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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_3592_Библиотеки_им_академика_М_И_Перельмана
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Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
[30] Banchhor S K et al 2016 Five multiresolution-based calcium volume measurement
techniques from coronary IVUS videos: a comparative approach Comput. Methods
Programs Biomed.
[31] Banchhor S K et al 2017 Well-balanced system for coronary calcium detection and volume
measurement in a low resolution intravascular ultrasound videos Comput. Biol. Med.
[32] Suri J S, Haralick R M and Sheehan F H 2000 Greedy algorithm for error correction in
automatically produced boundaries from low contrast ventriculograms Pattern Anal. Appl.
39–60
[33] Saba L et al 2013 Inter- and intra-observer variability analysis of completely automated
cIMT measurement software (AtheroEdge™) and its benchmarking against commercial
ultrasound scanner and expert readers Comput. Biol. Med.
[34] Haralick R M 1979 Statistical and structural approaches to texture Proc. IEEE 67 786–804
[35] Kalyan K et al 2014 Artificial neural network application in the diagnosis of disease
conditions with liver ultrasound images Adv. Bioinform.
[36] Weszka J S, Dyer C R and Rosenfeld A 1976 A comparative study of texture measures for
terrain classification IEEE Trans. Syst. Man. Cybern.
[37] Amadasun M and King R 1989 Textural features corresponding to textural properties IEEE
Trans. Syst. Man Cybern.
[38] Niu L et al 2013 Surface roughness detection of arteries via texture analysis of ultrasound
images for early diagnosis of atherosclerosis PLoS One
[39] Hu M-K 1962 Visual pattern recognition by moment invariants IRE Trans. Inf. Theory 8
179–87
[40] Wu C M and Chen Y C 1992 Statistical feature matrix for texture analysis CVGIP: Graph.
Models Image Process.
[41] Song F, Guo Z and Mei D 2010 Feature selection using principal component analysis Int.
Conf. on IEEE System Science, Engineering Design and Manufacturing Informatization
(ICSEM) vol 1 (Piscataway, NJ: IEEE) pp 27–30
[42] Burges C J 1998 A tutorial on support vector machines for pattern recognition Data Min.
Knowl. Discov.
[43] Sastry P 2003 An introduction to support vector machines Computing and Information
Sciences: Recent Trends pp 53–85
[44] Muller K R et al 2001 An introduction to kernel based learning algorithms IEEE Trans.
Neural Netw.
[45] David V and Sanchez A 2003 Advanced support vector machines and kernel methods Neuro
Comput. 55 5–20
[46] Rodriguez J D, Perez A and Lozano J A 2010 Sensitivity analysis of k-fold cross validation
in prediction error estimation IEEE Trans. Pattern Anal. Mach. Intell.
[47] Vapnik V 1998 Statistical Learning Theory vol 1 (New York: Wiley)
[48] Tabas I, Williams K J and Borén J 2007 Subendothelial lipoprotein in retention as the
initiating process in atherosclerosis: update and therapeutic implications Circulation
1832–44
[49] Setarehdan S K and Singh S 2002 Advanced Algorithmic Approaches to Medical Image
Segmentation: State-of-the-art Applications in Cardiology, Neurology, Mammography, and
Pathology (London: Springer)
[50] Campos C M et al 2015 Ex vivo validation of 45 MHz intravascular ultrasound backscatter
tissue characterization Eur. Heart J. Cardiovasc. Imaging
134 237–58
84 168–81
43 1261–72
2014 708279
6 269–85
19 1264–74
8 e76880
54 407–19
2 121–67
12 181–201
32 569–75
116
16 1112–9
3
14-34
Vascular and Intravascular Imaging Trends, Analysis, and Challenges, Volume 1
https://t.me/medicina_free
[51] Araki T et al 2017 Stroke risk stratification and its validation using ultrasonic echolucent
carotid wall plaque morphology: a machine learning paradigm Comput. Biol. Med.
[52] Saba L et al 2017 Plaque tissue morphology-based stroke risk stratification using carotid
ultrasound: a polling-based PCA learning paradigm J. Med. Syst.
[53] Scott D S et al 2000 Pathologic validation of a new method to quantify coronary calcific
deposits in vivo using intravascular ultrasound Am. J. Cardiol.
[54] Schamroth N, Gutstein A and Kornowski R 2017 Coronary artery calcium score: where do
we stand? Current uses and implications in asymptomatic patients Isr. Med. Assoc. J. 19
214–5
[55] Wahle A and Sonka M 2005 Coronary p laque analysis by multimodality fusion Studies in
Health Technology and Informatics, Plaque Imaging: Pixel to Molec ular Level ed J S Suri
et al vol 113 (Amsterdam: IOS) pp 321–59
[56] Molinari F, Zeng G and Suri J S 2010 Intima–media thickness: setting a standard for a
completely automated method of ultrasound measurement IEEE Trans. Ultrason.
Ferroelectr. Freq. Control
[57] Sanches M J, Laine A and Suri J S 2011 Ultrasound Imaging (New York: Springer)
[58] Saba L et al 2014 Multi-modality Atherosclerosis Imaging and Diagnosis (New York:
Springer)
[59] Saba L et al 2013 Association of automated carotid IMT measurement and HbA1c in
Japanese patients with coronary artery disease Diab. Res. Clin. Pract.
[60] Ikeda N et al 2014 Ankle–brachial index and its link to automated carotid ultrasound
measurement of intima–media thickness variability in 500 Japanese coronary artery disease
patients Curr. Atheroscler. Rep.
[61] Nezu T et al 2015 Carotid intima-media thickness for atherosclerosis J. Atheroscler. Thromb.
23 18–31
[62] Ogata T et al 2005 Atherosclerosis found on carotid ultrasonography is associated with
atherosclerosis on coronary intravascular ultrasonography J. Ultrasound Med.
[63] Akhtar M and Liu W 2016 Use of intravascular ultrasound vs optical coherence tomography
for mechanism and patterns of in-stent restenosis among bare metal stents and drug eluting
stents J. Thorac. Dis. 8 104–8
57 1112–24
16 1–8
41 98
85 37–40
100 348–53
80 77–96
24 469–74
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