Image manifold revealing for breast lesion segmentation in DCE-MRI

Bio-Medical Materials and Engineering

Volumn 26, Issue , 2015, Pages S1353-S1360

  Hu, Liang ^a,b   Cheng, Zhaoning ^a,b   Wang, Manning ^a,b   Song, Zhijian ^a,b  

^a FUDAN UNIVERSITY   (China)

^b Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention   (China)

Author keywords

Breast lesion; DCE MRI; dimensionality reduction; manifold learning; tumor segmentation

Indexed keywords

IMAGE SEGMENTATION; MEDICAL IMAGING;

BREAST LESION; DCE-MRI; DIMENSIONALITY REDUCTION; MANIFOLD LEARNING; TUMOR SEGMENTATION;

MAGNETIC RESONANCE IMAGING;

GADOBENATE DIMEGLUMINE;

ARTICLE; BREAST LESION; CLINICAL ARTICLE; DIAGNOSTIC ACCURACY; DYNAMIC CONTRAST-ENHANCED MAGNETIC RESONANCE IMAGING; GOLD STANDARD; HUMAN; IMAGE MANIFOLD; IMAGE PROCESSING; IMAGE SEGMENTATION; NUCLEAR MAGNETIC RESONANCE SCANNER; PRIORITY JOURNAL; RADIOLOGIST; SENSITIVITY AND SPECIFICITY; ALGORITHM; AUTOMATED PATTERN RECOGNITION; BREAST TUMOR; COMPUTER ASSISTED DIAGNOSIS; FEMALE; IMAGE ENHANCEMENT; MACHINE LEARNING; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROCEDURES; REPRODUCIBILITY;

ALGORITHMS; BREAST NEOPLASMS; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84977448483     PISSN: 09592989     EISSN: 18783619     Source Type: Journal    
DOI: 10.3233/BME-151433     Document Type: Article

References (16)

1. M.D. Schnall, Breast MR imaging, Radiologic Clinics of North America 41 (2003), 43-50.
2. E.A. Morris, Breast cancer imaging with MRI, Radiologic Clinics of North America 40 (2002), 443-466.
3. D.J. Withey and Z.J. Koles, A review of medical image segmentation: Methods and available software, International Journal of Bioelectromagnetism 10 (2008), 125-148.
4. K. Doi, Overview on research and development of computer-aided diagnostic schemes, Semin Ultrasound CT MR 25 (2004), 404-410.
5. R.H. El Khouli, M.A. Jacobs and D.A. Bluemke, Magnetic resonance imaging of the breast, Seminars in Roentgenology 43 (2008), 265-281.
6. S.Y. Wan and W.E. Higgins, Symmetric region growing, IEEE Trans Image Process 12 (2003), 1007-1015.
7. J. Lai and Q. Wei. Automatic lung fields segmentation in CT scans using morphological operation and anatomical information, Bio-Medical Materials and Engineering 24 (2014), 335-340.
8. R. Chellappa and A. Jain, Markov Random Fields Theory and Application, Academic Press, 1993.
9. S. M'hiri, L. Cammoun and F. Ghorbel, Speeding up HMRF-EM algorithms for fast unsupervised image segmentation by bootstrap resampling: Application to the brain tissue segmentation, Signal Processing 87 (2007), 2544-2559.
10. W. Chen, M.L. Giger and U. Bick, A fuzzy C-Means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images, Academic Radiology 13 (2006), 63-72.
11. X. Han, C. Xu and J.L. Prince, A topology preserving level set method for geometric deformable models, IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (2003), 755-768.
12. A. Akhbardeh and M.A. Jacobs, Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation, Medical Physics 39 (2012), 2275-2289.
13. M. Belkin and P. Niyogi, Laplacian eigen maps for dimensionality reduction and data representation, Neural Computation 15 (2003), 1373-1396.
14. L. Hu, M.N. Wang and Z.J. Song, Manifold-based feature point matching for multi-modal image registration, International Journal of Medical Robotics and Computer Assisted Surgery 9 (2013), e10-e18.
15. O. Kubassova, M. Boesen, R.D. Boyle, et al., Fast and robust analysis of dynamic contrast enhanced MRI datasets, Medical Image Computing and Computer-Assisted Intervention 10 (2007), 261-269.
16. C. Lavini, M.S. Buiter and M. Maas, Use of dynamic contrast enhanced time intensity curve shape analysis in MRI: Theory and practice, Reports in Medical Imaging 6 (2013), 71-82.