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Books on the topic 'Diagnostic imaging. Algorithms'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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1

1937-, Höhne K. H., Fuchs Henry 1948-, Pizer Stephen M, and North Atlantic Organization. Scientific Affairs Division., eds. 3D imaging in medicine: Algorithms, systems, applications. Berlin: Springer-Verlag, 1990.

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2

D, Bartz, ed. Visualization in medicine: Theory, algorithms, and applications. Amsterdam: Morgan Kaufmann, 2007.

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3

Loizou, Christos P. Despeckle filtering algorithms and software for ultrasound imaging. San Rafael, Calif (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool Publishers, 2008.

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4

service), SpringerLink (Online, ed. Guide to Medical Image Analysis: Methods and Algorithms. London: Springer London, 2012.

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5

Haynor, David R., and Sébastien Ourselin. Medical imaging 2012: Image processing : 6-9 February 2012, San Diego, California, United States. Edited by SPIE (Society) and Agilent Technologies. Bellingham, Wash: SPIE, 2012.

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6

service), SpringerLink (Online, ed. Chest Imaging: An Algorithmic Approach to Learning. New York, NY: Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., 2012.

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7

Stanescu, Liana. Creating New Medical Ontologies for Image Annotation: A Case Study. New York, NY: Springer Science+Business Media, LLC, 2012.

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8

R, Paulsen Rasmus, Zhang Yongjie, and SpringerLink (Online service), eds. Mesh Processing in Medical Image Analysis 2012: MICCAI 2012 International Workshop, MeshMed 2012, Nice, France, October 1, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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9

Tianming, Liu, Shen Dinggang, Westin Carl-Fredrik, Shen Li, and SpringerLink (Online service), eds. Multimodal Brain Image Analysis: Second International Workshop, MBIA 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1-5, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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10

L, Eisenberg Ronald, ed. Diagnostic imaging. Lippincott, 1988.

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11

Eisenberg, Ronald L. Diagnostic Imaging: An Algorithmic Approach. Lippincott Williams & Wilkins, 1988.

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12

L, Eisenberg Ronald, ed. Diagnostic imaging: An algorithmic approach. Philadelphia: Lippincott, 1988.

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13

Toennies, Klaus D. Guide to Medical Image Analysis: Methods and Algorithms. Springer, 2014.

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14

Toennies, Klaus D. Guide to Medical Image Analysis: Methods and Algorithms. Springer, 2018.

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15

Preim, Bernhard, and Dirk Bartz. Visualization in Medicine: Theory, Algorithms, and Applications (The Morgan Kaufmann Series in Computer Graphics) (The Morgan Kaufmann Series in Computer Graphics). Morgan Kaufmann, 2007.

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16

Fuchs, Henry, and K. H. Hohne. 3D Imaging in Medicine: Algorithms, Systems, Applications (Nato a S I Series Series III, Computer and Systems Sciences). Springer, 1990.

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17

Preim, Bernhard, and Dirk Bartz. Visualization in Medicine: Theory, Algorithms, and Applications (The Morgan Kaufmann Series in Computer Graphics) (The Morgan Kaufmann Series in Computer Graphics). Morgan Kaufmann, 2007.

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18

Visual Computing For Medicine Theory Algorithms And Applications. Elsevier Science & Technology, 2013.

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19

Bruno, Michael A. Error and Uncertainty in Diagnostic Radiology. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190665395.001.0001.

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Diagnostic radiology is a medical specialty that is primarily devoted to the diagnostic process, centered on the interpretation of medical images. This book reviews the high level of uncertainty inherent to radiological interpretation and the overlap that exists between the uncertainty of the process and what might be considered “error.” There is also a great deal of variability inherent in the physical and technological aspects of the imaging process itself. The information in diagnostic images is subtly encoded, with a broad range of “normal” that usually overlaps the even broader range of “abnormal.” Image interpretation thus blends technology, medical science, and human intuition. To develop their skillset, radiologists train intensively for years, and most develop a remarkable level of expertise. But radiology itself remains a fallible human endeavor, one involving complex neurophysiological and cognitive processes employed under a range of conditions and generally performed under time pressure. This book highlights the human experience of error. A taxonomy of error is presented, along with a theoretical classification of error types based on the underlying causes and an extensive discussion of potential error-reduction strategies. The relevant perceptual science, cognitive science, and imaging science are reviewed. A chapter addresses the issue of accountability for error, including peer review, regulatory oversight/accreditation, and malpractice litigation. The potential impact of artificial intelligence, including the use of machine learning and deep-learning algorithms, to reduce human error and improve radiologists’ efficiency is also explored.
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20

J, Gelfand Michael, and Thomas Stephen R, eds. Effective use of computers in nuclear medicine: Practical clinical applications in the imaging laboratory. New York: McGraw-Hill, 1988.

