Добірка наукової літератури з теми "Non-invasive images"
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Статті в журналах з теми "Non-invasive images"
Gupta, Ambuj, Yajur Shridhar, Gayathri Mohan, and Shubham Tyagi. "Image Processing Based Non-Invasive Health Monitoring in Civil Engineering." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 1163–68. http://dx.doi.org/10.22214/ijraset.2022.47550.
Повний текст джерелаFatemah Memon, Hijab, Suraiya Hirani, Jaweria Yousfani, Reema Aslam, Sehar Mushtaque, and Iqra Memon. "Enamel Demineralization and Remineralization Detection Using Non-invasive Optical Imaging." Pakistan Journal of Medical and Health Sciences 15, no. 9 (September 30, 2021): 2766–69. http://dx.doi.org/10.53350/pjmhs211592766.
Повний текст джерелаKale, Shruti, Reema Kharat, Sagarika Kalyankar, Sangita Chaudhari, and Apurva Shinde. "Automated Non-invasive Skin Cancer Detection using Dermoscopic Images." ITM Web of Conferences 40 (2021): 03044. http://dx.doi.org/10.1051/itmconf/20214003044.
Повний текст джерелаAgrawal, Neelam, Bikesh Kumar Singh, and Kesari Verma. "Non-invasive technique of diabetes detection using iris images." International Journal of Computational Vision and Robotics 9, no. 4 (2019): 351. http://dx.doi.org/10.1504/ijcvr.2019.10022856.
Повний текст джерелаVerma, Kesari, Bikesh Kumar Singh, and Neelam Agrawal. "Non-invasive technique of diabetes detection using iris images." International Journal of Computational Vision and Robotics 9, no. 4 (2019): 351. http://dx.doi.org/10.1504/ijcvr.2019.101537.
Повний текст джерелаRodriguez-Lozano, Francisco J., Fernando León-García, M. Ruiz de Adana, Jose M. Palomares, and J. Olivares. "Non-Invasive Forehead Segmentation in Thermographic Imaging." Sensors 19, no. 19 (September 22, 2019): 4096. http://dx.doi.org/10.3390/s19194096.
Повний текст джерелаLi, Ming Jian, Jia Han Guo, Zheng Yu, Lei Yan, and Ning Han. "Non-Invasive Image Processing Method for Detecting Seed Vigor." Applied Mechanics and Materials 397-400 (September 2013): 2134–37. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2134.
Повний текст джерелаKajinami, K., N. Takekoshi, and H. Mabuchi. "Images in cardiology. Non-invasive detection of quadricuspid aortic valve." Heart 78, no. 1 (July 1, 1997): 87. http://dx.doi.org/10.1136/hrt.78.1.87.
Повний текст джерелаLeech, Michelle, John Gaffney, and Laure Marignol. "Improving non-invasive detection of prostate cancer using diffusion-weighted MRI." Advances in Modern Oncology Research 2, no. 6 (December 13, 2016): 309. http://dx.doi.org/10.18282/amor.v2.i6.152.
Повний текст джерелаMazzeo, Pier Luigi, Christian Libetta, Paolo Spagnolo, and Cosimo Distante. "A Siamese Neural Network for Non-Invasive Baggage Re-Identification." Journal of Imaging 6, no. 11 (November 20, 2020): 126. http://dx.doi.org/10.3390/jimaging6110126.
Повний текст джерелаДисертації з теми "Non-invasive images"
Dhinagar, Nikhil J. "Non-Invasive Skin Cancer Classification from Surface Scanned Lesion Images." Ohio University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1366384987.
Повний текст джерелаMeng, Goh Chuan. "Estimation de profondeur de veine sous-invasive non invasive utilisant une imagerie multispectrale et des images de réflectance diffuses." Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCK081.
