A Review on Carotid Ultrasound Atherosclerotic Tissue Characterization and Stroke Risk Stratification in Machine Learning Framework | springermedizin.de

Springer Medizin

nach oben

Current Atherosclerosis Reports

Erschienen in:

01.09.2015 | Cardiovascular Disease and Stroke

A Review on Carotid Ultrasound Atherosclerotic Tissue Characterization and Stroke Risk Stratification in Machine Learning Framework

verfasst von: Aditya M. Sharma, Ajay Gupta, P. Krishna Kumar, Jeny Rajan, Luca Saba, Ikeda Nobutaka, John R. Laird, Andrew Nicolades, Jasjit S. Suri

Erschienen in: Current Atherosclerosis Reports | Ausgabe 9/2015

Einloggen, um Zugang zu erhalten

Abstract

Cardiovascular diseases (including stroke and heart attack) are identified as the leading cause of death in today’s world. However, very little is understood about the arterial mechanics of plaque buildup, arterial fibrous cap rupture, and the role of abnormalities of the vasa vasorum. Recently, ultrasonic echogenicity characteristics and morphological characterization of carotid plaque types have been shown to have clinical utility in classification of stroke risks. Furthermore, this characterization supports aggressive and intensive medical therapy as well as procedures, including endarterectomy and stenting. This is the first state-of-the-art review to provide a comprehensive understanding of the field of ultrasonic vascular morphology tissue characterization. This paper presents fundamental and advanced ultrasonic tissue characterization and feature extraction methods for analyzing plaque. Additionally, the paper shows how the risk stratification is achieved using machine learning paradigms. More advanced methods need to be developed which can segment the carotid artery walls into multiple regions such as the bulb region and areas both proximal and distal to the bulb. Furthermore, multimodality imaging is needed for validation of such advanced methods for stroke and cardiovascular risk stratification.

Anzeige

1.

Cardiovascular disease report. World heart federation 2010.http://www.world-heartfederation.org/fileadmin/user_upload/documents/WHD2010/FINALStateofthHeartCVDReport121010.pdf. 2010.

2.

WHO factsheet on cardiovascular disease. (Last updated on March 2013) http://www.who.int/mediacentre/factsheets/fs317/en/

3.

Giannoukos G, Min M. Mathematical and physical modelling of the dynamic fluidic impedance of arteries using electrical impedance equivalents. Math Methods Appl Sci. 2014;37(5):711–7.CrossRef

4.•

Friera F, Leticia, Ibáñez B, Fuster V. Imaging subclinical atherosclerosis: is it ready for prime time? A Rev J Cardiovasc Transl Res. 2014;7(7):623–34. This article offers an overview of the traditional and emerging non-invasive imaging modalities used to detect early atherosclerosis.CrossRef

5.

Katakami N, Takahara M, Kaneto H, Sakamoto KY, Yoshiuchi K, Irie Y, et al. Ultrasonic tissue characterization of carotid plaque improves the prediction of cardiovascular events in diabetic patients a pilot study. Diabetes Care. 2012;35(12):2640–6.PubMedCentralPubMedCrossRef

6.

Bots ML, Hoes AW, Koudstaal PJ, Hofman A, Grobbee DE. Common carotid intima-media thickness and risk of stroke and myocardial infarction the Rotterdam Study. Circulation. 1997;96(5):1432–7.PubMedCrossRef

7.

Wu TH, Kuo HC, Tain YL, Lin KM, Chien SJ. Common carotid artery intima-media thickness is useful for diagnosis of the acute stage of Kawasaki disease. BMC Paediatr. 2014;14(1):98.CrossRef

8.

Granada JF, Feinstein SB. Imaging of the vasa vasorum. Nat Clin Pract Cardiovasc Med. 2008;5 Suppl 2:S18–25.PubMedCrossRef

9.

