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Journal articles on the topic 'Semi-automatic classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Gurulingappa, Harsha, Corinna Kolářik, Martin Hofmann-Apitius, and Juliane Fluck. "Concept-Based Semi-Automatic Classification of Drugs." Journal of Chemical Information and Modeling 49, no. 8 (August 10, 2009): 1986–92. http://dx.doi.org/10.1021/ci9000844.

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Lee, Won-Goo, Sung-Ho Shin, Kwang-Young Kim, Do-Heon Jeon, Hwa-Mook Yoon, Won-Kyung Sung, and Min-Ho Lee. "Semi-Automatic Management of Classification Scheme with Interoperability." Journal of the Korea Contents Association 11, no. 12 (December 28, 2011): 466–74. http://dx.doi.org/10.5392/jkca.2011.11.12.466.

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Yang, Jie, and Xianzhong Zhou. "Semi-automatic Web Service Classification Using Machine Learning." International Journal of u- and e-Service, Science and Technology 8, no. 4 (April 30, 2015): 339–48. http://dx.doi.org/10.14257/ijunesst.2015.8.4.31.

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Kockelkorn, Thessa T. J. P., Pim A. de Jong, Cornelia M. Schaefer-Prokop, Rianne Wittenberg, Audrey M. Tiehuis, Hester A. Gietema, Jan C. Grutters, Max A. Viergever, and Bram van Ginneken. "Semi-automatic classification of textures in thoracic CT scans." Physics in Medicine and Biology 61, no. 16 (July 20, 2016): 5906–24. http://dx.doi.org/10.1088/0031-9155/61/16/5906.

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Chen, Yung-Fu, Po-Chi Huang, Ker-Cheng Lin, Hsuan-Hung Lin, Li-En Wang, Chung-Chuan Cheng, Tsung-Po Chen, Yung-Kuan Chan, and John Y. Chiang. "Semi-Automatic Segmentation and Classification of Pap Smear Cells." IEEE Journal of Biomedical and Health Informatics 18, no. 1 (January 2014): 94–108. http://dx.doi.org/10.1109/jbhi.2013.2250984.

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Pereira, Tiago Rafael de Barros, Helenice Vital, André Giskard Aquino da Silva, and Cecília Alves de Oliveira. "COMPARISON OF AUTOMATIC AND SEMI-AUTOMATIC METHODS FOR CLASSIFICATION OF SIDE SCAN SONAR IMAGERY." Brazilian Journal of Geophysics 37, no. 1 (March 27, 2019): 1. http://dx.doi.org/10.22564/rbgf.v37i1.1985.

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ABSTRACT. The main scope of this paper is the analysis of seafloor classification using acoustic remote sensing data. These data were acquired in a hydroacoustic survey of bathymetry and sonography using an interferometric swath bathymetry system. The study area is a sector of the internal northeast Brazilian shelf adjacent to the Ponta Negra beach - Natal (RN). The objective of the work is to identify and draw the different textural patterns, which characterize the seafloor of the study area. In addition, two approaches for textural classification of sonograms were compared and evaluated, which were: Automatic Gray Level Co-occurrence Matrix (GLCM) classification available in SonarWiz software; and the semi-automatic Maximum Likelihood, available in ArcGIS software. The comparison tested the capacity for identifying and drawing the textural patterns distribution. The automated classification identified 4 patterns while on the semi-automated 5 patterns were identified. It was made the correlation between the textural patterns found in each classification, besides the correlation between textural patterns and the levels of intensity of reflectance presents on the sonogram.Keywords: sonography, textural classification, textural patterns, hydroacoustic. RESUMO. Este trabalho foi realizado a partir da análise de dados geofísicos adquiridos em levantamento hidroacústico de batimetria e sonografia utilizando um sonar interferométrico EdgeTech 4600. A área de estudo é uma porção da plataforma interna do nordeste brasileiro adjacente Natal (RN). O objetivo deste trabalho é identificar e delimitar os diferentes padrões texturais que caracterizam o substrato marinho da área de estudo. Adicionalmente, são avaliadas e comparadas duas abordagens distintas de classificação textural de sonogramas, sendo elas: a classificação automática GLCM disponível no software SonarWiz, e a classificação semi-automática máxima verossimilhança (Maximum Likelihood) disponível no software ArcGIS. A comparação foi realizada com base na capacidade de identificação e delimitação da distribuição dos padrões texturais. A utilização da classificação automática identificou 4 padrões, enquanto que, na classificação semi-automática 6 padrões foram identificados. Foi feita a correlação entre os padrões texturais encontrados em cada classificação, além da correlação entre os padrões texturais e os níveis de intensidade de reflectância presente no sonograma.Palavras-chave: sonografia, classificação textural, padrões de textura, hidroacústica.

