric assignment of edge weights in graph cuts for brain MRI images. The new graph cuts model was performed iteratively in the neighborhood of brain boundary named the active contour neighborhood (ACN), and was effective to eliminate boundary leakage and avoid local convergence. The method was compared with other popular methods on the Internet Brain Segmentation Repository (IBSR) and OASIS data sets. In testing cross IBSR data set (18 scans with 1.5 mm thickness), IBSR data set (20 scans with 3.1 mm thickness) and OASIS data set (77 scans with 1 mm thickness), the mean Dice similarity coefficients obtained by the proposed method were 0.957 ± 0.013, 0.960 ± 0.009 and 0.936 ± 0.018 respectively. The result obtained by the proposed method is very similar with manual segmentation and achieved the best mean Dice similarity coefficient on IBSR data. Our experiments indicate that the proposed method can provide competitively accurate results and may obtain brain tissues with sharp brain boundary from brain MRI images.
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Tanabe, Yoshinori, Takayuki Ishida, Hidetoshi Eto, Tatsuhiro Sera, and Yuki Emoto. "Evaluation of the correlation between prostatic displacement and rectal deformation using the Dice similarity coefficient of the rectum." Medical Dosimetry 44, no. 4 (2019): e39-e43. http://dx.doi.org/10.1016/j.meddos.2018.12.005.
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Cao, Ruifen, Xi Pei, Ning Ge, and Chunhou Zheng. "Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning." Technology in Cancer Research & Treatment 20 (January 1, 2021): 153303382110342. http://dx.doi.org/10.1177/15330338211034284.
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Radiotherapy plays an important role in controlling the local recurrence of esophageal cancer after radical surgery. Segmentation of the clinical target volume is a key step in radiotherapy treatment planning, but it is time-consuming and operator-dependent. This paper introduces a deep dilated convolutional U-network to achieve fast and accurate clinical target volume auto-segmentation of esophageal cancer after radical surgery. The deep dilated convolutional U-network, which integrates the advantages of dilated convolution and the U-network, is an end-to-end architecture that enables rapid training and testing. A dilated convolution module for extracting multiscale context features containing the original information on fine texture and boundaries is integrated into the U-network architecture to avoid information loss due to down-sampling and improve the segmentation accuracy. In addition, batch normalization is added to the deep dilated convolutional U-network for fast and stable convergence. In the present study, the training and validation loss tended to be stable after 40 training epochs. This deep dilated convolutional U-network model was able to segment the clinical target volume with an overall mean Dice similarity coefficient of 86.7% and a respective 95% Hausdorff distance of 37.4 mm, indicating reasonable volume overlap of the auto-segmented and manual contours. The mean Cohen kappa coefficient was 0.863, indicating that the deep dilated convolutional U-network was robust. Comparisons with the U-network and attention U-network showed that the overall performance of the deep dilated convolutional U-network was best for the Dice similarity coefficient, 95% Hausdorff distance, and Cohen kappa coefficient. The test time for segmentation of the clinical target volume was approximately 25 seconds per patient. This deep dilated convolutional U-network could be applied in the clinical setting to save time in delineation and improve the consistency of contouring.
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Cheng, Da-Chuan, Jen-Hong Chi, Shih-Neng Yang, and Shing-Hong Liu. "Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks." Sensors 20, no. 17 (August 26, 2020): 4823. http://dx.doi.org/10.3390/s20174823.
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In this study, we proposed a semi-automated and interactive scheme for organ contouring in radiotherapy planning for patients with non-small cell lung cancers. Several organs were contoured, including the lungs, airway, heart, spinal cord, body, and gross tumor volume (GTV). We proposed some schemes to automatically generate and vanish the seeds of the random walks (RW) algorithm. We considered 25 lung cancer patients, whose computed tomography (CT) images were obtained from the China Medical University Hospital (CMUH) in Taichung, Taiwan. The manual contours made by clinical oncologists were taken as the gold standard for comparison to evaluate the performance of our proposed method. The Dice coefficient between two contours of the same organ was computed to evaluate the similarity. The average Dice coefficients for the lungs, airway, heart, spinal cord, and body and GTV segmentation were 0.92, 0.84, 0.83, 0.73, 0.85 and 0.66, respectively. The computation time was between 2 to 4 min for a whole CT sequence segmentation. The results showed that our method has the potential to assist oncologists in the process of radiotherapy treatment in the CMUH, and hopefully in other hospitals as well, by saving a tremendous amount of time in contouring.