Randoux B, Marro B, Koskas F, Duyme M, Sahel M, Zouaoui A, et al. Carotid artery stenosis: prospective comparison of CT, three-dimensional gadolinium-enhanced MR, and conventional angiography. Radiology. 2001;220(1):179–85.PubMedCrossRef

10.

Reiter M, Horvat R, Puchner S, Rinner W, Polterauer P, Lammer J, et al. Plaque imaging of the internal carotid artery-correlation of B-flow imaging with histopathology. Am J Neuroradiol. 2007;28(1):122–6.PubMed

11.

Das M, Braunschweig T, Mühlenbruch G, Mahnken AH, Krings T, Langer S, et al. Carotid plaque analysis: comparison of dual-source computed tomography (CT) findings and histopathological correlation. Eur J Vasc Endovasc Surg. 2009;38(1):14–9.PubMedCrossRef

12.

Schuster L, Hauser T, Essig M. Significance of MR angiography for imaging diagnostics of carotid artery diseases. Radiologe. 2010;50(7):597–606.PubMedCrossRef

13.

Wyman RA, Mays ME, McBride PE, Stein JH. Ultrasound-detected carotid plaque as a predictor of cardiovascular events. Vasc Med. 2006;11(2):123–30.PubMedCrossRef

14.

Polak JF, Shemanski L, O’Leary DH. Hypoechoic plaque at US of the carotid artery: an independent risk factor for incident stroke in adults aged 65 years or older. Cardiovasc Health Study Radiol. 1998;208(3):649–54.

15.

AbuRahma AF, Wulu JT, Crotty B. Carotid plaque ultrasonic heterogeneity and severity of stenosis. Stroke. 2002;33(7):1772–5.PubMedCrossRef

16.

Xiong L, Deng YB, Zhu Y, Liu YN, Bi XJ. Correlation of carotid plaque neovascularization detected by using contrast-enhanced US with clinical symptoms. Radiology. 2009;251(2):583–9.PubMedCrossRef

17.

Yoshida K, Narumi O, Chin M, Inoue K, Tabuchi T, Oda K, et al. Characterization of carotid atherosclerosis and detection of soft plaque with use of black-blood MR imaging. Am J Neuroradiol. 2008;29(5):868–74.PubMedCrossRef

18.•

Gupta A, Mtui EE, Baradaran H, Salama G, Pandya A, Kamel H, et al. CT angiographic features of symptom-producing plaque in moderate-grade carotid artery stenosis. Am J Neuroradiol. 2014;36(5):1–8. This study is about the association between soft and hard (calcified) plaque thickness measurements on CTA and symptomatic disease status in patients with moderate-grade carotid artery stenosis.

19.

Gupta A, Baradaran H, Kamel H, Pandya A, Mangla A, Dunning A, et al. Evaluation of computed tomography angiography plaque thickness measurements in high-grade carotid artery stenosis. Stroke. 2014;45(3):740–5.PubMedCrossRef

20.

Madani A, Beletsky V, Tamayo A, Munoz C, Spence JD. High-risk asymptomatic carotid stenosis ulceration on 3D ultrasound vs TCD microemboli. Neurology. 2011;77(8):744–50.PubMedCrossRef

21.••

Saba L, Anzidei M, Marincola BC, Piga M, Raz E, Bassareo PP, et al. Imaging of the carotid artery vulnerable plaque. Cardiovasc Intervent Radiol. 2014;37(3):572–85. This paper explains the concept of “vulnerable plaque” and also describes the imaging characteristics of “vulnerable carotid plaques.”.PubMedCrossRef

22.•

Joshi FR, Lindsay AC, Obaid DR, Falk E, Rudd JH. Non-invasive imaging of atherosclerosis. Eur Heart J-Cardiovasc Imaging. 2012;13(3):205–18. Explains the importance of imaging modalities such as ultrasound, computed tomography, magnetic resonance imaging and nuclear imaging methods and thier capability of reporting the underlying pathology.PubMedCrossRef

23.