Повний текст джерелаThe estimation of subcutaneous vein depth has been an important research topic in recent years due to its importance in optimizing the intravenous (IV) access of venipuncture. Various techniques and system of vein visualization were proposed to improve the vein viewing, but the lack of vein depth information limits the system performance in assisting the IV access; thus, the IV access in many cases remains dependent on skill or experience of the clinicians. Several techniques were proposed to estimate the vein depth using diffuse reflectance of which the optical density ratio (ODR) technique is the most complete solution. The concept of measuring the veins depth using ODR based technique is deserved to be applied in the real-world due to its low cost, non-invasive properties and from the fact that it is a non-skin contact measurement technique. Nishidate et. al. [1] suggested an optimum conditions to measure the vein depth and thickness by using ODR which was supported by experiment with customized tissue-like agar gel phantom. However, such experiment may not be sufficient to prove its application for in vivo measurement due to the lack of experiment for real data. Therefore, this thesis work was first started to improve the proposed model by Nishidate et. al. and expand it to measure the in vivo estimation of vein depth on real patients. The proposed system incorporates new components such as an autonomous vein segmentation algorithm, a more accurate estimation method for melanin content (Cm) and a fully new hardware design with reliable parts. Importantly, the experiment estimate the vein depth on real patients as well as a through comparison with Ultrasound data. The experiment results show a strong Pearson correlation of 0.843 as compared to Ultrasound data and this evidence that the developed system is works for the in vivo measurement of vein depth. Besides that, an optimum vein filter (matched filter) is proposed to be used in the imaging system to preserve the most accurate vein detection and allow the system to produce the results with least detection error. The selection of the optimum vein filter has laid an important platform from which to obtain the accurate vein segmentation of a NIR image
Soltani, Mariem. "Partitionnement des images hyperspectrales de grande dimension spatiale par propagation d'affinité." Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S099/document.
Повний текст джерелаThe interest in hyperspectral image data has been constantly increasing during the last years. Indeed, hyperspectral images provide more detailed information about the spectral properties of a scene and allow a more precise discrimination of objects than traditional color images or even multispectral images. High spatial and spectral resolutions of hyperspectral images enable to precisely characterize the information pixel content. Though the potentialities of hyperspectral technology appear to be relatively wide, the analysis and the treatment of these data remain complex. In fact, exploiting such large data sets presents a great challenge. In this thesis, we are mainly interested in the reduction and partitioning of hyperspectral images of high spatial dimension. The proposed approach consists essentially of two steps: features extraction and classification of pixels of an image. A new approach for features extraction based on spatial and spectral tri-occurrences matrices defined on cubic neighborhoods is proposed. A comparative study shows the discrimination power of these new features over conventional ones as well as spectral signatures. Concerning the classification step, we are mainly interested in this thesis to the unsupervised and non-parametric classification approach because it has several advantages: no a priori knowledge, image partitioning for any application domain, and adaptability to the image information content. A comparative study of the most well-known semi-supervised (knowledge of number of classes) and unsupervised non-parametric methods (K-means, FCM, ISODATA, AP) showed the superiority of affinity propagation (AP). Despite its high correct classification rate, affinity propagation has two major drawbacks. Firstly, the number of classes is over-estimated when the preference parameter p value is initialized as the median value of the similarity matrix. Secondly, the partitioning of large size hyperspectral images is hampered by its quadratic computational complexity. Therefore, its application to this data type remains impossible. To overcome these two drawbacks, we propose an approach which consists of reducing the number of pixels to be classified before the application of AP by automatically grouping data points with high similarity. We also introduce a step to optimize the preference parameter value by maximizing a criterion related to the interclass variance, in order to correctly estimate the number of classes. The proposed approach was successfully applied on synthetic images, mono-component and multi-component and showed a consistent discrimination of obtained classes. It was also successfully applied and compared on hyperspectral images of high spatial dimension (1000 × 1000 pixels × 62 bands) in the context of a real application for the detection of invasive and non-invasive vegetation species
Florea, Ioana. "Pet parametric imaging of acetylcholine esterase activity without arterial blood sampling in normal subjects and patients with neurovegetative disease." Doctoral thesis, Università degli studi di Padova, 2008. http://hdl.handle.net/11577/3425120.
Повний текст джерелаBelem, Brahima. "Non-invasive wound assessment by image analysis." Thesis, University of South Wales, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409107.
Повний текст джерелаSharp, Angela. "Assessment of putative markers for non-invasive detection of bladder cancer /." Assessment of putative markers for non-invasive detection of bladder cancerRead the abstract of the thesis, 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16763.pdf.
Повний текст джерелаAlonso-Caneiro, David. "Non-invasive assessment of tear film surface quality." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/41737/1/David_Alonso-Caneiro_Thesis.pdf.
Повний текст джерелаHillergren, Pierre. "Towards non-invasive Gleason grading of prostate cancer using diffusion weighted MRI." Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172808.
Повний текст джерелаWang, Shaobai. "Development and application of a non invasive image matching method to study spine biomechanics." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44851.
Повний текст джерелаIncludes bibliographical references (p. 83-92).
Research on spine biomechanics is critical to understand pathology such as degenerative changes and low back pain. However, current study on in-vivo spine biomechanics is limited by the complex anatomy and invasive methodology. Modem clinical imaging techniques such as magnetic resonance and fluoroscope images, which are widely accessible nowadays, have the potential to study in-vivo spine biomechanics accurately and non-invasively. This research presents a new combined magnetic resonance and fluoroscope imaging matching method to study human lumbar vertebral kinematics and disc deformation during various physiologic functional activities. Validation and application of this method as well as discussion of its performance and applicability are detailed herein.
by Shaobai Wang.