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Yakubu, D. E., F. J. R. Abadi, and T. H. Pennington. "Molecular epidemiology of recent United Kingdom isolates ofNeisseria meningitidisserogroup C." Epidemiology and Infection 113, no. 1 (August 1994): 53–65. http://dx.doi.org/10.1017/s0950268800051463.
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SUMMARYThe genomes of 34 recent United Kingdom isolates ofNeisseria meningitidisserogroup C were examined by restriction enzyme digestion and by conventional and pulsed-field gel electrophoresis (PFGE). Strains were assigned to groups on the basis of the Dice similarity coefficient; strains with values of >92% were considered to be clonally related. Twelve clones were identified by PFGE. Strains of two clonal groups predominated. Restriction endonuclease analyses using the ‘high frequency cleavage’ endonucleaseStuI and conventional electrophoresis gave 11 groups; in general it had lower resolving power than PFGE.
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Muhadi, Nur, Ahmad Abdullah, Siti Bejo, Muhammad Mahadi, and Ana Mijic. "Image Segmentation Methods for Flood Monitoring System." Water 12, no. 6 (June 26, 2020): 1825. http://dx.doi.org/10.3390/w12061825.
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Flood disasters are considered annual disasters in Malaysia due to their consistent occurrence. They are among the most dangerous disasters in the country. Lack of data during flood events is the main constraint to improving flood monitoring systems. With the rapid development of information technology, flood monitoring systems using a computer vision approach have gained attention over the last decade. Computer vision requires an image segmentation technique to understand the content of the image and to facilitate analysis. Various segmentation algorithms have been developed to improve results. This paper presents a comparative study of image segmentation techniques used in extracting water information from digital images. The segmentation methods were evaluated visually and statistically. To evaluate the segmentation methods statistically, the dice similarity coefficient and the Jaccard index were calculated to measure the similarity between the segmentation results and the ground truth images. Based on the experimental results, the hybrid technique obtained the highest values among the three methods, yielding an average of 97.70% for the dice score and 95.51% for the Jaccard index. Therefore, we concluded that the hybrid technique is a promising segmentation method compared to the others in extracting water features from digital images.
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Aboussaleh, Ilyasse, Jamal Riffi, Adnane Mohamed Mahraz, and Hamid Tairi. "Brain Tumor Segmentation Based on Deep Learning’s Feature Representation." Journal of Imaging 7, no. 12 (December 8, 2021): 269. http://dx.doi.org/10.3390/jimaging7120269.
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Brain tumor is considered as one of the most serious causes of death in the world. Thus, it is very important to detect it as early as possible. In order to predict and segment the tumor, many approaches have been proposed. However, they suffer from different problems such as the necessity of the intervention of a specialist, the long required run-time and the choice of the appropriate feature extractor. To address these issues, we proposed an approach based on convolution neural network architecture aiming at predicting and segmenting simultaneously a cerebral tumor. The proposal was divided into two phases. Firstly, aiming at avoiding the use of the labeled image that implies a subject intervention of the specialist, we used a simple binary annotation that reflects the existence of the tumor or not. Secondly, the prepared image data were fed into our deep learning model in which the final classification was obtained; if the classification indicated the existence of the tumor, the brain tumor was segmented based on the feature representations generated by the convolutional neural network architectures. The proposed method was trained on the BraTS 2017 dataset with different types of gliomas. The achieved results show the performance of the proposed approach in terms of accuracy, precision, recall and Dice similarity coefficient. Our model showed an accuracy of 91% in tumor classification and a Dice similarity coefficient of 82.35% in tumor segmentation.
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Dionisio, Fernando Carrasco Ferreira, Larissa Santos Oliveira, Mateus de Andrade Hernandes, Edgard Eduard Engel, Paulo Mazzoncini de Azevedo-Marques, and Marcello Henrique Nogueira-Barbosa. "Manual versus semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging: evaluation of similarity and comparison of segmentation times." Radiologia Brasileira 54, no. 3 (June 2021): 155–64. http://dx.doi.org/10.1590/0100-3984.2020.0028.