Portanova A, Hakakian N, Mikulis DJ, Virmani R, Abdalla WM, Wasserman BA. Intracranial vasa vasorum: insights and implications for imaging. Radiology. 2013;267(3):667–79.PubMedCrossRef

24.

Carr S, Farb A, Pearce WH, Virmani R, Yao JS. Atherosclerotic plaque rupture in symptomatic carotid artery stenosis. J Vasc Surg. 1996;23(5):755–65.PubMedCrossRef

25.

Mughal MM, Khan MK, DeMarco JK, Majid A, Shamoun F, Abela GS. Symptomatic and asymptomatic carotid artery plaque. Expert Rev Cardiovasc Ther. 2011;9(10):1315–30.PubMedCentralPubMedCrossRef

26.

Keenan NG, Sheppard MN, Nott DM, Pennell DJ, Mohiaddin RH. Carotid plaque rupture. Lancet. 2009;374(9702):1703.PubMedCrossRef

27.

Lindenmaier TJ, Buchanan DN, Pike D, Hartley T, Reid RD, Spence JD, et al. One, two and threedimensional ultrasound measurements of carotid atherosclerosis before and after cardiac rehabilitation: preliminary results of a randomized controlled trial. Cardiovasc Ultrasound. 2013;11(1):39.PubMedCentralPubMedCrossRef

28.

McCarthy MJ, Loftus IM, Thompson MM, Jones L, London NJM, Bell PRF, et al. Angiogenesis and the atherosclerotic carotid plaque: an association between symptomatology and plaque morphology. J Vasc Surg. 1999;30(2):261–8.PubMedCrossRef

29.

Dunmore BJ, McCarthy MJ, Naylor AR, Brindle NP. Carotid plaque instability and ischemic symptoms are linked to immaturity of microvessels within plaques. J Vasc Surg. 2007;45(1):155–59.PubMedCrossRef

30.

Feinstein SB. The powerful microbubble: from bench to bedside, from intravascular indicator to therapeutic delivery system, and beyond. Am J Physiol Heart Circ Physiol. 2004;287(2):H450–7.PubMedCrossRef

31.

Shah F, Balan P, Weinberg M, Reddy V, Neems R, Feinstein M, et al. Contrast-enhanced ultrasound imaging of atherosclerotic carotid plaque neovascularization: a new surrogate marker of atherosclerosis? Vasc Med. 2007;12(4):291–7.PubMedCrossRef

32.

Dumont TM, Rughani AI. National trends in carotid artery revascularization surgery: clinical article. J Neurosurg. 2012;116(6):1251–7.PubMedCrossRef

33.

Schneider PA, Naylor AR. Asymptomatic carotid artery stenosis-medical therapy alone versus medical therapy plus carotid endarterectomy or stenting. Eur J Vasc Endovasc Surg. 2010;40(2):274–81.PubMedCrossRef

34.

Virmani R, Ladich ER, Burke AP, Kolodgie FD. Histopathology of carotid atherosclerotic disease. Neurosurgery. 2006;59(5):S219–S3.PubMed

35.

Loizou CP, Petroudi S, Pantziaris M, Nicolaides AN, Pattichis CS. An integrated system for the segmentation of atherosclerotic carotid plaque ultrasound video. IEEE Trans Ultrason Ferroelectr Freq Control. 2014;61(1):86–101.PubMedCrossRef

36.

El-Barghouty N, Nicolaides A, Bahal V, Geroulakos G, Androulakis A. The identification of the high risk carotid plaque. Eur J Vasc Endovasc Surg. 1996;11(4):470–8.PubMedCrossRef

37.

Lal BK, Hobson II RW, Pappas PJ, Kubicka R, Hameed M, Chakhtura EY, et al. Pixel distribution analysis of B-mode ultrasound scan images predicts histologic features of atherosclerotic carotid plaques. J Vasc Surg. 2002;35(6):1210–7.PubMedCrossRef

38.

