Academic literature on the topic 'Quantitative imaging analysis'

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Journal articles on the topic "Quantitative imaging analysis"

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Asinovski, L., D. Beaglehole, and M. T. Clarkson. "Imaging ellipsometry: quantitative analysis." physica status solidi (a) 205, no. 4 (April 2008): 764–71. http://dx.doi.org/10.1002/pssa.200777855.

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Woo, D.-C., C.-B. Choi, J.-W. Nam, K.-N. Ryu, G.-H. Jahng, S.-H. Lee, D.-W. Lee, et al. "Quantitative analysis of hydrocephalic ventricular alterations in Yorkshire terriers using magnetic resonance imaging." Veterinární Medicína 55, No. 3 (April 15, 2010): 125–32. http://dx.doi.org/10.17221/127/2009-vetmed.

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The purpose of this work was to evaluate hydrocephalic ventricular changes using three quantitative analysis methods. The height, area and volume of the ventricles and brain were measured in 20 Yorkshire terriers (10 normal and 10 hydrocephalic dogs) using low-field MR imaging (at 0.2 Tesla). All measurements were averaged and the relative ventricle size was defined as a percentage (percent size of the ventricle/size of the brain). The difference between normal and hydrocephalic dogs was statistically significant for the average of each ventricle as well as for the percentage value. Five hydrocephalic symptoms were identified: circling, head tilting, seizures, ataxia, and strabismus. With respect to height, area and volume of the brain/ventricle, the difference between normal and hydrocephalic dogs was not significant. The ventricle/brain with height (1D) was related to the area (2D) and volume (3D). The correlations with area and volume were as good as the ventricle/brain height ratio in the case of hydrocephalic dogs. Therefore, one-, two- and three-dimensional quantitative methods may be complementary. We expect that the stage of hydrocephalic symptoms can be classified if statistical significance for ventricular size among symptoms is determined with the analysis of a large number of hydrocephalic cases.
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Kjær, L., P. Ring, C. Thomsen, and O. Henriksen. "Texture Analysis in Quantitative MR Imaging." Acta Radiologica 36, no. 2 (March 1, 1995): 127–35. http://dx.doi.org/10.3109/02841859509173364.

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Zaidi, H., and W. D. Erwin. "Quantitative Analysis in Nuclear Medicine Imaging." Journal of Nuclear Medicine 48, no. 8 (August 1, 2007): 1401. http://dx.doi.org/10.2967/jnumed.107.042598.

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Harpen, M. D., J. Powell Williams, and J. P. Williams. "Quantitative analysis of NMR spectroscopic imaging." Physics in Medicine and Biology 32, no. 4 (April 1, 1987): 421–30. http://dx.doi.org/10.1088/0031-9155/32/4/001.

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Ranade, Vasant. "Quantitative Analysis in Nuclear Medicine Imaging." American Journal of Therapeutics 13, no. 4 (July 2006): 385. http://dx.doi.org/10.1097/00045391-200607000-00018.

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Kjær, L., P. Ring, C. Thomsen, and O. Henriksen. "Texture Analysis in Quantitative MR Imaging." Acta Radiologica 36, no. 2 (January 1995): 127–35. http://dx.doi.org/10.1080/02841859509173364.

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Kjær, L., P. Ring, C. Thomsen, and O. Henriksen. "Texture Analysis in Quantitative MR Imaging." Acta Radiologica 36, no. 2 (March 1995): 127–35. http://dx.doi.org/10.1177/028418519503600204.

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The diagnostic potential of texture analysis in quantitative tissue characterisation by MR imaging at 1.5 T was evaluated in the brain of 6 healthy volunteers and in 88 patients with intracranial tumours. Texture images were computed from calculated T1 and T2 parameter images by applying groups of common first-order and second-order grey level statistics. Tissue differentiation in the images was estimated by the presence or absence of significant differences between tissue types. A fine discrimination was obtained between white matter, cortical grey matter, and cerebrospinal fluid in the normal brain, and white matter was readily separated from the tumour lesions. Moreover, separation of solid tumour tissue and peritumoural oedema was suggested for some tumour types. Mutual comparison of all tumour types revealed extensive differences, and even specific tumour differentiation turned out to be successful in some cases of clinical importance. However, no discrimination between benign and malignant tumour growth was possible. Much texture information seems to be contained in MR images, which may prove useful for classification and image segmentation.
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Madsen, Mark. "Quantitative Analysis in Nuclear Medicine Imaging." Medical Physics 34, no. 4 (March 28, 2007): 1522. http://dx.doi.org/10.1118/1.2716416.