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Jenerowicz, M., and M. Banaszkiewicz. "ASTEROID (21) LUTETIA: SEMI-AUTOMATIC IMPACT CRATERS DETECTION AND CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 479–86. http://dx.doi.org/10.5194/isprs-archives-xlii-2-479-2018.

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The need to develop an automated method, independent of lighting and surface conditions, for the identification and measurement of impact craters, as well as the creation of a reliable and efficient tool, has become a justification of our studies. This paper presents a methodology for the detection of impact craters based on their spectral and spatial features. The analysis aims at evaluation of the algorithm capabilities to determinate the spatial parameters of impact craters presented in a time series. In this way, time-consuming visual interpretation of images would be reduced to the special cases. The developed algorithm is tested on a set of OSIRIS high resolution images of asteroid Lutetia surface which is characterized by varied landforms and the abundance of craters created by collisions with smaller bodies of the solar system.The proposed methodology consists of three main steps: characterisation of objects of interest on limited set of data, semi-automatic extraction of impact craters performed for total set of data by applying the Mathematical Morphology image processing (Serra, 1988, Soille, 2003), and finally, creating libraries of spatial and spectral parameters for extracted impact craters, i.e. the coordinates of the crater center, semi-major and semi-minor axis, shadow length and cross-section. The overall accuracy of the proposed method is 98&thinsp;%, the Kappa coefficient is 0.84, the correlation coefficient is &sim;&thinsp;0.80, the omission error 24.11&thinsp;%, the commission error 3.45&thinsp;%. The obtained results show that methods based on Mathematical Morphology operators are effective also with a limited number of data and low-contrast images.

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Chen, Zhixin, and Robert C. Maher. "Semi-automatic classification of bird vocalizations using spectral peak tracks." Journal of the Acoustical Society of America 120, no. 5 (November 2006): 2974–84. http://dx.doi.org/10.1121/1.2345831.

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Wong, M. L. Dennis, and Asoke K. Nandi. "Semi-blind algorithms for automatic classification of digital modulation schemes." Digital Signal Processing 18, no. 2 (March 2008): 209–27. http://dx.doi.org/10.1016/j.dsp.2007.02.007.

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Duangsuwan, Jarunee, and Pawin Saeku. "Semi-automatic Classification Based on ICD Code for Thai Text-Based Chief Complaint by Machine Learning Techniques." International Journal of Future Computer and Communication 7, no. 2 (June 2018): 37–41. http://dx.doi.org/10.18178/ijfcc.2018.7.2.517.

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Grimova, Nela, and Martin Macas. "Query-By-Committee Framework Used for Semi-Automatic Sleep Stages Classification." Proceedings 31, no. 1 (November 21, 2019): 80. http://dx.doi.org/10.3390/proceedings2019031080.

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Active learning is very useful for classification problems where it is hard or time-consuming to acquire classes of data in order to create a subset for training a classifier. The classification of over-night polysomnography records to sleep stages is an example of such application because an expert has to annotate a large number of segments of a record. Active learning methods enable us to iteratively select only the most informative instances for the manual classification so the total expert’s effort is reduced. However, the process is able to be insufficiently initialised because of a large dimensionality of polysomnography (PSG) data, so the fast convergence of active learning is at risk. In order to prevent this threat, we have proposed a variant of the query-by-committee active learning scenario which take into account all features of data so it is not necessary to reduce a feature space, but the process is quickly initialised. The proposed method is compared to random sampling and margin uncertainty sampling which is another well-known active learning method. It was shown that, during crucial first iteration of the process, the provided variant of query-by-committee acquired the best results among other strategies in most cases.

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Nunes, Bernardo Pereira, and Marco Antonio Casanova. "A Frame-Based System for Automatic Classification of Semi- Structured Data." Revista de Informática Teórica e Aplicada 16, no. 2 (March 31, 2010): 87–92. http://dx.doi.org/10.22456/2175-2745.12579.

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Kaur, Sukhvir, Shreelekha Pandey, and Shivani Goel. "Semi-automatic leaf disease detection and classification system for soybean culture." IET Image Processing 12, no. 6 (June 1, 2018): 1038–48. http://dx.doi.org/10.1049/iet-ipr.2017.0822.

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Chen, Zezhi, and Tim Ellis. "Semi-automatic annotation samples for vehicle type classification in urban environments." IET Intelligent Transport Systems 9, no. 3 (April 1, 2015): 240–49. http://dx.doi.org/10.1049/iet-its.2013.0150.

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Abid, Adnan, Waqas Ali, Muhammad Shoaib Farooq, Uzma Farooq, Nabeel Sabir Khan, and Kamran Abid. "Semi-Automatic Classification and Duplicate Detection From Human Loss News Corpus." IEEE Access 8 (2020): 97737–47. http://dx.doi.org/10.1109/access.2020.2995789.

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Zheng, Jiamin, Jiawei Cao, Zixian Wang, Fan Liu, Sihan Wang, Tianying Wu, Chen Yang, Kaifa Zhao, Yizhang Jiang, and Pengjiang Qian. "Semi-Automatic Synthetic Computed Tomography Generation for Abdomens Using Transfer Learning and Semi-Supervised Classification." Journal of Medical Imaging and Health Informatics 9, no. 9 (December 1, 2019): 1878–86. http://dx.doi.org/10.1166/jmihi.2019.2809.

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Hayman Oo, Khin, Azlin Nordin, Amelia Ritahani Ismail, and Suriani Sulaiman. "An Analysis of Ambiguity Detection Techniques for Software Requirements Specification (SRS)." International Journal of Engineering & Technology 7, no. 2.29 (May 22, 2018): 501. http://dx.doi.org/10.14419/ijet.v7i2.29.13808.

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Ambiguity is the major problem in Software Requirements Specification (SRS) documents because most of the SRS documents are written in natural language and natural language is generally ambiguous. There are various types of techniques that have been used to detect ambiguity in SRS documents. Based on an analysis of the existing work, the ambiguity detection techniques can be categorized into three approaches: (1) manual approach, (2) semi-automatic approach using natural language processing, (3) semi-automatic approach using machine learning. Among them, one of the semi-automatic approaches that uses the Naïve Bayes (NB) text classification technique obtained high accuracy and performed effectively in detecting ambiguities in SRS.

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Dell’Acqua, F., P. Gamba, and G. Trianni. "Semi-automatic choice of scale-dependent features for satellite SAR image classification." Pattern Recognition Letters 27, no. 4 (March 2006): 244–51. http://dx.doi.org/10.1016/j.patrec.2005.08.005.

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Haara, Arto, and Mika Haarala. "Tree Species Classification using Semi-automatic Delineation of Trees on Aerial Images." Scandinavian Journal of Forest Research 17, no. 6 (January 2002): 556–65. http://dx.doi.org/10.1080/02827580260417215.

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Wang, Yu, Guan Gui, Haris Gacanin, Tomoaki Ohtsuki, Hikmet Sari, and Fumiyuki Adachi. "Transfer Learning for Semi-Supervised Automatic Modulation Classification in ZF-MIMO Systems." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 10, no. 2 (June 2020): 231–39. http://dx.doi.org/10.1109/jetcas.2020.2992128.

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Tachibana, Ryosuke O., Naoya Oosugi, and Kazuo Okanoya. "Semi-Automatic Classification of Birdsong Elements Using a Linear Support Vector Machine." PLoS ONE 9, no. 3 (March 21, 2014): e92584. http://dx.doi.org/10.1371/journal.pone.0092584.

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Fitri, L. A., Y. Warty, F. Haryanto, U. Fauzi, and F. D. E. Latief. "Classification of urinary stones based on edge detection using semi-automatic threshold." Journal of Physics: Conference Series 1248 (June 2019): 012025. http://dx.doi.org/10.1088/1742-6596/1248/1/012025.

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23

Song, Young Ju, Hyo Sung Kwak, Gyung Ho Chung, and Seongil Jo. "Quantification of Carotid Intraplaque Hemorrhage: Comparison between Manual Segmentation and Semi-Automatic Segmentation on Magnetization-Prepared Rapid Acquisition with Gradient-Echo Sequences." Diagnostics 9, no. 4 (November 11, 2019): 184. http://dx.doi.org/10.3390/diagnostics9040184.

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Purpose: Carotid intraplaque hemorrhage (IPH) increases risk of territorial cerebral ischemic events, but different sequences or criteria have been used to diagnose or quantify carotid IPH. The purpose of this study was to compare manual segmentation and semi-automatic segmentation for quantification of carotid IPH on magnetization-prepared rapid acquisition with gradient-echo (MPRAGE) sequences. Methods: Forty patients with 16–79% carotid stenosis and IPH on MPRAGE sequences were reviewed by two trained radiologists with more than five years of specialized experience in carotid plaque characterization with carotid plaque MRI. Initially, the radiologists manually viewed the IPH based on the MPRAGE sequence. IPH volume was then measured by three different semi-automatic methods, with high signal intensity 150%, 175%, and 200%, respectively, above that of adjacent muscle on the MPRAGE sequence. Agreement on measurements between manual segmentation and semi-automatic segmentation was assessed using the intraclass correlation coefficient (ICC). Results: There was near-perfect agreement between manual segmentation and the 150% and 175% criteria for semi-automatic segmentation in quantification of IPH volume. The ICC of each semi-automatic segmentation were as follows: 150% criteria: 0.861, 175% criteria: 0.809, 200% criteria: 0.491. The ICC value of manual vs. 150% criteria and manual vs. 175% criteria were significantly better than the manual vs. 200% criteria (p < 0.001). Conclusions: The ICC of 150% and 175% criteria for semi-automatic segmentation are more reliable for quantification of IPH volume. Semi-automatic classification tools may be beneficial in large-scale multicenter studies by reducing image analysis time and avoiding bias between human reviewers.