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Abstract Objective: To evaluate the degree of similarity between manual and semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging (MRI). Materials and Methods: This was a retrospective study of 15 MRI examinations of patients with histopathologically confirmed soft-tissue sarcomas acquired before therapeutic intervention. Manual and semiautomatic segmentations were performed by three radiologists, working independently, using the software 3D Slicer. The Dice similarity coefficient (DSC) and the Hausdorff distance were calculated in order to evaluate the similarity between manual and semiautomatic segmentation. To compare the two modalities in terms of the tumor volumes obtained, we also calculated descriptive statistics and intraclass correlation coefficients (ICCs). Results: In the comparison between manual and semiautomatic segmentation, the DSC values ranged from 0.871 to 0.973. The comparison of the volumes segmented by the two modalities resulted in ICCs between 0.9927 and 0.9990. The DSC values ranged from 0.849 to 0.979 for intraobserver variability and from 0.741 to 0.972 for interobserver variability. There was no significant difference between the semiautomatic and manual modalities in terms of the segmentation times (p > 0.05). Conclusion: There appears to be a high degree of similarity between manual and semiautomatic segmentation, with no significant difference between the two modalities in terms of the time required for segmentation.
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Zhang, Yanshan, and Yuru Tian. "A New Active Contour Medical Image Segmentation Method Based on Fractional Varying-Order Differential." Mathematics 10, no. 2 (January 10, 2022): 206. http://dx.doi.org/10.3390/math10020206.
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Image segmentation technology is dedicated to the segmentation of intensity inhomogeneous at present. In this paper, we propose a new method that incorporates fractional varying-order differential and local fitting energy to construct a new variational level set active contour model. The energy functions in this paper mainly include three parts: the local term, the regular term and the penalty term. The local term combined with fractional varying-order differential can obtain more details of the image. The regular term is used to regularize the image contour length. The penalty term is used to keep the evolution curve smooth. True positive (TP) rate, false positive (FP) rate, precision (P) rate, Jaccard similarity coefficient (JSC), and Dice similarity coefficient (DSC) are employed as the comparative measures for the segmentation results. Experimental results for both synthetic and real images show that our method has more accurate segmentation results than other models, and it is robust to intensity inhomogeneous or noises.
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Ibrahim, Arsmah, Zainab Abu Bakar, Nuru’l–‘Izzah Othman, and Nor Fuzaina Ismail. "Assessing the Line-By-Line Marking Performance of n-Gram String Similarity Method." Scientific Research Journal 6, no. 1 (June 30, 2009): 15. http://dx.doi.org/10.24191/srj.v6i1.5636.
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Manual marking of free-response solutions in mathematics assessments is very demanding in terms of time and effort. Available software equipped with automated marking features to mark open-ended questions has very limited capabilities. In most cases the marking process focuses on the final answer only. Few available software are capable of marking the intermediate steps as is norm in manual marking. This paper discusses the line-by-line marking performance of the n_gram string similarity method using the Dice coefficient as means to measure similarity. The marks awarded by the automated marking process are compared with marks awarded by manual marking. Marks awarded by manual marking are used as the benchmark to gauge the performance of the automated marking technique in terms of its closeness to manual marking.
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Jiao, Han, Xinhua Jiang, Zhiyong Pang, Xiaofeng Lin, Yihua Huang, and Li Li. "Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI." Computational and Mathematical Methods in Medicine 2020 (May 5, 2020): 1–12. http://dx.doi.org/10.1155/2020/2413706.
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Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper, deep convolutional neural networks (DCNN) were employed for breast segmentation and mass detection in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). First, the region of the breasts was segmented from the remaining body parts by building a fully convolutional neural network based on U-Net++. Using the method of deep learning to extract the target area can help to reduce the interference external to the breast. Second, a faster region with convolutional neural network (Faster RCNN) was used for mass detection on segmented breast images. The dataset of DCE-MRI used in this study was obtained from 75 patients, and a 5-fold cross validation method was adopted. The statistical analysis of breast region segmentation was carried out by computing the Dice similarity coefficient (DSC), Jaccard coefficient, and segmentation sensitivity. For validation of breast mass detection, the sensitivity with the number of false positives per case was computed and analyzed. The Dice and Jaccard coefficients and the segmentation sensitivity value for breast region segmentation were 0.951, 0.908, and 0.948, respectively, which were better than those of the original U-Net algorithm, and the average sensitivity for mass detection achieved 0.874 with 3.4 false positives per case.
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Almajalid, Rania, Ming Zhang, and Juan Shan. "Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI." Diagnostics 12, no. 1 (January 6, 2022): 123. http://dx.doi.org/10.3390/diagnostics12010123.
Full textAbstract:
In the medical sector, three-dimensional (3D) images are commonly used like computed tomography (CT) and magnetic resonance imaging (MRI). The 3D MRI is a non-invasive method of studying the soft-tissue structures in a knee joint for osteoarthritis studies. It can greatly improve the accuracy of segmenting structures such as cartilage, bone marrow lesion, and meniscus by identifying the bone structure first. U-net is a convolutional neural network that was originally designed to segment the biological images with limited training data. The input of the original U-net is a single 2D image and the output is a binary 2D image. In this study, we modified the U-net model to identify the knee bone structures using 3D MRI, which is a sequence of 2D slices. A fully automatic model has been proposed to detect and segment knee bones. The proposed model was trained, tested, and validated using 99 knee MRI cases where each case consists of 160 2D slices for a single knee scan. To evaluate the model’s performance, the similarity, dice coefficient (DICE), and area error metrics were calculated. Separate models were trained using different knee bone components including tibia, femur, patella, as well as a combined model for segmenting all the knee bones. Using the whole MRI sequence (160 slices), the method was able to detect the beginning and ending bone slices first, and then segment the bone structures for all the slices in between. On the testing set, the detection model accomplished 98.79% accuracy and the segmentation model achieved DICE 96.94% and similarity 93.98%. The proposed method outperforms several state-of-the-art methods, i.e., it outperforms U-net by 3.68%, SegNet by 14.45%, and FCN-8 by 2.34%, in terms of DICE score using the same dataset.
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Vargas-Bedoya, Eliseo, Juan Carlos Rivera, Maria Eugenia Puerta, Aurelio Angulo, Niklas Wahl, and Gonzalo Cabal. "Contour Propagation for Radiotherapy Treatment Planning Using Nonrigid Registration and Parameter Optimization: Case Studies in Liver and Breast Cancer." Applied Sciences 12, no. 17 (August 26, 2022): 8523. http://dx.doi.org/10.3390/app12178523.
Full textAbstract:
Radiotherapy treatments are carried out using computerized axial tomography. In radiation therapy planning, the radiation oncologist must do a manual segmentation of volumes of interest to delineate the organs that should be irradiated. This way of carrying out the process generates long execution times and introduces a subjective component. In this study, a contour-propagation algorithm is formulated to automate the segmentation, based on elastic registration or nonrigid demon registration. A heuristic algorithm to find the parameters that optimize the registration is also proposed. The parameters found along with the contour-propagation algorithm are able to estimate contours of scans with Dice similarity coefficients (DSC) greater than 0.92 and maintain stability with B-spline registration, which takes in the parameters found as input. The study allows for validating the results using the correlation coefficient (CC) to compare the similarity between the voxels’ gray-scale intensity of the estimated tomography and the original tomography, obtaining values greater than 0.96. These values were validated under medical criteria and applied to liver and breast CT scans, indicating good performance for radiation therapy planning.
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Rathna Priya, T. S., and Annamalai Manickavasagan. "Evaluation of segmentation methods for RGB colour image-based detection of Fusarium infection in corn grains using support vector machine (SVM) and pre-trained convolution neural network (CNN)." Canadian Biosystems Engineering 64, no. 1 (December 31, 2022): 7.09–7.20. http://dx.doi.org/10.7451/cbe.2022.64.7.9.