Irie Y, Katakami N, Kaneto H, Takahara M, Sakamoto KY, Kosugi K, et al. The risk factors associated with ultrasonic tissue characterization of carotid plaque in type 2 diabetic patients. J Diabetes Complicat. 2014;28(4):523–7.PubMedCrossRef

39.

Nicolaides AN, Kakkos SK, Kyriacou E, Griffin M, Sabetai M, Thomas DJ, et al. Asymptomatic internal carotid artery stenosis and cerebrovascular risk stratification. J Vasc Surg. 2010;52(6):1486–96.PubMedCrossRef

40.

European Carotid Plaque Study Group. Carotid artery plaque composition—relationship to clinical presentation and ultrasound B-mode imaging. Eur J Vasc Endovasc Surg. 1995;10(1):23–30.CrossRef

41.

Chiu B, Shamdasani V, Entrekin R, Yuan C, Kerwin WS. Characterization of carotid plaques on 3-dimensional ultrasound imaging by registration with multicontrast magnetic resonance imaging. J Ultrasound Med. 2012;31(10):1567–80.PubMed

42.

Hitchner E, Zayed MA, Lee G, Morrison D, Lane B, Zhou W. Intravascular ultrasound as a clinical adjunct for carotid plaque characterization. J Vasc Surg. 2014;59(3):774–80.PubMedCentralPubMedCrossRef

43.

Irshad K, Millar S, Velu R, Reid AW, Diethrich EB, Reid DB. Virtual histology intravascular ultrasound in carotid interventions. J Endovasc Ther. 2007;14(2):198–207.PubMedCrossRef

44.

Diethrich EB, Margolis MP, Reid DB, Burke A, Ramaiah V, Rodriguez-Lopez JA, et al. Virtual histology intravascular ultrasound assessment of carotid artery disease: the Carotid Artery Plaque Virtual Histology Evaluation (CAPITAL) study. J Endovascther. 2007;14(5):676–86.

45.

González A, López-Rueda A, Gutiérrez I, Moniche F, Cayuela A, Bustamante A, et al. Carotid plaque characterization by virtual histology intravascular ultrasound related to the timing of carotid intervention. J Endovasc Ther. 2012;19(6):764–73.PubMedCrossRef

46.••

Acharya UR, Krishnan, Muthu Rama Krishnan M, Vinitha Sree S, Sanches J, Shafique S, et al. Plaque tissue characterization and classification in ultrasound carotid scans: a paradigm for vascular feature amalgamation. IEEE Trans Instrum Meas. 2013;62(2):392–400. Proposed a CAD system to effectively classify symptomatic and asymptomatic plaques from B-mode ultrasound carotid images. Several grayscale features are extracted that quantify the textural differences inherent in the plaque regions and selected the most significant among them.CrossRef

47.

Erikson KR, Fry FJ, Jones JP. Ultrasound in medicine—a review. IEEE Trans Sonics Ultrasonics. 1974;21(3):144–70.CrossRef

48.

Hurtut T, Gousseau Y, Schmitt F. Adaptive image retrieval based on the spatial organization of colors. Comput Vis Image Underst. 2008;112(2):101–13.CrossRef

49.

Christodoulou CI, Pattichis CS, Kyriacou E, Nicolaides A. Image retrieval and classification of carotid plaque ultrasound images. Open Cardiovasc Imaging J. 2010;2:18–28.CrossRef

50.

Wilhjelm JE, Gronholdt ML, Wiebe B, Jespersen SK, Hansen LK, Sillesen H. Quantitative analysis of ultrasound B-mode images of carotid atherosclerotic plaque: correlation with visual classification and histological examination. IEEE Trans Med Imaging. 1998;17(6):910–22.PubMedCrossRef

51.