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Meuschke, M., B. Köhler, U. Preim, B. Preim, and K. Lawonn. "Semi-automatic Vortex Flow Classification in 4D PC-MRI Data of the Aorta." Computer Graphics Forum 35, no. 3 (June 2016): 351–60. http://dx.doi.org/10.1111/cgf.12911.

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Bronge, Laine Boresjö, and Barbro Näslund-Landenmark. "Wetland classification for Swedish CORINE Land Cover adopting a semi-automatic interactive approach." Canadian Journal of Remote Sensing 28, no. 2 (January 2002): 139–55. http://dx.doi.org/10.5589/m02-011.

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Chen, Lili, and Huoyao Xu. "Deep neural network for semi-automatic classification of term and preterm uterine recordings." Artificial Intelligence in Medicine 105 (May 2020): 101861. http://dx.doi.org/10.1016/j.artmed.2020.101861.

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Alok, Abhay Kumar, Sriparna Saha, and Asif Ekbal. "Multi-objective semi-supervised clustering for automatic pixel classification from remote sensing imagery." Soft Computing 20, no. 12 (May 16, 2015): 4733–51. http://dx.doi.org/10.1007/s00500-015-1701-x.

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Beyreuther, M., and J. Wassermann. "Hidden semi-Markov Model based earthquake classification system using Weighted Finite-State Transducers." Nonlinear Processes in Geophysics 18, no. 1 (February 14, 2011): 81–89. http://dx.doi.org/10.5194/npg-18-81-2011.

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Abstract. Automatic earthquake detection and classification is required for efficient analysis of large seismic datasets. Such techniques are particularly important now because access to measures of ground motion is nearly unlimited and the target waveforms (earthquakes) are often hard to detect and classify. Here, we propose to use models from speech synthesis which extend the double stochastic models from speech recognition by integrating a more realistic duration of the target waveforms. The method, which has general applicability, is applied to earthquake detection and classification. First, we generate characteristic functions from the time-series. The Hidden semi-Markov Models are estimated from the characteristic functions and Weighted Finite-State Transducers are constructed for the classification. We test our scheme on one month of continuous seismic data, which corresponds to 370 151 classifications, showing that incorporating the time dependency explicitly in the models significantly improves the results compared to Hidden Markov Models.

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Piloyan, Artak, and Milan Konečný. "Semi-Automated Classification of Landform Elements in Armenia Based on SRTM DEM using K-Means Unsupervised Classification." Quaestiones Geographicae 36, no. 1 (March 1, 2017): 93–103. http://dx.doi.org/10.1515/quageo-2017-0007.

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Abstract Land elements have been used as basic landform descriptors in many science disciplines, including soil mapping, vegetation mapping, and landscape ecology. This paper presents a semi-automatic method based on k-means unsupervised classification to analyze geomorphometric features as landform elements in Armenia. First, several data layers were derived from DEM: elevation, slope, profile curvature, plan curvature and flow path length. Then, k-means algorithm has been used for classifying landform elements based on these morphomertic parameters. The classification has seven landform classes. Overall, landform classification is performed in the form of a three-level hierarchical scheme. The resulting map reflects the general topography and landform character of Armenia.

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Sandstedt, Mårten, Lilian Henriksson, Magnus Janzon, Gusten Nyberg, Jan Engvall, Jakob De Geer, Joakim Alfredsson, and Anders Persson. "Evaluation of an AI-based, automatic coronary artery calcium scoring software." European Radiology 30, no. 3 (November 14, 2019): 1671–78. http://dx.doi.org/10.1007/s00330-019-06489-x.

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Abstract Objectives To evaluate an artificial intelligence (AI)–based, automatic coronary artery calcium (CAC) scoring software, using a semi-automatic software as a reference. Methods This observational study included 315 consecutive, non-contrast-enhanced calcium scoring computed tomography (CSCT) scans. A semi-automatic and an automatic software obtained the Agatston score (AS), the volume score (VS), the mass score (MS), and the number of calcified coronary lesions. Semi-automatic and automatic analysis time were registered, including a manual double-check of the automatic results. Statistical analyses were Spearman’s rank correlation coefficient (⍴), intra-class correlation (ICC), Bland Altman plots, weighted kappa analysis (κ), and Wilcoxon signed-rank test. Results The correlation and agreement for the AS, VS, and MS were ⍴ = 0.935, 0.932, 0.934 (p < 0.001), and ICC = 0.996, 0.996, 0.991, respectively (p < 0.001). The correlation and agreement for the number of calcified lesions were ⍴ = 0.903 and ICC = 0.977 (p < 0.001), respectively. The Bland Altman mean difference and 1.96 SD upper and lower limits of agreements for the AS, VS, and MS were − 8.2 (− 115.1 to 98.2), − 7.4 (− 93.9 to 79.1), and − 3.8 (− 33.6 to 25.9), respectively. Agreement in risk category assignment was 89.5% and κ = 0.919 (p < 0.001). The median time for the semi-automatic and automatic method was 59 s (IQR 35–100) and 36 s (IQR 29–49), respectively (p < 0.001). Conclusions There was an excellent correlation and agreement between the automatic software and the semi-automatic software for three CAC scores and the number of calcified lesions. Risk category classification was accurate but showing an overestimation bias tendency. Also, the automatic method was less time-demanding. Key Points • Coronary artery calcium (CAC) scoring is an excellent candidate for artificial intelligence (AI) development in a clinical setting. • An AI-based, automatic software obtained CAC scores with excellent correlation and agreement compared with a conventional method but was less time-consuming.

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Lakhmi Prasanna, P., D. Rajeswara Rao, Y. Meghana, K. Maithri, and T. Dhinesh. "Analysis of supervised classification techniques." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 283. http://dx.doi.org/10.14419/ijet.v7i1.1.9486.

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As the number of digital documents and data are being increased rapidly, it is important to classify them in to respective categories. This process of classifying the data is called classification. There are three ways in to which the data can be classified un supervised, supervised and semi supervised methods. Automatic Text Classification is done by supervised learning techniques. This paper discusses about various classification techniques, their advantages and limitations. Finally, it concludes with the best classification technique. In this paper the best classification technique that was proposed is Artificial Neural Network (ANN). The reason for proposing ANN as the best algorithm is given and its application in various important fields was given.

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Amin, Ali Hama, Sirish Shrestha, Marton Tokodi, Muhammad Ashraf, Grace Verzosa, Mokoto Amaki, Haruhiko Abe, and Partho P. Sengupta. "SEMI-AUTOMATIC CLASSIFICATION OF CARDIAC BLOOD-FLOW FIELDS USING ECHOCARDIOGRAPHIC PARTICLE IMAGING VELOCIMETRY DATA." Journal of the American College of Cardiology 71, no. 11 (March 2018): A1659. http://dx.doi.org/10.1016/s0735-1097(18)32200-9.

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Pedersen, G. B. M. "Semi-automatic classification of glaciovolcanic landforms: An object-based mapping approach based on geomorphometry." Journal of Volcanology and Geothermal Research 311 (February 2016): 29–40. http://dx.doi.org/10.1016/j.jvolgeores.2015.12.015.

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Gao, Yan, Prashanth Marpu, Imgard Niemeyer, Daniel Miller Runfola, Nicholas M. Giner, Thomas Hamill, and Robert Gilmore Pontius. "Object-based classification with features extracted by a semi-automatic feature extraction algorithm – SEaTH." Geocarto International 26, no. 3 (June 2011): 211–26. http://dx.doi.org/10.1080/10106049.2011.556754.

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Gao, Yan, Prashanth Marpu, Imgard Niemeyer, Daniel Miller Runfola, Nicholas M. Giner, Thomas Hamill, and Robert Gilmore Pontius. "Object-based classification with features extracted by a semi-automatic feature extraction algorithm – SEaTH." Geocarto International 26, no. 5 (August 2011): 413. http://dx.doi.org/10.1080/10106049.2011.584726.

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Jiang, Dong, Yaohuan Huang, Dafang Zhuang, Yunqiang Zhu, Xinliang Xu, and Hongyan Ren. "A Simple Semi-Automatic Approach for Land Cover Classification from Multispectral Remote Sensing Imagery." PLoS ONE 7, no. 9 (September 26, 2012): e45889. http://dx.doi.org/10.1371/journal.pone.0045889.

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Giovannini, E. C., A. Tomalini, E. Pristeri, L. Bergamasco, and M. Lo Turco. "DECAY CLASSIFICATION USING ARTIFICIAL INTELLIGENCE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 847–54. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-847-2021.

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Abstract. The paper presents DECAI - DEcay Classification using Artificial Intelligence, a novel study using machine learning algorithms to identify materials, degradations or surface gaps of an architectural artefact in a semi-automatic way. A customised software has been developed to allow the operator to choose which categories of materials to classify, and selecting sample data from an orthophoto of the artefact to train the machine learning algorithms. Thanks to Visual Programming Language algorithms, the classification results are directly imported into the H-BIM environment and used to enrich the H-BIM model of the artefact. To date, the developed tool is dedicated to research use only; future developments will improve the graphical interface to make this tool accessible to a wider public.

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Tonbul, Hasan, and Taskin Kavzoglu. "Semi-Automatic Building Extraction from WorldView-2 Imagery Using Taguchi Optimization." Photogrammetric Engineering & Remote Sensing 86, no. 9 (September 1, 2020): 547–55. http://dx.doi.org/10.14358/pers.86.9.547.

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Due to the complex spectral and spatial structures of remotely sensed images, the delineation of land use/land cover classes using conventional approaches is a challenging task. This article tackles the problem of seeking optimal parameters of multi-resolution segmentation for a classification task using WorldView-2 imagery. Taguchi optimization was applied to search optimal parameters using the plateau objective function (<small>POF</small>) and quality rate (<small>Qr</small>) as fitness criteria. Analysis of variance was also used to estimate the contributions of the parameters for POF and Qr, separately. The scale parameter was the most effective one, with contribution levels of 87.45% and 56.87% for POF and Qr, respectively. Linear regression and support-vector regression methods were used to predict the results of the experiment. Test results revealed that Taguchi optimization was more effective than linear regression and support-vector regression for predicting POF and Qr values.

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Maktabi, Marianne, Hannes Köhler, Claire Chalopin, Thomas Neumuth, Yannis Wichmann, Boris Jansen-Winkeln, Ines Gockel, et al. "Semi-automatic decision-making process in histopathological specimens from Barrett’s carcinoma patients using hyperspectral imaging (HSI)." Current Directions in Biomedical Engineering 6, no. 3 (September 1, 2020): 261–63. http://dx.doi.org/10.1515/cdbme-2020-3066.

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AbstractDiscrimination of malignant and non-malignant cells of histopathologic specimens is a key step in cancer diagnostics. Hyperspectral Imaging (HSI) allows the acquisition of spectra in the visual and near-infrared range (500-1000nm). HSI can support the identification and classification of cancer cells using machine learning algorithms. In this work, we tested four classification methods on histopathological slides of esophageal adenocarcinoma. The best results were achieved with a Multi-Layer Perceptron. Sensitivity and F1-Score values of 90% were obtained.

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Gentillon, Hugues, Ludomir Stefańczyk, Michał Strzelecki, and Maria Respondek-Liberska. "Texture analysis of the developing human brain using customization of a knowledge-based system." F1000Research 6 (January 12, 2017): 40. http://dx.doi.org/10.12688/f1000research.10401.1.

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Background: Pattern recognition software originally designed for geospatial and other technical applications could be trained by physicians and used as texture-analysis tools for evidence-based practice, in order to improve diagnostic imaging examination during pregnancy. Methods: Various machine-learning techniques and customized datasets were assessed for training of an integrable knowledge-based system (KBS), to determine a hypothetical methodology for texture classification of closely-related anatomical structures in fetal brain magnetic resonance (MR) images. Samples were manually categorized according to the magnetic field of the MRI scanner (i.e. 1.5-tesla (1.5T), 3-tesla (3T)), rotational planes (i.e. coronal, sagittal and axial), and signal weighting (i.e. spin-lattice, spin-spin, relaxation, proton density). In the machine-learning sessions, the operator manually selected relevant regions of interest (ROI) in 1.5/3T MR images. Semi-automatic procedures in MaZda/B11 were performed to determine optimal parameter sets for ROI classification. Four classes were defined: ventricles, thalamus, grey matter, and white matter. Various textures analysis methods were tested. The KBS performed automatic data pre-processing and semi-automatic classification of ROIs. Results: After testing 3456 ROIs, statistical binary classification revealed that combination of reduction techniques with linear discriminant algorithms (LDA) or nonlinear discriminant algorithms (NDA) yielded the best scoring in terms of sensitivity (both 100%, 95% CI: 99.79-100), specificity (both 100%, 95% CI: 99.79-100) and Fisher coefficient (≈E+4, ≈E+5, respectively). Conclusions: LDA and NDA in MaZda can be useful data mining tools for screening a population of interest subjected to a clinical test.

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Gentillon, Hugues, Ludomir Stefańczyk, Michał Strzelecki, and Maria Respondek-Liberska. "Texture analysis of the developing human brain using customization of a knowledge-based system." F1000Research 6 (September 11, 2017): 40. http://dx.doi.org/10.12688/f1000research.10401.2.

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Background: Pattern recognition software originally designed for geospatial and other technical applications could be trained by physicians and used as texture analysis tools for evidence-based practice, in order to improve diagnostic imaging examination during pregnancy. Methods: Various machine-learning techniques and customized datasets were assessed for training of an integrable knowledge-based system (KBS) to determine a hypothetical methodology for texture classification of closely related anatomical structures in fetal brain magnetic resonance (MR) images. Samples were manually categorized according to the magnetic field of the MRI scanner (i.e., 1.5-tesla [1.5T], 3-tesla [3T]), rotational planes (i.e., coronal, sagittal, and axial), and signal weighting (i.e., spin-lattice, spin-spin, relaxation, and proton density). In the machine-learning sessions, the operator manually selected relevant regions of interest (ROI) in 1.5/3T MR images. Semi-automatic procedures in MaZda/B11 were performed to determine optimal parameter sets for ROI classification. Four classes were defined: ventricles, thalamus, gray matter, and white matter. Various texture analysis methods were tested. The KBS performed automatic data preprocessing and semi-automatic classification of ROI. Results: After testing 3456 ROI, statistical binary classification revealed that the combination of reduction techniques with linear discriminant algorithms (LDA) or nonlinear discriminant algorithms (NDA) yielded the best scoring in terms of sensitivity (both 100%, 95% CI: 99.79–100), specificity (both 100%, 95% CI: 99.79–100), and Fisher coefficient (≈E+4 and ≈E+5, respectively). Conclusions: LDA and NDA in MaZda can be useful data mining tools for screening a population of interest subjected to a clinical test.

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Nunes, Darlan Miranda, Nilcilene das Graças Medeiros, and Afonso de Paula dos Santos. "SEMI-AUTOMATIC ROAD NETWORK EXTRACTION FROM DIGITAL IMAGES USING OBJECT-BASED CLASSIFICATION AND MORPHOLOGICAL OPERATORS." Boletim de Ciências Geodésicas 24, no. 4 (December 2018): 485–502. http://dx.doi.org/10.1590/s1982-21702018000400030.

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Abstract: The demand for geospatial data concerning road network is constant, due to the wide variety of application which needs this type of data. It stands out the importance of this data in cartography update cycles, that can be obtained using automated processes of feature extraction in digital images, which are more accurate, fast and less costly than the traditional methods. In this sense, this work aimed the road network extraction from RapidEye satellite imagery, by developing a hybrid methodology using techniques of object-based image classification and morphological operators. The methodology was tested in three different sites, with images acquired in distinct dates, and the extraction process was evaluated through metrics obtained from the linear matching procedure. By the proposed extraction process, were achieved in terms of correctness and completeness the values of 92.23% and 85.15% for test site 1, the values of 79.16% and 81.06% for test site 2, and the values of 82.05% and 92.22% for test site 3, respectively. The results shown that the proposed methodology presented a good performance for semi-automatic road network extraction from Rapideye images, representing an alternative to auxiliary road network database acquisition and updating.

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Boudissa, M., B. Orfeuvre, M. Chabanas, and J. Tonetti. "Does semi-automatic bone-fragment segmentation improve the reproducibility of the Letournel acetabular fracture classification?" Orthopaedics & Traumatology: Surgery & Research 103, no. 5 (September 2017): 633–38. http://dx.doi.org/10.1016/j.otsr.2017.03.018.

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Lai, Daphne Teck Ching, and Jonathan M. Garibaldi. "A preliminary study on automatic breast cancer data classification using semi-supervised fuzzy c-means." International Journal of Biomedical Engineering and Technology 13, no. 4 (2013): 303. http://dx.doi.org/10.1504/ijbet.2013.058535.

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Zhang, Enjie, Zhidong Zhang, Long Yan, and Da Li. "Automatic classification model of semi-structured HTML text data based on State Grid cloud architecture." Journal of Physics: Conference Series 1920, no. 1 (May 1, 2021): 012072. http://dx.doi.org/10.1088/1742-6596/1920/1/012072.

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Woźniak, Rafał, Piotr Ożdżyński, and Danuta Zakrzewska. "CLUSTER ANALYSIS OF MEDICAL TEXT DOCUMENTS BY USING SEMI-CLUSTERING APPROACH BASED ON GRAPH REPRESENTATION." Information System in Management 7, no. 3 (September 30, 2018): 213–24. http://dx.doi.org/10.22630/isim.2018.7.3.19.

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The development of Internet resulted in an increasing number of online text re-positories. In many cases, documents are assigned to more than one class and automatic multi-label classification needs to be used. When the number of labels exceeds the number of the documents, effective label space dimension reduction may signifi-cantly improve classification accuracy, what is a major priority in the medical field. In the paper, we propose document clustering for label selection. We use semi-clustering method, by considering graph representation, where documents are represented by vertices and edge weights are calculated according to their mutual similarity. Assigning documents to semi-clusters helps in reducing number of labels, further used in multilabel classification process. The performance of the method is examined by experiments conducted on real medical datasets.

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Rahmayudi, Aji, and Aldino Rizaldy. "COMPARISON OF SEMI AUTOMATIC DTM FROM IMAGE MATCHING WITH DTM FROM LIDAR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 373–80. http://dx.doi.org/10.5194/isprsarchives-xli-b3-373-2016.

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Nowadays DTM LIDAR was used extensively for generating contour line in Topographic Map. This method is very superior compared to traditionally stereomodel compilation from aerial images that consume large resource of human operator and very time consuming. Since the improvement of computer vision and digital image processing, it is possible to generate point cloud DSM from aerial images using image matching algorithm. It is also possible to classify point cloud DSM to DTM using the same technique with LIDAR classification and producing DTM which is comparable to DTM LIDAR. This research will study the accuracy difference of both DTMs and the result of DTM in several different condition including urban area and forest area, flat terrain and mountainous terrain, also time calculation for mass production Topographic Map. From statistical data, both methods are able to produce 1:5.000 Topographic Map scale.

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Rahmayudi, Aji, and Aldino Rizaldy. "COMPARISON OF SEMI AUTOMATIC DTM FROM IMAGE MATCHING WITH DTM FROM LIDAR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 373–80. http://dx.doi.org/10.5194/isprs-archives-xli-b3-373-2016.

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Nowadays DTM LIDAR was used extensively for generating contour line in Topographic Map. This method is very superior compared to traditionally stereomodel compilation from aerial images that consume large resource of human operator and very time consuming. Since the improvement of computer vision and digital image processing, it is possible to generate point cloud DSM from aerial images using image matching algorithm. It is also possible to classify point cloud DSM to DTM using the same technique with LIDAR classification and producing DTM which is comparable to DTM LIDAR. This research will study the accuracy difference of both DTMs and the result of DTM in several different condition including urban area and forest area, flat terrain and mountainous terrain, also time calculation for mass production Topographic Map. From statistical data, both methods are able to produce 1:5.000 Topographic Map scale.

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Dias Tardelli Uehara, Tatiana, Sabrina Paes Leme Passos Corrêa, Renata Pacheco Quevedo, Thales Sehn Körting, Luciano Vieira Dutra, and Camilo Daleles Rennó. "Landslide Scars Detection using Remote Sensing and Pattern Recognition Techniques: Comparison Among Artificial Neural Networks, Gaussian Maximum Likelihood, Random Forest, and Support Vector Machine Classifiers." Revista Brasileira de Cartografia 72, no. 4 (November 14, 2020): 665–80. http://dx.doi.org/10.14393/rbcv72n4-54037.

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Landslide inventory is an essential tool to support disaster risk mitigation. The inventory is usually obtained via conventional methods, as visual interpretation of remote sensing images, or semi-automatic methods, through pattern recognition. In this study, four classification algorithms are compared to detect landslides scars: Artificial Neural Network (ANN), Maximum Likelihood (ML), Random Forest (RF) and Support Vector Machine (SVM). From Sentinel-2A imagery and SRTM’s Digital Elevation Model (DEM), vegetation indices and slope features were extracted and selected for two areas at the Rolante River Catchment, in Brazil. The classification products showed that the ML and the RF presented superior results with OA values above 92% for both study areas. These best accuracy’s results were identified in classifications using all attributes as input, so without previous feature selection.

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Yastikli, N., and Z. Cetin. "AUTOMATIC 3D BUILDING MODEL GENERATIONS WITH AIRBORNE LiDAR DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4 (November 13, 2017): 411–14. http://dx.doi.org/10.5194/isprs-annals-iv-4-w4-411-2017.

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LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, which has the major importance for urban planning, illegal construction monitoring, 3D city modelling, environmental simulation, tourism, security, telecommunication and mobile navigation etc. The manual or semi-automatic 3D building model generation is costly and very time-consuming process for these applications. Thus, an approach for automatic 3D building model generation is needed in a simple and quick way for many studies which includes building modelling. In this study, automatic 3D building models generation is aimed with airborne LiDAR data. An approach is proposed for automatic 3D building models generation including the automatic point based classification of raw LiDAR point cloud. The proposed point based classification includes the hierarchical rules, for the automatic production of 3D building models. The detailed analyses for the parameters which used in hierarchical rules have been performed to improve classification results using different test areas identified in the study area. The proposed approach have been tested in the study area which has partly open areas, forest areas and many types of the buildings, in Zekeriyakoy, Istanbul using the TerraScan module of TerraSolid. The 3D building model was generated automatically using the results of the automatic point based classification. The obtained results of this research on study area verified that automatic 3D building models can be generated successfully using raw LiDAR point cloud data.

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