Effect of geometric-based coronary calcium volume as a feature along with its shape-based attributes for cardiological risk prediction from low contrast intravascular ultrasound

Journal of Medical Imaging and Health Informatics

Volumn 4, Issue 2, 2014, Pages 255-261

  Araki, Tadashi ^a   Ikeda, Nobutaka ^b   Molinari, Filippo ^c   Dey, Nilanjan ^d   Suvojit, Acharjee ^d   Saba, Luca ^f   Nicolaides, Andrew ^g,h   Suri, Jasjit S ^d,e,i  

^a TOHO UNIVERSITY OHASHI MEDICAL CENTER   (Japan)

^b NATIONAL CENTER FOR GLOBAL HEALTH AND MEDICINE   (Japan)

^c POLITECNICO DI TORINO   (Italy)

^d AtheroPoint LLC   (United States)

^e Global Biomedical Technologies Inc   (United States)

^f DEPARTMENT OF RADIOLOGY   (Italy)

^g Vascular Screening and Diagnostic Centre   (United Kingdom)

^h UNIVERSITY OF CYPRUS   (Cyprus)

ⁱ IDAHO STATE UNIVERSITY   (United States)

Author keywords

Calcium Lesions; Cardiological Risk; Carotid Intima Media Thickness; Intravascular Ultrasound; Neurological risk; Shape Based Lesion Features

Indexed keywords

ACETYLSALICYLIC ACID; CLOPIDOGREL; GLYCERYL TRINITRATE;

ALGORITHM; ARTERIAL WALL THICKNESS; ARTICLE; CARDIOVASCULAR RISK; CAROTID ARTERY OBSTRUCTION; CORONARY ARTERY; CORONARY ARTERY CALCIUM SCORE; DIGITAL IMAGING AND COMMUNICATIONS IN MEDICINE; ECHOGRAPHY; GEOMETRY; HUMAN; IMAGE ANALYSIS; INTRACORONARY DRUG ADMINISTRATION; INTRAVASCULAR ULTRASOUND; MAJOR CLINICAL STUDY; PERCUTANEOUS CORONARY INTERVENTION; PREDICTION; RECEIVER OPERATING CHARACTERISTIC; RISK ASSESSMENT; SENSITIVITY ANALYSIS; STABLE ANGINA PECTORIS; STATISTICAL SIGNIFICANCE; ULTRASOUND TRANSDUCER;

EID: 84896971287     PISSN: 21567018     EISSN: 21567026     Source Type: Journal    
DOI: 10.1166/jmihi.2014.1251     Document Type: Article

References (33)