Niu L, Qian M, Yang W, Meng L, Xiao Y, Wong KKL, et al. Surface roughness detection of arteries via texture analysis of ultrasound images for early diagnosis of atherosclerosis. PLoS ONE. 2013;8(10):e76880.PubMedCentralPubMedCrossRef

52.

Wagner RF, Smith SW, Sandrik JM, Lopez H. Statistics of speckle in ultrasound B-scans. IEEE Trans Sonics Ultrasonics. 1983;30(3):156–63.CrossRef

53.

Guan F, Ton P, Ge S, Zhao L. Anisotropic diffusion filtering for ultrasound speckle reduction. Sci China Technol Sci. 2014;57(3):607–14.CrossRef

54.

Magnin PA, von Ramm OT, Thurstone FL. Frequency compounding for speckle contrast reduction in phased array images. Ultrason Imaging. 1982;4(3):267–81.PubMedCrossRef

55.

Zacharatos H, Hassan AE, Qureshi AI. Intravascular ultrasound: principles and cerebrovascular applications. Am J Neuroradiol. 2010;31(4):586–97.PubMedCrossRef

56.

Christodoulou CI, Pattichis CS, Pantziaris M, Nicolaides A. Texture based classification of atherosclerotic carotid plaques. IEEE Trans Med Imaging. 2003;22(7):902–12.PubMedCrossRef

57.

Tsiaparas NN, Golemati S, Andreadis I, Stoitsis JS, Valavanis I, Nikita KS. Comparison of multiresolution features for texture classification of carotid atherosclerosis from B-mode ultrasound. IEEE Trans Inf Technol Biomed. 2011;15(1):130–7.PubMedCrossRef

58.

Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. IEEE Trans Syst Man Cybernet. 1973;3(6):610–21.CrossRef

59.

Gupta L, Srinath MD. Contour sequence moments for the classification of closed planar shapes. Pattern Recogn. 1987;20(3):267–72.CrossRef

60.

Asvestas P, Golemati S, Matsopoulos GK, Nikita KS, Nicolaides AN. Fractal dimension estimation of carotid atherosclerotic plaques from B-mode ultrasound: a pilot study. Ultrasound Med Biol. 2002;28(9):1129–36.PubMedCrossRef

61.

Wu CM, Chen YC, Hsieh KS. Texture features for classification of ultrasonic liver images. IEEE Trans Med Imaging. 1992;11(2):141–52.PubMedCrossRef

62.

Deans, S. R. The Radon transform and some of its applications. Courier Dover Publications 2007.

63.

Fogel I, Sagi D. Gabor filters as texture discriminator. Biol Cybern. 1989;61(2):103–13.CrossRef

64.

Acharya UR, Faust O, Alvin APC, Krishnamurthi G, Seabra JCR, Sanches J, et al. Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization. Comput Methods Prog Biomed. 2013;110(1):66–75.CrossRef

65.

Mirmehdi, M., Xie, X., Suri, J.S. Handbook of texture analysis. Imperial College Press 2009.

66.••

Acharya UR, Faust O, Alvin APC, Vinitha Sree S, Molinari F, Saba L, et al. Symptomatic vs. asymptomatic plaque classification in carotid ultrasound. J Med Syst. 2012;36(3):1861–71. This article presents a CAD) system which analyzes ultrasound images and classifies them into symptomatic and asymptomatic based on a combination of discrete wavelet transform, higher order spectra and textural features. Also proposed an integrated index, a unique number called symptomatic asymptomatic carotid index (SACI) to discriminate symptomatic and asymptomatic carotid ultrasound images.PubMedCrossRef

67.•

Acharya UR, VinithaSree S, Molinari F, Saba L, Nicolaides AN, Suri JS. An automated technique for carotid far wall classification using grayscale features and wall thickness variability. J Clin Ultrasound. 2014;42(5):302–11. This article describes a Computer Aided Diagnostic (CAD) method for identification of symptomatic and asymptomatic carotid ultrasound images to be applied for the early diagnosis of atherosclerosis, on images that could contain a light plaque.