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21

Charalel, Resmi A., and Martin R. Prince. Contrast Agents. Edited by Bradley B. Pua, Anne M. Covey, and David C. Madoff. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190276249.003.0003.

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Imaging is reliant upon the contrast between different body elements, which may be present naturally or may require the introduction of extrinsic contrast agents. Since the 1920s, the use of contrast agents has been refined to enhance the diagnostic potential of multiple imaging modalities. Contrast agents are a vital part of diagnosis and treatment algorithms involving image guidance. Given the wealth of contrast agents on the market, a basic understanding of the various types is critical for budding interventional radiologists who need to use such agents judiciously on a daily basis. Such contrast agents may be administered intravenously, intraarterially, intrathecally, orally, via inhalation, transrectally, or via indwelling tubes or catheters cannulating a specific viscus. In this chapter, we review the key categories, contraindications, and alternatives for such agents, with special attention to their use in an interventional radiology (IR) practice.
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22

(Editor), Alexander Horsch, Thomas M. Deserno (Editor), Heinz Handels (Editor), Hans-Peter Meinzer (Editor), and Thomas Tolxdoff (Editor), eds. Bildverarbeitung für die Medizin 2007: Algorithmen - Systeme - Anwendungen (Informatik aktuell). Springer, 2007.

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23

Lee, Christoph I. Multidetector CT for Acute Appendicitis in Adults. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190223700.003.0027.

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This chapter, found in the abdominal and pelvic pain section of the book, provides a succinct synopsis of a key study examining the use of multidetector computed tomography for diagnosing acute appendicitis in adults. This summary outlines the study methodology and design, major results, limitations and criticisms, related studies and additional information, and clinical implications. This technique was shown to reduce rates of perforation and negative findings at appendectomy when incorporated into routine diagnostic algorithms, and can redirect management for patients with alternative diagnoses. In addition to outlining the most salient features of the study, a clinical vignette and imaging example are included in order to provide relevant clinical context.
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24

Folio, Les R. Chest Imaging: An Algorithmic Approach to Learning. Springer, 2011.

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25

Bildverarbeitung Fr Die Medizin 2008 Algorithmen Systeme Anwendungen Proceedings Des Workshops Vom 6 Bis 8 April 2008 In Berlin. Springer, 2008.

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26

(Editor), Jasjit S. Suri, S. Kamaledin Setarehdan (Editor), and Sameer Singh (Editor), eds. Advanced Algorithmic Approaches to Medical Image Segmentation: State Of The Art Applications in Cardiology, Neurology, Mammography and Pathology. Springer, 2002.

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27

Butler, Reni S. Architectural Distortion (Radial Scar). Edited by Christoph I. Lee, Constance D. Lehman, and Lawrence W. Bassett. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190270261.003.0030.

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Radial scars are benign lesions of the breast characterized pathologically by a fibroelastic core containing entrapped ducts and lobules that radiate outwards in a stellate pattern. This chapter, highlighting radial scar as a cause of architectural distortion, reviews its imaging features and differential diagnosis on mammography, digital breast tomosynthesis, ultrasound, and MRI; its diagnostic workup using multiple modalities; and its histological confirmation with image-guided core needle biopsy. The particular challenge of radial scar presenting as architectural distortion seen only with tomosynthesis is discussed, along with an algorithm for imaging evaluation and biopsy guidance in this setting. As radial scar, which is histologically related to complex sclerosing lesion and radial sclerosing lesion, is considered a high-risk lesion, management recommendations are also reviewed.
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28

(Editor), Jasjit S. Suri, and Swamy Laxminarayan (Editor), eds. PDE and Level Sets: Algorithmic Approaches to Static and Motion Imagery (Topics in Biomedical Engineering). Springer, 2002.

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29

PDE and level sets: Algorithmic approaches to static and motion imagery. New York: Kluwer Academic/Plenum Publishers, 2002.

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30

Zhang, Yongjie, Joshua A. Levine, and Rasmus R. Paulsen. Mesh Processing in Medical Image Analysis 2012: MICCAI 2012 International Workshop, MeshMed 2012, Nice, France, October 1, 2012, Proceedings. Springer, 2012.

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31

Shen, Li, Tianming Liu, Pew-Thian Yap, Carl-Fredrik Westin, and Dinggang Shen. Multimodal Brain Image Analysis: Second International Workshop, MBIA 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1-5, 2012, Proceedings. Springer, 2012.

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