S.M.
Randall, D. "Towards a non-invasive diagnostic aid for abdominal adhesions using dynamic MRI and image processing." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/19141/.
Повний текст джерелаКниги з теми "Non-invasive images"
Stergios, Stergiopoulos, ed. Advanced signal processing: Theory and implementation for sonar, radar, and non-invasive medical diagnostic systems. 2nd ed. Boca Raton: Taylor & Francis, 2009.
Знайти повний текст джерелаMarzec, Mariusz, and Robert Koprowski, eds. Non-Invasive Diagnostic Methods - Image Processing. IntechOpen, 2018. http://dx.doi.org/10.5772/intechopen.76952.
Повний текст джерелаBadakhshi, Harun. Image-Guided Stereotactic Radiosurgery: High-Precision, Non-invasive Treatment of Solid Tumors. Springer, 2018.
Знайти повний текст джерелаBadakhshi, Harun. Image-Guided Stereotactic Radiosurgery: High-Precision, Non-invasive Treatment of Solid Tumors. Springer, 2016.
Знайти повний текст джерелаBadakhshi, Harun. Image-Guided Stereotactic Radiosurgery: High-Precision, Non-Invasive Treatment of Solid Tumors. Springer London, Limited, 2016.
Знайти повний текст джерелаStergiopoulos, Stergios. Advanced Signal Processing: Theory and Implementation for Sonar, Radar, and Non-Invasive Medical Diagnostic Systems, Second Edition. Taylor & Francis Group, 2017.
Знайти повний текст джерелаGaliuto, L., R. Senior, and H. Becher. Contrast echocardiography. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780199599639.003.0007.
Повний текст джерелаAndrade, Maria João, and Albert Varga. Stress echocardiography: methodology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198726012.003.0012.
Повний текст джерелаChappell, Michael, Bradley MacIntosh, and Thomas Okell. Introduction to Perfusion Quantification using Arterial Spin Labelling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198793816.001.0001.
Повний текст джерелаAdvanced Signal Processing: Theory and Implementation for Sonor, Radar, and Non-Invasive Medical Diagnostic Systems, Second Edition (Electrical Engineering & Applied Signal Processing). 2nd ed. CRC, 2009.
Знайти повний текст джерелаЧастини книг з теми "Non-invasive images"
Ferdousi, Rahatara, Nabila Mabruba, Fedwa Laamarti, Abdulmotaleb El Saddik, and Chunsheng Yang. "Non-invasive Anemia Detection from Conjunctival Images." In Lecture Notes in Computer Science, 189–201. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22061-6_14.
Повний текст джерелаShaju, Abheet, Ansh Shah, Garima Iyer, Pranav Pandya, and Vinaya Sawant. "Non-Invasive Anemia Detection Using Images Acquired from Smartphone Camera." In Algorithms for Intelligent Systems, 803–13. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3951-8_61.
Повний текст джерелаDedhiya, Ronak, Siva Teja Kakileti, Kanchana Gopinath, Agbogah Edem, Bismark Donkor, Abdulai Mahmood Seidu, Simon K. Attah, Christopher L. King, Nicholas Opoku, and Geetha Manjunath. "Non-invasive Thermal Imaging for Estimation of the Fecundity of Live Female Onchocerca Worms." In Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery, 102–10. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19660-7_10.
Повний текст джерелаLanzarone, Ettore, Ferdinando Auricchio, Michele Conti, and Anna Ferrara. "Bayesian Estimation of the Aortic Stiffness based on Non-invasive Computed Tomography Images." In Springer Proceedings in Mathematics & Statistics, 133–42. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16238-6_12.
Повний текст джерелаGumulski, Jakub, Marta Jankowska, and Dominik Spinczyk. "Non-invasive Measurement of Human Pulse Based on Photographic Images of the Face." In Advances in Intelligent Systems and Computing, 455–64. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09135-3_38.
Повний текст джерелаLupsor, M., R. Badea, C. Vicas, S. Nedevschi, H. Stefanescu, M. Grigorescu, C. Radu, and D. Crisan. "Non-invasive Steatosis Assessment through the Computerized Processing of Ultrasound Images: Attenuation versus First Order Texture Parameters." In IFMBE Proceedings, 184–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22586-4_40.
Повний текст джерелаLungu, A., J. Wild, Andy Swift, David Capener, David Kiely, and D. R. Hose. "Automatic, Simultaneous, Non-invasive Measurements of Flow and Area in the Human Pulmonary Arteries from MRI Images." In International Conference on Advancements of Medicine and Health Care through Technology; 5th – 7th June 2014, Cluj-Napoca, Romania, 259–64. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07653-9_53.
Повний текст джерелаGillam, Linda D., and Sofia Shames. "Quality Control: Equipment and Laboratory Structure; Image Acquisition, Review and Analysis; Study Reporting." In Quality Evaluation in Non-Invasive Cardiovascular Imaging, 331–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28011-0_28.
Повний текст джерелаMostapha, M., M. F. Casanova, G. Gimel’farb, and A. El-Baz. "Towards Non-invasive Image-Based Early Diagnosis of Autism." In Lecture Notes in Computer Science, 160–68. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24571-3_20.
Повний текст джерелаHan, Lianghao, Michael Burcher, and J. Alison Noble. "Non-invasive Measurement of Biomechanical Properties of in vivo Soft Tissues." In Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002, 208–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45786-0_26.
Повний текст джерелаТези доповідей конференцій з теми "Non-invasive images"
Fuster, Saul, Farbod Khoraminia, Umay Kiraz, Neel Kanwal, Vebjorn Kvikstad, Trygve Eftestol, Tahlita C. M. Zuiverloon, Emiel A. M. Janssen, and Kjersti Engan. "Invasive Cancerous Area Detection in Non-Muscle Invasive Bladder Cancer Whole Slide Images." In 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP). IEEE, 2022. http://dx.doi.org/10.1109/ivmsp54334.2022.9816352.
Повний текст джерелаAarav, Shaurya, and Jason W. Fleischer. "Non-invasive, Depth-resolved Imaging through Scattering Media." In Computational Optical Sensing and Imaging. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/cosi.2022.cm3a.7.
Повний текст джерелаKumar, K. Vignesh, and R. Periyasamy. "Development of a non-invasive non-contact optical device for estimating TcB in Neonates." In 2020 Sixth International Conference on Bio Signals, Images, and Instrumentation (ICBSII). IEEE, 2020. http://dx.doi.org/10.1109/icbsii49132.2020.9167612.
Повний текст джерелаDuarte, Marta, Victor Coch, Jovania Dias, Silvia Botelho, Nelson Duarte, and Paulo Drews. "Thermographic Non-Invasive Inspection Modelling of Fertilizer Pipelines Using Neural Networks." In 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2020. http://dx.doi.org/10.1109/sibgrapi51738.2020.00045.
Повний текст джерелаOtoya, Paulo E. Linares, and Sixto R. Prado Gardini. "Real-Time Non-Invasive Leaf Area Measurement Method using Depth Images." In 2020 IEEE ANDESCON. IEEE, 2020. http://dx.doi.org/10.1109/andescon50619.2020.9271993.
Повний текст джерелаOkada, Shima, Yuko Ohno, Goyahan, Kumi Kato-Nishimura, Ikuko Mohri, and Masako Taniike. "Examination of non-restrictive and non-invasive sleep evaluation technique for children using difference images." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4649956.
Повний текст джерелаMilner, Thomas E., Sergey A. Telenkov, B. Samuel Tanenbaum, J. Stuart Nelson, and Dennis M. Goodman. "Non-Invasive Evaluation of Biological Materials Using Pulsed Photothermal Tomography." In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-0817.
Повний текст джерелаGannot, I., Gandjbakhche AH, Gannot G, Fox PC, Koch H, and Bonner RF. "Non-invasive Technique for the Diagnosis of Diseased Salivary Glands in situ." In Biomedical Optical Spectroscopy and Diagnostics. Washington, D.C.: Optica Publishing Group, 2006. http://dx.doi.org/10.1364/bosd.1996.dr3.
Повний текст джерелаSubashini, M. Monica, and V. Indra Gandhi. "An efficient non-invasive method for brain tumor grade analysis on MR images." In TENCON 2017 - 2017 IEEE Region 10 Conference. IEEE, 2017. http://dx.doi.org/10.1109/tencon.2017.8228041.
Повний текст джерелаPiresa, Luiz F., Ricardo Alarcon, Phil Cole, Andres J. Kreiner, and Hugo F. Arellano. "Soil Crust Changes due to Wetting and Drying Analyzed by Non-Invasive Images." In VIII LATIN AMERICAN SYMPOSIUM ON NUCLEAR PHYSICS AND APPLICATIONS. AIP, 2010. http://dx.doi.org/10.1063/1.3480241.
Повний текст джерелаЗвіти організацій з теми "Non-invasive images"
Author, Unknown. DTRS56-02-T-0005 Digital Mapping of Buried Pipelines with a Dual Array System. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 2005. http://dx.doi.org/10.55274/r0011943.
Повний текст джерела