1. T. E. Craven, J. E. Ryu, M. A. Espeland, F. R. Kahl, W. M. McKinney, J. F. Toole, M. R. McMahan, C. J. Thompson, G. Heiss, and J. R. Crouse, 3rd, Evaluation of associations between carotid artery atherosclerosis and coronary artery stenosis, A case-control study. Circulation 82, 1230 (1990).
2. D. Satio, T. Shiraki, T. Oka, A. Kajiyama, M. Doi, and T. Masaka, Morphologic correlation between atherosclerotic lesions of the carotid and coronary arteries in patients with angina pectoris. Jpn. Circ. J. 63, 522 (1999).
3. F. Jashari, P. Ibrahimi, R. Nicoll, G. Bajraktari, P. Wester, and M. Y. Henein, Coronary and carotid atherosclerosis: Similarities and differences. Atherosclerosis 227, 193 (2013).
4. P. Schoenhagen, S. S. Halliburton, R. D. White, D. G. Vince, S. E. Nissen, and E. M. Tuzcu, Characterization of coronary atherosclerotic plaques and the significance of vessel calcification. Applied Radiology 30, 40 (2001).
5. M. Matsumoto, D. Yoshikawa, H. Ishii, S. Hayakawa, M. Tanaka, S. Kumagai, M. Hayashi, and T. Murohara, Morphologic characterization and quantification of superficial calcifications of the coronary artery-in vivo assessment using optical coherence tomography-nagoya. J. Med. Sci. 74, 253 (2012).
6. E. S. Filho, Y. Saijo, A. Tanaka, T. Yambe, S. Li, and M. Yoshizawa, Automated calcification detection and quantification in intravascular ultrasound images by adaptive thresholding. World Congress on Medical Physics and Biomedical Engineering 2006, IFMBE Proceedings, Seoul, Korea (2007), Vol. 14, p. 1421.
7. S. Ehara, Y. Kobayashi, T. Kataoka, M. Yoshiyama, M. Ueda, and J. Yoshikawa, Quantification of coronary calcification by intravascular ultrasound. Circulation Journal 71, 530 (2007).
8. W. Stanford, B. H. Thompson, and R. M. Weiss, Coronary artery calcification: Clinical significance and current methods of detection. AJR Am. J. Roentgenol. 161, 1139 (1993).
9. T. Araki, M. Nakamura, M. Utsunomiya, and K. Sugi, Visualization of coronary plaque in arterial remodelling using a new 40-MHz intravascular ultrasound imaging system. Catheterization and Cardiovascular Interventions 81, 471 (2013).
10. J. H. Stein, C. E. Korcarz, R. T. Hurst, E. Lonn, C. B. Kendall, E. R. Mohler, S. S. Najjar, C. M. Rembold, and W. S. Post, Force ASoECI-MTT, Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: A consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force, Endorsed by the Society for Vascular Medicine. J. Am. Soc. Echocardiogr. 21, 189 (2008).
11. F. Molinari, C. S. Pattichis, G. Zeng, L. Saba, U. R. Acharya, R. Sanfilippo, A. Nicolaides, and J. S. Suri, Completely automated multiresolution edge snapper-a new technique for an accurate carotid ultrasound IMT measurement: Clinical validation and benchmarking on a multi-institutional database. IEEE Trans. Image Process 21, 1211 (2012).
12. M. Demi, M. Paterni, and A. Benassi, The first absolute central moment in low-level image processing. Comput Vis Image Und. 80, 57 (2000).
13. F. Faita, V. Gemignani, E. Bianchini, C. Giannarelli, L. Ghiadoni, M. Demi, Real-time measurement system for evaluation of the carotid intima-media thickness with a robust edge operator. J. Ultrasound. Med. 27, 1353 (2008).
14. F. Molinari, K. M. Meiburger, L. Saba, U. R. Acharya, G. Ledda, G. Zeng, S. Y. Ho, A. T. Ahuja, S. C. Ho, A. Nicolaides, J. S. Suri, 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 Programs Biomed. 108, 946 (2012).
15. L. Saba, N. Ikeda, M. Deidda, T. Araki, F. Molinari, K. M. Meiburger, U. R. Acharya, Y. Nagashima, G. Mercuro, M. Nakano, A. Nicolaides, and J. S. Suri, Association of automated carotid IMT measurement and HbA1c in Japanese patients with coronary artery disease. Diabetes Research and Clinical Practice 100, 348 (2013).
16. M. Masseroli, R. M. Cothren, E. M. Tuzcu, D. S. Meier, J. D. Thomas, S. E. Nissen, and J. F. Cornhill, 950-93 Automatic identification of calcified regions on intravascular ultrasound images of human coronary arteries using texture analysis. Journal of the American College of Cardiology 25, 180A (1995).
