[1]Noma, A., Graciano, A., Consularo, L., Cesar, M. R., & Bloch, I. (2008). A new algorithm for interactive structural image segmentation. arXiv:0805.1854v2 [cs.CV].

[2]Rocha, L.M., Cappabianco, F.A.M., & Falcao, A.X. (2009). Data clustering as an optimum-path forest problem with applications in image analysis. International Journal of Imaging Systems and Technology, 19(2), 50-68.

[3]Mortensen, E. (1998). Interactive segmentation with intelligent scissors. Graphical Models and Image Processing, 60 (5), 349-384. 

[4]Falcao, A. X., Udupa, J. K., & Miyazawa, F. K. (2000). An ultra-fast user-steered image segmentation paradigm: live wire on the fly. IEEE Transaction on Medical Imaging, 19 (1), 55-62. 

[5]De Miranda, P. A. V., Falcão, A. X., & Udupa, J. K. (2010). Synergistic arc-weight estimation for interactive image segmentation using graphs. Computer Vision and Image Understanding, 114 (1), 85-99.

[6]Bai, X., & Sapiro G. (2007). Distance cut: interactive segmentation and matting of images and videos. IEEE International Conference on Image Processing, San Antonio, Texas, 2, pp. 249-252.

[7]Protiere, A., & Sapiro, G. (2007). Interactive image segmentation via adaptive weighted distances. IEEE Transactions on Image Processing, 16(4), 1046-1057.

[8]Boykov, Y. Y., & Jolly M. P. (2001). Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. Eighth IEEE International Conference on Computer Vision, 1, pp. 105-112.

[9]Talairach, J., & Tournoux P. (1988). Co-planar stereotaxic atlas of the human brain. New York: Thieme Medical Inc. 

[10]Cootes, T., Taylor, C., Cooper, D., & Graham, J. (1995). Active shape models-their training and application. Computer Vision and Image Understanding, 61(1), 38-59.

[11]Grau, V., Mewes, A.U.J., Alcaniz, M., Kikinis, R., & Warfield, S.K. (2004). Improved watershed transform for medical image segmentation using prior information. IEEE Transactions on Medical Imaging, 23(4), 447-458.

[12]Liu, J., & Udupa, J.K. 2009. Oriented active shape models. IEEE Transactions on Medical Imaging, 28(4), 571-584. 

[13]Miranda, P.A.V., Falcao, A.X., & Udupa, J.K. (2009). Cloud bank: a multiple clouds model and its use in MR brain image segmentation. IEEE International Symposium on Biomedical Imaging, Boston, MA, pp. 506-509.

[14]Miranda, P.A.V., & Falcao, A.X. (2009). Links between image segmentation based on optimum-path forest and minimum cut in graph. Journal of Mathematical Imaging and Vision, 35(2), 128-142.

[15]Falcao, A. X., Udupa, J. K., Samarasekera, S., Sharma, S., Hirsch, B. E., & Lotufo R. de. A. (1998). User-steered image segmentation paradigms: live wire and live lane. Graphical Models and Image Processing, 60 (4), 233-260.

[16]Marfil, R., Molina-Tanco, L., Bandera, A., Rodriguez, J. A., & Sandoval F. (2006). Pyramid segmentation algorithms revisited. Pattern Recognition, 39 (8), 1430-1451.

[17]Eriksson, A. P., Olsson, C., & Kahl, F. (2007). Normalized cuts revisited: a reformulation for segmentation with linear grouping constraints. International Conference on Computer Vision, pp. 1-8.

[18]Grady, L. (2006). Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (11), 1768-1783.

[19]Goldberg, A. V., & Tarjan R. E. (1988). A new approach to the maximum-flow problem. Journal of the ACM, 35 (4), 921–940.

[20]Dinic, E. A. (1980). Algorithm for solution of a problem of maximum flow in networks with power estimation. Soviet Mathematics- Doklady, 11, 1277-1280.

[21]Boykov, Y., & Kolmogorov, V. (2004). An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26 (9), 1124-1137.

[22]Zeng, Y., Samaras, D., Chen W., & Peng, Q. (2008). Topology cuts: a novel min-cut/max-flow algorithm for topology preserving segmentation in n–d images. Computer Vision and Image Understanding, 112 (1), pp. 81-90.

[23]Falcao, A. X., Stolfi, J., & Alencar, De. (2004). The image foresting transform: theory, algorithms, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26 (1), pp. 19-29.

[24]Felzenszwalb, P., & Huttenlocher, D. (2004). Efficient graph-based image segmentation. International Journal of Computer Vision, 59 (2), 167-181.

[25]Felzenszwalb, P.F., & Huttenlocher, D.P. (1998). Image segmentation using local variation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 98-104.

[26]Fahad, A., & Morris, T. (2006). A faster graph-based segmentation algorithm with statistical region merge. Lecture Notes in Computer Science, pp. 286-293.

[27]Zhang, M., & Alhajj, R. (2006). Improving the graph-based image segmentation method. Eighteenth IEEE International Conference on Tools with Artificial Intelligence, pp. 617 - 624.

[28]Saha, P.K., & Udupa, J.K. (2001). Relative fuzzy connectedness among multiple objects: theory, algorithms, and applications in image segmentation. Computer Vision and Image Understanding, 82 (1), 42-56.

[29]Ciesielski, K.C., Udupa, J.K., Saha, P.K., & Zhuge Y. (2007). Iterative relative fuzzy connectedness for multiple objects with multiple seeds. Computer Vision and Image Understanding, 107(3), 160-182.

[30]Vincent, L., & Soille, P. (1991). Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transaction on Pattern Analysis and Machine Intelligence, 13(6), 583-598.

[31]Ciesielski, K.C., Udupa, J.K., Falcao, A.X., & Miranda, P.A.V. (2011). Fuzzy connectedness and graph cut image segmentation - similarities and differences. Proceedings of SPIE on Medical Imaging, To appear.

[32]Audigier, R., & Lotufo, R.A. (2007). Watershed by image foresting transform, tie-zone, and theoretical relationship with other watershed definitions. Eighth International Symposium on Mathematical Morphology and its Applications to Signal and Image Processing, Rio de Janeiro, Brazil, pp. 277-288.

[33]Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (8), 888-905. 

[34]Greig, D.M., Porteous, B.T., & Seheult A.H. (1989). Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society Series B, 51, 271-279.

[35]Wu, Z., & Leahy, R. (2002). An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15 (11), 1101-1113.

[36]Shi, J., & Malik, J. (1997). Normalized cuts and image segmentation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 731-737.

[37]Sharon, E., Galun, M., Sharon, D., Basri, R., & Brandt, A. (2006). Hierarchy and adaptivity in segmenting visual scenes. Nature, 442 (7104), 810-813.

[38]Hochbaum, D. S. (2010). Polynomial time algorithms for ratio regions and a variant of normalized cut. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (5), 889-898.

[39]Yu, S. X., & Shi, J. (2004). Segmentation given partial grouping constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26 (2), 173-183.

[40]Xu, L., Li, W., & Schuurmans, D. (2009). Fast normalized cut with linear constraints. IEEE Conference on Computer Vision and Pattern Recognition, pp. 2866-2873.

[41]Sarkar, S., & Soundararajan, P. (2000). Supervised learning of large perceptual organization: graph 

spectral partitioning and learning automata. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (5), 504-525.

[42]Ding, C. H. Q., He, X., Zha, H., Gu, M., & Simon, H. D. (2001). A min-max cut algorithm for graph partitioning and data clustering. International Conference on Data Mining, pp. 107-114.

[43]Li, X., & Tian, Z. (2007). Optimum cut-based clustering. Signal Processing, 87(11), 2491-2502.

[44]Wang, S., & Siskind, J. M. (2001). Image segmentation with minimum mean cut. Eighth IEEE International Conference on Computer Vision, 1, pp. 517-524.

[45]Wang, S., & Siskind, J. M. (2003). Image segmentation with ratio cut. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (6), 675-690.

[46]Jermyn, I., & Ishikawa, H. (2001). Globally optimal regions and boundaries as minimum ratio weight cycles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (10), 1075-1088.

[47]Boykov, Y., & Kolmogorov, V. (2003). Computing geodesics and minimal surfaces via graph cuts. Ninth IEEE International Conference on Computer Vision, 1, pp. 26-33.

[48]Grady, L., & Schwartz, E. L. (2006). Isoperimetric graph partitioning for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (3), 469-475.

[49]Lempitsky, V., Blake, A., & Rother, C. (2008) . Image segmentation by branch-and-mincut. European Conference on Computer Vision, pp. 15-29.

[50]Cousty, J., Bertrand, G., Najman, L., and Couprie, M. (2009). Watershed cuts: minimum spanning forests and the drop of water principle. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 (8), 1362-1374.

[51]Cox, I. J., Rao, S. B., & Zhong, Y. (1996). Ratio regions: a technique for image segmentation. International Conference on Pattern Recognition, 2, pp. 557-564.

[52]Chen, C., Li, H., & Zhou, X. (2007). Automated segmentation of drosophila RNAi fluorescence cellular images using graph cuts. Lecture Notes in Computer Science, pp. 116-125.

[53]Yang, H., & Choe, Y. (2009). Cell tracking and segmentation in electron microscopy images using graph cuts, Sixth IEEE International Conference on Symposium on Biomedical Imaging, pp. 306-309.

[54]Zhilan, H., Guijin, W., Xinggang, L., & Hong, Y. (2009). Skin segmentation based on graph cuts. Tsinghua Science and Technology, 14 (4), 478-486.

[55]Hu, Z., Yan, H., & Lin, X. (2008). Clothing segmentation using foreground and background estimation based on the constrained delaunay triangulation. Pattern Recognition, 41 (5), 1581-1592.

[56]Han, D., Singh, V., Lee, J. E., Zakszewski, E., Adluru, N., Oakes, T. R., & Alexander, A. (2009). An experimental evaluation of diffusion tensor image segmentation using graph-cuts. Conference of the IEEE Engineering in Medicine and Biology Society, 5653-5656.

[57]Camilus, K. S., Govindan, V. K., & Sathidevi, P. S. (2010). Computer aided identification of the pectoral muscle in digitized mammograms. Journal of Digital Imaging, 23 (5), 562-580.

[58]Mohar, B. (1989). Isoperimetric numbers of graphs. