Atlas-based probabilistic fibroglandular tissue segmentation in breast MRI

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7511 LNCS, Issue , 2012, Pages 437-445

  Wu, Shandong ^a   Weinstein, Susan ^b   Kontos, Despina ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b HOSPITAL OF THE UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MAGNETIC RESONANCE IMAGING; MEDICAL COMPUTING; MEDICAL IMAGING; RISK PERCEPTION; TISSUE; TISSUE ENGINEERING;

AUTOMATED SEGMENTATION METHOD; BREAST CANCER RISK ESTIMATIONS; FIBROGLANDULAR TISSUE; KERNEL DENSITY ESTIMATION; MULTIVARIATE MODELING; POSTERIOR PROBABILITY; PROBABILISTIC MODELING; SIMILARITY COEFFICIENTS;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; AUDIOVISUAL EQUIPMENT; AUTOMATED PATTERN RECOGNITION; BREAST TUMOR; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; FEMALE; FIBROSIS; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PRECANCER; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

ALGORITHMS; BREAST NEOPLASMS; COMPUTER SIMULATION; FEMALE; FIBROSIS; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MODELS, ANATOMIC; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; PRECANCEROUS CONDITIONS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84872972908     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33418-4_54     Document Type: Conference Paper

References (19)

1. Khazen, M., Warren, R.M., et al.: A pilot study of compositional analysis of the breast and estimation of breast mammographic density using three-dimensional T1-weighted magnetic resonance imaging. Cancer Epidemiol. Biomarkers Prev. 17, 2268–2274 (2008)
2. Klifa, C., Carballido-Gamio, J., Wilmes, L., Laprie, A., Shepherd, J., Gibbs, J., Fan, B., Noworolski, S., Hylton, N.: Magnetic resonance imaging for secondary assessment of breast density in a high-risk cohort. Magn. Reson. Imag. 28(1), 8–15 (2010)
3. King, V., Brooks, J.D., Bernstein, J.L., Reiner, A.S., Pike, M.C., Morris, E.A.: Background parenchymal enhancement at breast MR imaging and breast cancer risk. Radiology 260(1), 50–60 (2011)
4. Mérida, A.G., Kallenberg, M., Martí, R., Karssemeijer, N.: Fully automatic fibroglandular tissue segmentation in breast MRI: atlas-based approach. In: MICCAI Workshop (2011)
5. Lin, M., Chan, S., Chen, J.H., Chang, D., Nie, K., Chen, S.T., Lin, C.J., Shih, T.C., Nalcioglu, O., Su, M.Y.: A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI. Medical Physics 38(1) (2011)
6. Nie, K., Chen, J.H., Chan, S., Chau, M.K., Yu, H.J., Bahri, S., Tseng, T., Nalcioglu, O., Su, M.Y.: Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI. Med. Phys. 35, 5253–5262 (2008)
7. Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE TMI 17, 87–97 (1998)
8. Li, C., Xu, C., Anderson, A.W., Gore, J.C.: MRI Tissue Classification and Bias Field Estimation Based on Coherent Local Intensity Clustering: A Unified Energy Minimization Framework. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 288–299. Springer, Heidelberg (2009)
9. Wu, S.D., Weinstein, S.P., Conant, E.F., Localio, A.R., Schnall, M.D., Kontos, D.: Fully automated chest wall line segmentation in breast MRI by using context information. In: SPIE Medical Imaging: CAD (2012)
10. Chintalapani, G., Ellingsen, L.M., Sadowsky, O., Prince, J.L., Taylor, R.H.: Statistical Atlases of Bone Anatomy: Construction, Iterative Improvement and Validation. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 499–506. Springer, Heidelberg (2007)
11. Bergen, J.R., Anandan, P., Hanna, T.J., Hingorani, R.: Hierarchical Model-based Motion Estimation. In: Sandini, G. (ed.) ECCV 1992. LNCS, vol. 588, pp. 237–252. Springer, Heidelberg (1992)
12. Wu, S.D., Li, Y.F.: Flexible signature descriptions for adaptive motion trajectory representation, perception and recognition. Pattern Recognition 42, 194–214 (2009)
13. Watson, D.F.: Contouring: A guide to the analysis and display of spacial data. Perg. (1994)
14. Botev, Z.I., Grotowski, J.F., Kroese, D.P.: Kernel density estimation via diffusion. Annals of Statistics 38(5), 2916–2957 (2010)
15. Mori, G.: Guiding model search using segmentation. In: IEEE ICCV (2005)
16. Yang, A.Y., Wright, J., Ma, Y., Sastry, S.S.: Unsupervised segmentation of natural images via lossy data compression, Com. Vis. and Imag. Und. 110, 212–225 (2008)
17. Byng, J.W., Boyd, N.F., Fishell, E., Jong, R.A., Yaffe, M.J.: Automated analysis of mammographic densities. Physics in Medicine and Biology 41, 909–923 (1996)
18. Weinstein, S.P., Localio, A.R., Conant, et al.: Multimodality screening of high-risk women: a prospective cohort study. J. Clin. Onco. 27(36), 6124–6128 (2009)
19. Xing, Y., Xue, Z., Englander, S., Schnall, M.D., Shen, D.: Improving Parenchyma Segmentation by Simultaneous Estimation of Tissue Property T1 Map and Group-Wise Registration of Inversion Recovery MR Breast Images. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 342–350. Springer, Heidelberg (2008)