68.

Abd-Allah F, Abo-Krysha N, Baligh E. Carotid atherosclerosis: socio demographic issues, the hidden dimensions. Perspect Med. 2012;1(1):167–9.CrossRef

69.

Shrivastava VK, Londhe ND, Sonawane RS, Suri JS. Reliable and accurate psoriasis disease classification in dermatology images using comprehensive feature space in machine learning paradigm. Exp Syst Appl. 2015;42(15):6184–95.CrossRef

70.

El-Baz A, Beache GM, Gimel’farb G, Suzuki K, Okada K, Elnakib A, et al. Computer-aided diagnosis systems for lung cancer: challenges and methodologies. Intl J Biomed Imaging. 2013;2013:1–46.

71.

Suri JS, Kathuria C, Molinari F. Atherosclerosis disease management. London: Springer; 2010.

72.

Sanches JM, Laine AF, Suri JS. Ultrasound imaging: advances and applications. London: Springer; 2012.CrossRef

73.

Wold S, Esbensen K, Geladi P. Principal component analysis. Chemom Intell Lab Syst. 1987;2(1):37–52.CrossRef

74.•

Acharya UR, Vinitha Sree S, Muthu Rama Krishnan M, Molinari F, Saba L, Ho SYS, et al. Atherosclerotic risk stratification strategy for carotid arteries using texture-based features. Ultrasound Med Biol. 2012;38(6):899–915. This article presents a CAD based ultrasound characterization methodology that classifies the patient into symptomatic and asymptomatic classes using two kinds of datasets: (1) plaque regions in ultrasound carotids segmented semi-automatically and (2) far wall gray-scale IMT regions along the common carotid artery segmented automatically.PubMedCrossRef

75.•

Acharya UR, Faust O, Vineetha Sree S, Molinari F, Saba L, Nicolaides A, et al. An accurate and generalized approach to plaque characterization in 346 carotid ultrasound scans. IEEE Trans Instrum Meas. 2012;61(4):1045–53. The article describes a patented CAD system called Atheromatic™ for symptomatic versus asymptomatic plaque classification in carotid ultrasound images. Feature extraction is done using a combination of discrete wavelet transform and averaging algorithms and SVM is used for classification.CrossRef

76.

Mougiakakou SG, Golemati S, Gousias I, Nicolaides AN, Nikita KS. Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws’ texture and neural networks. Ultrasound Med Biol. 2007;33(1):26–36.PubMedCrossRef

77.

Kyriacou E, Pattichis MS, Pattichis CS, Mavrommatis A, Christodoulou CI, Kakkos S, et al. Classification of atherosclerotic carotid plaques using morphological analysis on ultrasound images. J Appl Intell. 2009;30(1):3–23.CrossRef

78.

Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of International Joint Conferences on Artificial Intelligence 1995; 14(2): 1137–1145.

79.•

Pedro LM, Sanches JM, Seabra J, Suri JS, Fernandes e Fernandes J. Asymptomatic carotid disease—a new tool for assessing neurological risk. Echocardiography. 2014;31(3):353–61. A new computer-assisted HDU method to study the echomorphology of the carotid plaque and to determine a risk score for developing appropriate symptoms is proposed in this study. A clinical score—enhanced activity index (EAI)—was obtained and correlated with the presence or absence of ipsilateral appropriate ischemic symptoms.PubMedCrossRef

80.

Fletcher RH, Fletcher SW, Fletcher GS. Clinical epidemiology: the essentials. Lippincott: Williams & Wilkins; 2012.

81.

Fawcett T. An introduction to ROC analysis. Pattern Recogn Lett. 2006;27(8):861–74.CrossRef

82.

Flach, P. The many faces of ROC analysis in machine learning. ICML Tutorial 2004.

83.