17. M. Sonka, X. Zhang, M. Siebes, M. S. Bissing, S. C. DeJong, S. M. Collins, and C. R. McKay, Segmentation of intravascular ultrasound images: A knowledge-based approach. IEEE Trans. Med. Imag. 14, 719 (1995).
18. M. Sonka, V. Hlavac, and R. Boyle, Image Processing, Analysis, and Machine Vision, London, New York Chapman and Hall (1993).
19. Q. Zhang, Y. Wang, W. Wang, J. Ma, J. Qian, and J. Ge, Automatic segmentation of calcifications in intravascular ultrasound images using snakes and the contourlet transform. Ultrasound in Medicine and Biology 36, 111 (2010).
20. H. Lazrag, K. Aloui, and M. S. Naceur, Automatic segmentation of lumen in intravascular ultrasound images using fuzzy clustering and active contours. International Conference on control, Engineering and Information Technology Proceedings Engineering and Technology, Sousse, Tunisia (2013), Vol. 1, pp. 58-63.
21. A. Taki, Z. Najafi, A. Roodaki, S. K. Setarehdan, R. A. Zoroofi, A. Konig, and N. Navab, Automatic segmentation of calcified plaques and vessel borders in IVUS images. Int. J. CARS 3, 347 (2008).
22. A. Katouzian, E. Angelini, S. G. Carlier, and J. S. Suri, A state of the art review on segmentation algorithms in intravascular ultrasound (IVUS) images. Information Technology in Biomedicine, IEEE Transactions 16, 823 (2011).
23. S. Ehara, Y. Kobayashi, T. Kataoka, M. Yoshiyama, M. Ueda, and J. Yoshikawa, Quantification of coronary calcification by intravascular ultrasound-do calcium deposits with larger arcs have longer lengths? Circ. J. 71, 530 (2007).
24. Y. J. Hong, M. H. Jeong, Y. Ahn, D. S. Sim, J. W. Chung, J. S. Cho, N. S. Yoon, H. J. Yoon, J. Y. Moon, K. H. Kim, H. W. Park, J. H. Kim, J. G. Cho, J. C. Park, and J. C. Kang, Age-related differences in intravascular ultrasound findings in 1,009 coronary artery disease patients. Circ. J. 72, 1270 (2008).
25. H. Yamamoto, N. Ohashi, K. Ishibashi, H. Utsunomiya, E. Kunita, T. Oka, J. Horiguchi, and Y. Kihara, Coronary calcium score as a predictor for coronary artery disease and cardiac events in japanese high-risk patients. Circ J. 75, 2424 Epub (2011).
26. E. Mehanna, H. G. Bezerra, D. Prabhu, E. Brandt, Chamié D., H. Yamamoto, G. F. Attizzani, S. Tahara, Van N. Ditzhuijzen, Y. Fujino, T. Kanaya, G. Stefano, W. Wang, M. Gargesha, D. Wilson, and M. A. Costa, Volumetric characterization of human coronary calcification by frequency-domain optical coherence tomograph. Circ. J. 77, 2334 Epub (2013).
27. J. J. Carr, J. C. Nelson, N. D. Wong, M. McNitt-Gray, Y. Arad, D. R. Jacobs, Jr, S. Sidney, D. E. Bild, O. D. Williams, and R. C. Detrano, Calcified coronary artery plaque measurement with cardiac CT in population-based studies: Standardized protocol of multi-ethnic study of atherosclerosis (MESA) and coronary artery risk development in young adults (CARDIA) study. Radiology 234, 35 (2005).
28. J. Divers, L. E. Wagenknecht, and B. I. Freedman, Ethnic differences in the relationship between albuminuria and calcified atherosclerotic plaque. Diabetes Care 33, 131 (2010).
29. S. Sudha, G. R. Suresh, and R. Sukanesh, Speckle noise reduction in ultrasound images using context-based adaptive wavelet thresholding. IETE Journal of Research 55, 135 (2009).
30. J. S. Suri and S. Laxminarayan, Angiography, and Plaque Imaging: Advanced Segmentation Techniques, CRC Press LLC (2003).
31. König, M. Oepke, M. Leibig, and V. Klauss, Coronary plaque classification using intravascular ultrasound. Clin. Res. Car-Diol 96, 514 (2007).
32. A. H. Hernandez, D. G. Gil1, J. M. Mauri, and P. R. Radeva, Reducing cardiac motion in IVUS sequences. Computers in Cardiology 33, 685 (2006).
33. W. Casscells, M. Naghavi, and J. T. Willerson, Vulnerable atherosclerotic plaque a multifocal disease. Circulation 107, 2072 (2003).