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Lammertsma, A. "Radioligand studies: imaging and quantitative analysis." European Neuropsychopharmacology 12, no. 6 (December 2002): 513–16. http://dx.doi.org/10.1016/s0924-977x(02)00100-1.

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Dissertations / Theses on the topic "Quantitative imaging analysis"

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Weith-Glushko, Seth A. "Quantitative analysis of infrared contrast enhancement algorithms /." Online version of thesis, 2007. http://hdl.handle.net/1850/4208.

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Li, Chengshuai. "Quantitative Anisotropy Imaging based on Spectral Interferometry." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/99424.

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Spectral interferometry, also known as spectral-domain white light or low coherence interferometry, has seen numerous applications in sensing and metrology of physical parameters. It can provide phase or optical path information of interest in single shot measurements with exquisite sensitivity and large dynamic range. As fast spectrometer became more available in 21st century, spectral interferometric techniques start to dominate over time-domain interferometry, thanks to its speed and sensitivity advantage. In this work, a dual-modality phase/birefringence imaging system is proposed to offer a quantitative approach to characterize phase, polarization and spectroscopy properties on a variety of samples. An interferometric spectral multiplexing method is firstly introduced by generating polarization mixing with specially aligned polarizer and birefringence crystal. The retardation and orientation of sample birefringence can then be measured simultaneously from a single interference spectrum. Furthermore, with the addition of a Nomarski prism, the same setup can be used for quantitative differential interference contrast (DIC) imaging. The highly integrated system demonstrates its capability for noninvasive, label-free, highly sensitive birefringence, DIC and phase imaging on anisotropic materials and biological specimens, where multiple intrinsic contrasts are desired. Besides using different intrinsic contrast regime to quantitatively measure different biological samples, spectral multiplexing interferometry technique also finds an exquisite match in imaging single anisotropic nanoparticles, even its size is well below diffraction limit. Quantitative birefringence spectroscopy measurement over gold nanorod particles on glass substrate demonstrates that the proposed system can simultaneously determine the polarizability-induced birefringence orientation, as well as the scattering intensity and the phase differences between major/minor axes of single nanoparticles. With the anisotropic nanoparticles� spectroscopic polarizability defined prior to the measurement with calculation or simulation, the system can be further used to reveal size, aspect ratio and orientation information of the detected anisotropic nanoparticle. Alongside developing optical anisotropy imaging systems, the other part of this research describes our effort of investigating the sensitivity limit for general spectral interferometry based systems. A complete, realistic multi-parameter interference model is thus proposed, while corrupted by a combination of shot noise, dark noise and readout noise. With these multiple noise sources in the detected spectrum following different statistical behaviors, Cramer-Rao Bounds is derived for multiple unknown parameters, including optical pathlength, system-specific initial phase, spectrum intensity as well as fringe visibility. The significance of the work is to establish criteria to evaluate whether an interferometry-based optical measurement system has been optimized to its hardware best potential. An algorithm based on maximum likelihood estimation is also developed to achieve absolute optical pathlength demodulation with high sensitivity. In particular, it achieves Cramer-Rao bound and offers noise resistance that can potentially suppress the demodulation jump occurrence. By simulations and experimental validations, the proposed algorithm demonstrates its capability of achieving the Cramer-Rao bound over a large dynamic range of optical pathlengths, initial phases and signal-to-noise ratios.
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Gu, Ye. "Quantitative magnetization transfer imaging: validation and analysis tool development." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=123021.

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An on-resonance balanced steady-state free precession technique for quantitative magnetization transfer (qMT) imaging is examined through an initial validation process against the existing "gold-standard" off-resonance spoiled gradient-echomodel. Numerical simulation and sensitivity analysis of the analytical model are performed and confirm the reliability of the analytical model for the normal range of magnetization transfer (MT) parameters. In vivo comparison betweenbalanced steady-state free precession and spoiled-gradient models show agreement between the two models. This new model is shown to be valid and promises to have advantages over the existing methods for its clinical practicality.A user-friendly software package for qMT simulation as well as data analysis and model fitting was also developed as part of this project. The package will be released in the public domain, with the intention to become a standard tool forqMT researchers and users.
Au travers d'un processus de validation initiale, nous comparons une technique d'imagerie quantitative par transfert d'aimantation (qMT) basée sur une séquence ≪en résonance≫ en précession libre avec état d'équilibre et gradients équilibrés, à la référence communément admise que constitue le modèle ≪hors-résonance≫ en écho de gradient avec destruction de l'aimantation transversale résiduelle.Nous réalisons une simulation numérique et une analyse de sensibilité du modèle analytique et confirmons ainsi la fiabilité de ce dernier dans une gamme habituelle de paramètres de transfert d'aimantation.La comparaison in-vivo entre le modèle en état d'équilibre à précession libre et le modèle avec destruction de l'aimantation transversale résiduelle montre une cohérence. Ce nouveau modèle apparat comme valide et semble prometteur en terme d'utilisation clinique de par sa facilité d'utilisation, comparé aux méthodes existantes.Dans le cadre de ce projet, nous avons également développé un logiciel de simulation du transfert d'aimantation quantitatif facile d'emploi, ainsi qu'un outil d'analyse des données et d'ajustement du modèle. Le logiciel est sur le point d'être proposé dans le domaine public et nous espérons qu'il devienne un outil d'analyse standard pour les chercheurs et les utilisateurs du transfert d'aimantation quantitatif.
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Agrawal, Vishesh. "Quantitative Imaging Analysis of Non-Small Cell Lung Cancer." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:27007763.

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Quantitative imaging is a rapidly growing area of interest within the field of bioinformatics and biomarker discovery. Due to the routine nature of medical imaging, there is an abundance of high-quality imaging linked to clinical and genetic data. This data is particularly relevant for cancer patients who receive routine CT imaging for staging and treatment purposes. However, current analysis of tumor imaging is generally limited to two-dimensional diameter measurements and assessment of anatomic disease spread. This conventional tumor-node-metastasis (TNM) staging system stratifies patients to treatment protocols including decisions regarding adjuvant therapy. Recently there have been several studies suggesting that these images contain additional unique information regarding tumor phenotype that can further aid clinical decision-making. In this study I aimed to develop the predictive capability of medical imaging. I employed the principles of quantitative imaging and applied them to patients with non-small cell lung cancer (NSCLC). Quantitative imaging, also termed radiomics, seeks to extract thousands of imaging data points related to tumor shape, size and texture. These data points can potentially be consolidated to develop a tumor signature in the same way that a tumor might contain a genetic signature corresponding to mutational burden. To accomplish this I applied radiomics analyses to patients with early and late stage NSCLC and tested these for correlation with both histopathological data as well as clinical outcomes. Patients with both early and late stage NSCLC were assessed. For locally advanced NSCLC (LA-NSCLC), I analyzed patients treated with preoperative chemoradiation followed by surgical resection. To assess early stage NSCLC, I analyzed patients treated with stereotactic body radiation therapy (SBRT). Quantitative imaging features were extracted from CT imaging obtained prior to chemoradiation and post-chemoradiation prior to surgical resection. For patients who underwent SBRT, quantitative features were extracted from cone-beam CTs (CBCT) at multiple time points during therapy. Univariate and multivariate logistic regression were used to determine association with pathologic response. Concordance-index and Kaplan-Meier analyses were applied to time dependent endpoints of overall survival, locoregional recurrence-free and distant metastasis. In this study, 127 LA-NSCLC patients were identified and treated with preoperative chemoradiation and surgical resection. 99 SBRT patients were identified in a separate aim of this study. Reduction of CT-defined tumor volume (OR 1.06 [1.02-1.09], p=0.002) as continuous variables per percentage point was associated with pathologic complete response (pCR) and locoregional recurrence (LRR). Conventional response assessment determined by diameter (p=0.213) was not associated with pCR or any survival endpoints. Seven texture features on pre-treatment tumor imaging were associated with worse pathologic outcome (AUC 0.61-0.66). Quantitative assessment of lymph node burden demonstrated that pre-treatment and post-treatment volumes are significantly associated with both OS and LRR (CI 0.62-0.72). Textural analyses of these lymph nodes further identified 3 unique pre-treatment and 7 unique post-treatment features significantly associated with either LRR, DM or OS. Finally early volume change showed associated with overall survival in CBCT scans of early NSCLC. Quantitative assessment of NSCLC is thus strongly associated with pathologic response and survival endpoints. In contrast, conventional imaging response assessment was not predictive of pathologic response or survival endpoints. This study demonstrates the novel application of radiomics to lymph node texture, CBCT volume and patients undergoing neoadjuvant therapy for NSCLC. These examples highlight the potential within the rapidly growing field of quantitative imaging to better describe tumor phenotype. These results provide evidence to the growing radioimics literature that there is significant association between imaging, pathology and clinical outcomes. Further exploration will allow for more complete models describing tumor imaging phoentype with clinical outcomes.
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Hinsdale, Taylor A. "Laser Speckle Imaging: A Quantitative Tool for Flow Analysis." DigitalCommons@CalPoly, 2014. https://digitalcommons.calpoly.edu/theses/1251.

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Laser speckle imaging, often referred to as laser speckle contrast analysis (LASCA), has been sought after as a quasi-real-time, full-field, flow visualization method. It has been proven to be a valid and reliable qualitative method, but there has yet to be any definitive consensus on its ability to be used as a quantitative tool. The biggest impediment to the process of quantifying speckle measurements is the introduction of additional non dynamic speckle patterns from the surroundings. The dynamic speckle pattern under investigation is often obscured by noise caused by background static speckle patterns. One proposed solution to this problem is known as dynamic laser speckle imaging (dLSI). dLSI attempts to isolate the dynamic speckle signal from the previously mentioned background and provide a consistent dynamic measurement. This paper will investigate the use of this method over a range of experimental and simulated conditions. While it is believable that dLSI could be used quantitatively, there were inconsistencies that arose during analysis. Simulated data showed that if the mixed dynamic and static speckle patterns were modeled as the sum of two independent speckle patterns, increasing static contributions led to decreasing dynamic contrast contributions, something not expected by theory. Experimentation also showed that there were scenarios where scattering from the dynamic media obscured scattering from the static medium, resulting in poor estimates of the velocities causing the dynamic scattering. In light of these observations, steps were proposed and outlined to further investigate into this method. With more research it should be possible to create a set of conditions where dLSI is known be accurate and quantitative.
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Elagamy, Samar H. "Advancing ATR-FTIR Imaging into The Realm or Quantitative Analysis." Miami University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=miami1574416533908128.

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Premraj, Senthil Kumar. "Facilitating four-dimensional quantitative analysis of aortic MRI for clinical use." Thesis, University of Iowa, 2009. https://ir.uiowa.edu/etd/260.

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Marfan Syndrome leads to the weakening of the thoracic aorta and ultimate rupture causing death of the patient. Current monitoring method involves measuring the diameter of the aorta near the heart. Our approach is to develop a new technology that will provide clinicians the ability to evaluate the size, shape and motion of the entire thoracic aorta using four-dimensional cardiac MRI. This project alters the existing research algorithms to provides an integrated application for processing the images and provides novel measurements about the aorta from a data set of 32 normal subjects and 38 patients with serial scans.
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Snyder, William C. "An in-scene parameter estimation method for quantitative image analysis /." Online version of thesis, 1994. http://hdl.handle.net/1850/11061.

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Biffar, Andreas. "Quantitative Analysis of Diffusion-weighted Magnetic Resonance Imaging in the Spine." Diss., lmu, 2010. http://nbn-resolving.de/urn:nbn:de:bvb:19-126230.

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Wang, Kun. "Medical imaging of the heart : quantitative analysis of three-dimensional echocardiographic images." Thesis, University of Newcastle Upon Tyne, 2011. http://hdl.handle.net/10443/1392.

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Accurate, reproducible determination of cardiac chamber volume, especially left ventricular (LV) volume, is important for clinical assessment, risk stratification, selection of therapy, and serial monitoring of patients with cardiovascular disease. Echocardiography is the most widely used imaging modality in the clinical diagnosis of left ventricular function abnormalities. In the last 15 years, developments in real time three-dimensional echocardiography (RT3DE) have achieved superior accuracy and reproducibility compared with conventional two-dimensional echocardiography (2DE) for measurement of left ventricular volume and function. However, RT3DE suffers from the limitations inherent to the ultrasonic imaging modality and the cost of increased effort of data handling and image analysis. There were two aims of this research project. Firstly, it aimed to develop different new semi-automated algorithms for LV endocardial surface delineation, LV volume and EF quantification from clinical RT3DE images. Secondly, through assessing and comparing the performance of these algorithms in the aspects of accuracy and reproducibility, this project aimed to investigate what factors in real time 3D echo images influenced the performance of each algorithm, so that advantages and drawbacks of 3D echo images can be better understood. The basic structure of the content of this thesis is as follows: Chapter 1 introduces the background and the aims of this project. Chapter 2 describes the development of the new semi-automated algorithms. Chapter 3 to Chapter 6 presents the four studies designed to assess and compare the accuracy and reproducibility of each algorithm. These studies were the balloon phantom study, the tissue-mimicking phantom study, the clinical cardiac magnetic resonance images study and the clinical contrast enhanced 3D stress echo images study. Chapter 7 summarises all these studies, draws conclusions, and describes future work. In conclusion, it has been shown that the semi-automated algorithms can measure LV volume and EF quantitatively in clinical 3D echo images. To achieve better accuracy and reproducibility, 3D echo images should be analysed from all three dimensions.
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Books on the topic "Quantitative imaging analysis"

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Zaidi, Habib, ed. Quantitative Analysis in Nuclear Medicine Imaging. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/b107410.

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Brandt, Roland, and Lidia Bakota. Laser scanning microscopy and quantitative image analysis of neuronal tissue. New York: Humana Press, 2014.

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Rozenblat, Céline. Methods for Multilevel Analysis and Visualisation of Geographical Networks. Dordrecht: Springer Netherlands, 2013.

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Miller, James G. Physical interpretation and development of ultrasonic nondestructive evaluation techniques applied to the quantitative characterization of textile composite materials: Semiannual progress report, September 15, 1994 - March 14, 1995. St. Louis, Mo. : Washington University, Dept. of Physics, Laboratory for Ultrasonics: National Aeronautics and Space Administration, 1995.

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Miller, James G. Physical interpretation and development of ultrasonic nondestructive evaluation techniques applied to the quantitative characterization of textile composite materials: Semiannual progress report : March 15, 1992-September 14, 1992. St. Louis, Mo: Washington University, Dept. of Physics, Laboratory for Ultrasonics, 1992.

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Xavier, Ronot, and Usson Yves, eds. Imaging of nucleic acids and quantitation in photonic microscopy. Boca Raton, FL: CRC Press, 2001.

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Dolgov, I., Mihail Volovik, and Andrey Mahnovskiy. Thermographic signs of certain diseases of the respiratory system (acute sinusitis, pneumonia) Thermography Atlas. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/textbook_61b1ab7de6b1f9.69203696.

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The present issue focuses on the practice of medical thermal imaging in patients with paranasal sinusitis and pneumonia. The description of thermograms is based on a quantitative analysis of temperature gradients and trends in temperature of different body regions (Projection «head front» for paranasal sinusitis, «breast front» and «back», in a defined layout formed in «cloud» thermograms analysis program "Tvision" of «Dignosis», Russia) with values of thermographic markers that demonstrated their differentiating capabilities when compared with reference methods. Thus, the thermographic conclusion is formed not simply by thermal phenomenon «hot-cold», but on the basis of numerical values of markers, which indicate hypothetical nosological diagnosis and significantly simplifies the algorithm for those physicians who use this method as an additional. The publication is intended for doctors of any speciality who, in their daily clinical practice, treat the patients with suspicions disease of respiratory system
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Zaidi, Habib. Quantitative Analysis in Nuclear Medicine Imaging. Springer, 2005.

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Zaidi, Habib. Quantitative Analysis in Nuclear Medicine Imaging. Springer, 2010.

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Zaidi, Habib. Quantitative Analysis in Nuclear Medicine Imaging. Springer, 2006.

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Book chapters on the topic "Quantitative imaging analysis"

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Gremse, Felix. "Qualitative and Quantitative Data Analysis." In Small Animal Imaging, 529–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42202-2_19.

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Gremse, Felix, and Volkmar Schulz. "Qualitative and Quantitative Data Analysis." In Small Animal Imaging, 363–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-12945-2_25.

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Bentourkia, M’hamed. "Quantitative Analysis in PET Imaging." In Basic Sciences of Nuclear Medicine, 551–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65245-6_21.

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Meijering, Erik, and Gert van Cappellen. "Quantitative Biological Image Analysis." In Imaging Cellular and Molecular Biological Functions, 45–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71331-9_2.

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Flinton, David M., and Christina Malamateniou. "Quantitative Methods and Analysis." In Medical Imaging and Radiotherapy Research: Skills and Strategies, 273–322. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37944-5_15.

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Buvat, Irène. "Quantitative Image Analysis in Tomography." In Handbook of Particle Detection and Imaging, 1043–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-13271-1_41.

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Buvat, Irène. "Quantitative Image Analysis in Tomography." In Handbook of Particle Detection and Imaging, 1–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-47999-6_41-2.

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Buvat, Irène. "Quantitative Image Analysis in Tomography." In Handbook of Particle Detection and Imaging, 1407–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-93785-4_41.

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Van Laere, K., and H. Zaidi. "Quantitative Analysis in Functional Brain Imaging." In Quantitative Analysis in Nuclear Medicine Imaging, 435–70. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-25444-7_14.

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Tortoli, P., F. Valgimigli, and V. L. Newhouse. "Quantitative Flow Measurement Through Doppler Analysis at Right Angle." In Acoustical Imaging, 329–34. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3370-2_52.

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Conference papers on the topic "Quantitative imaging analysis"

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Uihdeniemi, M., A. Ekholm, and O. Santamaki. "Thermal imaging and frequency analysis." In 1996 Quantitative InfraRed Thermography. QIRT Council, 1996. http://dx.doi.org/10.21611/qirt.1996.046.

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Hockney, David, and Charles M. Falco. "Quantitative analysis of qualitative images." In Electronic Imaging 2005, edited by Bernice E. Rogowitz, Thrasyvoulos N. Pappas, and Scott J. Daly. SPIE, 2005. http://dx.doi.org/10.1117/12.601937.

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Loncaric, Sven, Atam P. Dhawan, Dubravko Cosic, Domagoj Kovacevic, Joseph Broderick, and Thomas Brott. "Quantitative intracerebral brain hemorrhage analysis." In Medical Imaging '99, edited by Kenneth M. Hanson. SPIE, 1999. http://dx.doi.org/10.1117/12.348648.

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Kiraly, Atilla P., Joseph M. Reinhardt, Eric A. Hoffman, Geoffrey McLennan, and William E. Higgins. "Virtual bronchoscopy for quantitative airway analysis." In Medical Imaging, edited by Amir A. Amini and Armando Manduca. SPIE, 2005. http://dx.doi.org/10.1117/12.595283.

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Reeves, Anthony P. "A Development Methodology for Automated Measurement of Quantitative Image Biomarkers: Analysis of Chest CT Images." In Quantitative Medical Imaging. Washington, D.C.: OSA, 2013. http://dx.doi.org/10.1364/qmi.2013.qtu1g.3.

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Upputuri, Paul Kumar, and Manojit Pramanik. "Multiple wavelength fringe analysis for surface profile measurements." In Quantitative Phase Imaging V, edited by Gabriel Popescu and YongKeun Park. SPIE, 2019. http://dx.doi.org/10.1117/12.2508310.

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Hayden, Oliver. "Hematology analysis with holographic imaging cytometry (Conference Presentation)." In Quantitative Phase Imaging V, edited by Gabriel Popescu and YongKeun Park. SPIE, 2019. http://dx.doi.org/10.1117/12.2513270.

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Chrzanowski, K., and Z. Jankiewicz. "Accuracy analysis of measuring thermal imaging systems." In 1994 Quantitative InfraRed Thermography. QIRT Council, 1994. http://dx.doi.org/10.21611/qirt.1994.009.

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Pavillon, Nicolas, Alison J. Hobro, and Nicholas I. Smith. "High-throughput analysis at single-cell level through multimodal label-free microscopy." In Quantitative Phase Imaging V, edited by Gabriel Popescu and YongKeun Park. SPIE, 2019. http://dx.doi.org/10.1117/12.2512665.

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Chou, Pei-Lin, and Snow H. Tseng. "PSTD simulation analysis of light transmission through cornea-like transparent scattering medium." In Quantitative Phase Imaging VI, edited by Gabriel Popescu, YongKeun Park, and Yang Liu. SPIE, 2020. http://dx.doi.org/10.1117/12.2543266.

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Reports on the topic "Quantitative imaging analysis"

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Field, Aaron S. Voxel-Wise Time-Series Analysis of Quantitative MRI in Relapsing-Remitting MS: Dynamic Imaging Metrics of Disease Activity Including Pre-Lesional Changes. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada561881.

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Field, Aaron S. Voxel-Wise Time-Series Analysis of Quantitative MRI in Relapsing-Remitting MS: Dynamic Imaging Metrics of Disease Activity Including Pre-Lesional Changes. Fort Belvoir, VA: Defense Technical Information Center, October 2013. http://dx.doi.org/10.21236/ada596670.

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He, Yuhao, Yujia Yan, and Sunfu Zhang. Quantitative liver surface nodularity score based on imaging for assessment of early cirrhosis in patients with chronic liver disease: A protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2020. http://dx.doi.org/10.37766/inplasy2020.10.0096.

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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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Abstract:
This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.
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