Detection of spatially correlated objects in 3D images using appearance models and coupled active contours

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

Volumn 5762 LNCS, Issue PART 2, 2009, Pages 641-648

  Mosaliganti, Kishore ^a   Gelas, Arnaud ^a   Gouaillard, Alexandre ^a   Noche, Ramil ^a   Obholzer, Nikolaus ^a   Megason, Sean ^a  

^a HARVARD MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

3-D IMAGE; ACTIVE CONTOURS; APPEARANCE MODELS; CELL SHAPES; CORRELATION FUNCTION; ENERGY FUNCTIONALS; FLUORESCENCE MICROSCOPY IMAGES; IMAGE FORMATIONS; IMAGE INTENSITY DISTRIBUTION; LABELED CELLS; LEVEL SETS; MEMBRANE-BOUND; MULTI-SPECTRAL; PARAMETRIC DISTRIBUTIONS; SECOND PHASE;

FLUORESCENCE; FLUORESCENCE MICROSCOPY; MEDICAL COMPUTING;

THREE DIMENSIONAL;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; CELL NUCLEUS; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; FLUORESCENCE MICROSCOPY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING; ULTRASTRUCTURE;

ALGORITHMS; CELL NUCLEUS; COMPUTER SIMULATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MICROSCOPY, FLUORESCENCE; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84883847055     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04271-3_78     Document Type: Conference Paper

References (9)

1. Khairy, K., Reynaud, E., Stelzer, E.: Detection of deformable objects in 3D images using markov-chain monte carlo and spherical harmonics. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 1075-1082. Springer, Heidelberg (2008)
2. Chan, T., Vese, L.: An active contour model without edges. In: Scale-Space Theories in Comp. Vision, pp. 141-151 (1999)
3. Dufour, A., Shinin, V., Tajbakhsh, S., Guillon-Aghion, N., Olivo-Marin, J., Zimmer, C.: Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces. IEEE Trans. Image Process 14, 1396-1410 (2008) (Pubitemid 41430901)
4. Mosaliganti, K., Machiraju, R., Leone, G.: Tensor classification of N-point correlation function features for histology tissue segmentation. Medical Image Analysis 13(1) (2009)
5. Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Comm. on Pure and App. Math. 42(5), 577-685 (1989)
6. Vese, L., Chan, T.: A multiphase level set framework for image segmentation using the mumford and shah model. Intl. Journal of Comp. Vision 50, 271-293 (2002) (Pubitemid 35377416)
7. Li, C., Huang, R., Ding, Z., Gatenby, C., Metaxas, D., Gore, J.: A variational level set approach to segmentation and bias correction of images with intensity inhomogeneity. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 1083-1091. Springer, Heidelberg (2008)
8. Li, C., Xu, C., Gui, C., Fox, M.D.: Level set evolution without re-initialization: A new variational formulation. In: Comp. Vision and Pattern Recogn., pp. 430-436 (2005)
9. Redenbach, C.: Microstructure models for cellular materials. Computational Materials Science 44(4), 1397-1407 (2009)