Ferrero GVS, Seabra J, Leor OR, Vida AS, Aja-Fernández S, Palencia C, et al. Gamma mixture classifier for plaque detection in intravascular ultrasonic image. IEEE Trans Ultrason Ferroelectr Freq Control. 2014;61(1):44–61.CrossRef

84.

AbuRahma AF, Thiele SP, Wulu Jr JT. Prospective controlled study of the natural history of asymptomatic 60% to 69% carotid stenosis according to ultrasonic plaque morphology. J Vasc Surg. 2002;36(3):437–43.PubMedCrossRef

85.

Wold, H. Partial least squares. Encyclopedia of statistical sciences 1985.

86.

Scholkopft, B., Mullert, K.R. Fisher discriminant analysis with kernels. Neural networks for signal processing IX 1999.

87.

Molinari F, Zeng G, Suri JS. A state of the art review on intima-media thickness (IMT) measurement and wall segmentation techniques for carotid ultrasound. Comput Methods Prog Biomed. 2010;100(3):201–21.CrossRef

88.••

Molinari F, Meiburger KM, Saba L, Acharya UR, Ledda G, Zeng G, et al. Ultrasound IMT measurement on a multi-ethnic and multi-institutional database: our review and experience using four fully automated and one semi-automated methods. Comput Methods Prog Biomed. 2012;108(3):946–60. This article compares fully automated and semi-automated IMT measurement techniques. This was the first time that completely automated and user-driven techniques have been compared on a multi-ethnic dataset, acquired using multiple original equipment manufacturer (OEM) machines with different gain settings, representing normal and pathologic cases.CrossRef

89.

Steinl DC, Kaufmann BA. Ultrasound imaging for risk assessment in atherosclerosis. Int J Mol Sci. 2015;16(5):9749–69.PubMedCentralPubMedCrossRef

90.

Molinari F, Zeng G, Suri JS. Intima-media thickness: setting a standard for a completely automated method of ultrasound measurement. IEEE Trans Ultrason Ferroelectr Freq Control. 2010;57(5):1112–24.PubMedCrossRef

91.

Saba L, Meiburger KM, Molinari F, Ledda G, Anzidei M, Acharya UR, et al. Carotid IMT variability (IMTV) and its validation in symptomatic versus asymptomatic Italian population: can this be a useful index for studying symptomaticity? Echocardiography. 2012;29(9):1111–9.PubMedCrossRef

92.

Saba L, Mallarini G, Sanfilippo R, Zeng G, Montisci R. Intima media thickness variability (IMTV) and its association with cerebrovascular events: a novel marker of carotid atherosclerosis? Cardiovasc Diagn Ther. 2012;2(1):10–8.PubMedCentralPubMed

93.

Acharya, R. Advances in cardiac signal processing. S. M. Krishnan, J. A. Spaan, J. S. Suri (Eds.). Berlin: Springer 2007.

94.

Naghavi M, Libby P, Falk E, Casscells SW, Litovsky S, Rumberger J, et al. From vulnerable plaque to vulnerable patient: a call for new definitions and risk assessment strategies: part I. Circulation. 2003;108:1664–72.PubMedCrossRef

95.

Saba L, Potters F, van der Lugt A, Mallarini G. Imaging of the fibrous cap in atherosclerotic carotid plaque. Cardiovasc Intervent Radiol. 2010;33(4):681–9.PubMedCrossRef

96.

Virmani R, Burke AP, Kolodgie FD, Farb A. Pathology of the thin-cap fibroatheroma: a type of vulnerable plaque. J Interv Cardiol. 2003;16(3):267–72.PubMedCrossRef

97.

Spagnoli LG, Bonanno E, Sangiorgi G, Mauriello A. Role of inflammation in atherosclerosis. J Nucl Med. 2007;48(11):1800–15.PubMedCrossRef

98.

Maseri A, Fuster V. Is there a vulnerable plaque? Circulation. 2003;107(16):2068–71.PubMedCrossRef

99.

