Computer-aided analysis of ultrasound elasticity images for classification of benign and malignant breast masses

American Journal of Roentgenology

Volumn 195, Issue 6, 2010, Pages 1460-1465

  Moon, Woo Kyung ^a   Choi, Ji Won ^a   Cho, Nariya ^a   Park, Sang Hee ^a   Chang, Jung Min ^a   Jang, Mijung ^a   Kim, Kwang Gi ^b  

^a Seoul National University Hospital   (South Korea)

^b National Cancer Center   (South Korea)

Author keywords

Breast tumor; Breast ultrasound; Computer aided diagnosis; Sonoelastography

Indexed keywords

ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; BREAST CANCER; BREAST TUMOR; CANCER CLASSIFICATION; COMPUTER AIDED DESIGN; COMPUTER ANALYSIS; CONTROLLED STUDY; DIAGNOSTIC PROCEDURE; DIAGNOSTIC VALUE; ELASTOGRAPHY; FEMALE; HISTOGRAM; HUMAN; MAJOR CLINICAL STUDY; NEEDLE BIOPSY; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; ULTRASOUND;

ADULT; AGED; BREAST NEOPLASMS; ELASTICITY IMAGING TECHNIQUES; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MIDDLE AGED; PREDICTIVE VALUE OF TESTS; ROC CURVE; SENSITIVITY AND SPECIFICITY; ULTRASONOGRAPHY, MAMMARY;

EID: 78649737510     PISSN: 0361803X     EISSN: 15463141     Source Type: Journal    
DOI: 10.2214/AJR.09.3140     Document Type: Article

References (26)

1. Krouskop TA, Wheeler TM, Kallel F, Garra BS, Hall T. Elastic moduli of breast and prostate tissues under compression. Ultrason Imaging 1998; 20:260-274
2. Ophir J, Cespedes I, Ponnekanti H, Yazdi Y, Li X. Elastography: a quantitative method for imaging the elasticity of biological tissues. Ultrason Imaging 1991; 13:111-134
3. Bae U, Dighe M, Dubinsky T, Minoshima S, Shamdasani V, Kim Y. Ultrasound thyroid elastography using carotid artery pulsation: preliminary study. J Ultrasound Med 2007; 26:797-805
4. Garra BS, Cespedes EI, Ophir J, et al. Elastography of breast lesions: initial clinical results. Radiology 1997; 202:79-86
5. Lyshchik A, Higashi T, Asato R, et al. Cervical lymph node metastases: diagnosis at sonoelastography - initial experience. Radiology 2007; 243:258-267
6. Lyshchik A, Higashi T, Asato R, et al. Thyroid gland tumor diagnosis at US elastography. Radiology 2005; 237:202-211
7. Millonig G, Reimann FM, Friedrich S, et al. Extrahepatic cholestasis increases liver stiffness (FibroScan) irrespective of fibrosis. Hepatology 2008; 48:1718-1723
8. Pallwein L, Mitterberger M, Struve P, et al. Comparison of sonoelastography guided biopsy with systematic biopsy: impact on prostate cancer detection. Eur Radiol 2007; 17:2278-2285
9. Cho N, Moon WK, Park JS, Cha JH, Jang M, Seong MH. Nonpalpable breast masses: evaluation by US elastography. Korean J Radiol 2008; 9:111-118
10. Itoh A, Ueno E, Tohno E, et al. Breast disease: clinical application of US elastography for diagnosis. Radiology 2006; 239:341-350
11. Scaperrotta G, Ferranti C, Costa C, et al. Role of sonoelastography in non-palpable breast lesions. Eur Radiol 2008; 18:2381-2389
12. Sohn YM, Kim MJ, Kim EK, Kwak JY, Moon HJ, Kim SJ. Sonographic elastography combined with conventional sonography: how much is it helpful for diagnostic performance? J Ultrasound Med 2009; 28:413-420
13. Thomas A, Kummel S, Fritzsche F, et al. Real-time sonoelastography performed in addition to B-mode ultrasound and mammography: improved differentiation of breast lesions? Acad Radiol 2006; 13:1496-1504
14. Burnside ES, Hall TJ, Sommer AM, et al. Differentiating benign from malignant solid breast masses with US strain imaging. Radiology 2007; 245:401-410
15. Regner DM, Hesley GK, Hangiandreou NJ, et al. Breast lesions: evaluation with US strain imaging - clinical experience of multiple observers. Radiology 2006; 238:425-437
16. Chang RF, Huang SF, Moon WK, Lee YH, Chen DR. Solid breast masses: neural network analysis of vascular features at three-dimensional power Doppler US for benign or malignant classification. Radiology 2007; 243:56-62
17. Joo S, Moon WK, Kim HC. Computer-aided diagnosis of solid breast nodules on ultrasound with digital image processing and artificial neural network. Conf Proc IEEE Eng Med Biol Soc 2004; 2:1397-1400
18. Saftoiu A, Vilmann P, Gorunescu F, et al. Neural network analysis of dynamic sequences of EUS elastography used for the differential diagnosis of chronic pancreatitis and pancreatic cancer. Gastrointest Endosc 2008; 68:1086-1094
19. American College of Radiology. Breast Imaging Reporting and Data System (BI-RADS):Ultrasound. Reston, VA: American College of Radiology, 2003
20. Pitas I. Digital image processing algorithms and applications. New York, NY: Wiley, 2000:419
21. Mardia K. Measures of multivariate skewness and kurtosis with applications. Biometrika 1970; 57:519
22. Galloway M. Texture analysis using grey level run lengths. Comput Graph Image Proc 1974; 4:172-179
23. Friedman N, Geiger D, Goldszmidt M, et al. Bayesian network classifiers. Mach Learn 1997; 29:131-163
24. Freeman JA, Skapura DM. Neural networks: algorithms, applications, and programming techniques. Reading, MA: Addison-Wesley, 1991:xiii, 401
25. Cho N, Moon WK, Kim HY, Chang JM, Park SH, Lyou CY. Sonoelastographic strain index for differentiation of benign and malignant nonpalpable breast masses. J Ultrasound Med 2010; 29:1-7
26. Horsch K, Giger ML, Venta LA, Vyborny CJ. Automatic segmentation of breast lesions on ultrasound. Med Phys 2001; 28:1652-1659