Full textAbstract:
This study evaluated six segmentation methods (clustering, flood-fill, graph-cut, colour-thresholding, watershed, and Otsu’s-thresholding) for segmentation accuracy and classification accuracy in discriminating Fusarium infected corn grains using RGB colour images. The segmentation accuracy was calculated using Jaccard similarity index and Dice coefficient in comparison with the gold standard (manual segmentation method). Flood-fill and graph-cut methods showed the highest segmentation accuracy of 77% and 87% for Jaccard and Dice evaluation metrics, respectively. Pre-trained convolution neural network (CNN) and support vector machine (SVM) were used to evaluate the effect of segmentation methods on classification accuracy using segmented images and extracted features from the segmented images, respectively. The SVM based two-class model to discriminate healthy and Fusarium infected corn grains yielded the classification accuracy of 84%, 79%, 78%, 74%, 69% and 65% for graph-cut, watershed, clustering, flood-fill, colour-thresholding, and Otsu’s-thresholding, respectively. In pretrained CNN model, the classification accuracies were 93%, 88%, 87%, 84%, 61% and 59% for flood-fill, graph-cut, colour-thresholding, clustering, watershed, and Otsu’s-thresholding, respectively. Jaccard and Dice evaluation metrics showed the highest correlation with the pretrained CNN classification accuracies with R2 values of 0.9693 and 0.9727, respectively. The correlation with SVM classification accuracies were R2–0.505 for Jaccard and R2–0.5151 for Dice evaluation metrics.
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Góes, Larissa Brandão, Ana Bolena Lima da Costa, Laurineide Lopes de Carvalho Freire, and Neiva Tinti de Oliveira. "Randomly Amplified Polymorphic DNA of Trichoderma isolates and antagonism against Rhizoctonia solani." Brazilian Archives of Biology and Technology 45, no. 2 (June 2002): 151–60. http://dx.doi.org/10.1590/s1516-89132002000200005.
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Random Amplified Polymorphic DNA (RAPD) procedure was used to examine the genetic variability among fourteen isolates of Trichoderma and their ability to antagonize Rhizoctonia solani using a dual-culture assay for correlation among RAPD products and their hardness to R. solani. Seven oligodeoxynucleotide primers were selected for the RAPD assays which resulted in 197 bands for 14 isolates of Trichoderma. The data were entered into a binary matrix and a similarity matrix was constructed using DICE similarity (SD) index. A UPGMA cluster based on SD values was generated using NTSYS (Numerical Taxonomy System, Applied Biostatistics) computer program. A mean coefficient of similarity obtained for pairwise comparisons among the most antagonics isolates was around 40%. The results presented here showed that the variability among the isolates of Trichoderma was very high. No relationship was found between the polymorphism showed by the isolates and their hardness, origin and substrata.
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Agrawal, Ritu, Manisha Sharma, and Bikesh Kumar Singh. "Segmentation of Brain Tumour Based on Clustering Technique: Performance Analysis." Journal of Intelligent Systems 28, no. 2 (April 24, 2019): 291–306. http://dx.doi.org/10.1515/jisys-2017-0027.
Full textAbstract:
AbstractManual detection and analysis of brain tumours is an exhaustive and time-consuming process. Further, it is subject to intra-observer and inter-observer variabilities. Automated brain tumour segmentation and analysis has thus gained much attention in recent years. However, the existing segmentation techniques do not meet the requirements of real-time use due to limitations posed by poor image quality and image complexity. This article proposes a hybrid approach for image segmentation by combining biorthogonal wavelet transform, skull stripping, fuzzy c-means threshold clustering, Canny edge detection, and morphological operations. Biorthogonal wavelet transform and skull stripping are essential pre-processing steps for analysis of brain images. Initially, biorthogonal wavelet transform is used to remove impulsive noise and skull stripping is employed to eliminate non-cerebral tissue regions from the acquired images, followed by segmentation using fuzzy c-means threshold clustering, Canny edge detection, and morphological processing. The performance of the proposed automated system is tested on standard datasets using performance measures such as Jaccard index, Dice similarity coefficient, execution time, and entropy. The proposed method achieves a Jaccard index and Dice similarity coefficient of 0.886 and 0.935, respectively, which indicate better overlap between the automated segmentation method and manual segmentation method performed by an expert radiologist. The average execution time and average entropy values obtained are 1.001 s and 0.202, respectively. The results obtained are discussed in view of some reported studies in terms of execution time and tumour area. Further work is needed to evaluate the proposed method in routine clinical practice and its effect on radiologists’ performances.