Naqvi TZ, Lee MS. Carotid intima-media thickness and plaque in cardiovascular risk assessment. J Am Coll Cardiol Img. 2014;7(10):1025–38.CrossRef

100.

Acharya UR, Mookiah MRK, Sree SV, Afonso D, Sanches J, Shafique S, et al. Atherosclerotic plaque tissue characterization in 2D ultrasound longitudinal carotid scans for automated classification: a paradigm for stroke risk assessment. Med Biol Eng Comput. 2013;51(5):513–23.PubMedCrossRef

101.

Kakkos SK, Stevens JM, Nicolaides AN, Kyriacou E, Pattichis CS, Geroulakos G, et al. Texture analysis of ultrasonic images of symptomatic carotid plaques can identify those plaques associated with ipsilateral embolic brain infarction. Eur J Vasc Endovasc Surg. 2007;33(4):422–9.PubMedCrossRef

102.

Kyriacou EC, Petroudi S, Pattichis CS, Pattichis MS, Griffin M, Kakkos S, et al. Prediction of high-risk asymptomatic carotid plaques based on ultrasonic image features. IEEE Trans Inf Technol Biomed. 2012;16(5):966–73.PubMedCrossRef

103.

Tsiaparas NN, Golemati S, Andreadis I, Stoitsis J, Valavanis I, Nikita KS. Assessment of carotid atherosclerosis from B-mode ultrasound images using directional multi scale texture features. Meas Sci Technol. 2012;23(11):114004.CrossRef

Titel: A Review on Carotid Ultrasound Atherosclerotic Tissue Characterization and Stroke Risk Stratification in Machine Learning Framework
verfasst von: Aditya M. Sharma
Ajay Gupta
P. Krishna Kumar
Jeny Rajan
Luca Saba
Ikeda Nobutaka
John R. Laird
Andrew Nicolades
Jasjit S. Suri
Publikationsdatum: 01.09.2015
Verlag: Springer US
Erschienen in: Current Atherosclerosis Reports / Ausgabe 9/2015
Print ISSN: 1523-3804
Elektronische ISSN: 1534-6242
DOI: https://doi.org/10.1007/s11883-015-0529-2

Leitlinien kompakt für die Innere Medizin

Mit medbee Pocketcards sicher entscheiden.

^{Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag}

Kostenlos registrieren

Anzeige

Neu im Fachgebiet Innere Medizin

Erhebliches Risiko für Kehlkopfkrebs bei mäßiger Dysplasie

29.05.2024 Larynxkarzinom Nachrichten

Fast ein Viertel der Personen mit mäßig dysplastischen Stimmlippenläsionen entwickelt einen Kehlkopftumor. Solche Personen benötigen daher eine besonders enge ärztliche Überwachung.

Nach Herzinfarkt mit Typ-1-Diabetes schlechtere Karten als mit Typ 2?

29.05.2024 Herzinfarkt Nachrichten

Bei Menschen mit Typ-2-Diabetes sind die Chancen, einen Myokardinfarkt zu überleben, in den letzten 15 Jahren deutlich gestiegen – nicht jedoch bei Betroffenen mit Typ 1.

15% bedauern gewählte Blasenkrebs-Therapie

29.05.2024 Urothelkarzinom Nachrichten

Ob Patienten und Patientinnen mit neu diagnostiziertem Blasenkrebs ein Jahr später Bedauern über die Therapieentscheidung empfinden, wird einer Studie aus England zufolge von der Radikalität und dem Erfolg des Eingriffs beeinflusst.

Costims – das nächste heiße Ding in der Krebstherapie?

28.05.2024 Onkologische Immuntherapie Nachrichten

„Kalte“ Tumoren werden heiß – CD28-kostimulatorische Antikörper sollen dies ermöglichen. Am besten könnten diese in Kombination mit BiTEs und Checkpointhemmern wirken. Erste klinische Studien laufen bereits.

Update Innere Medizin

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen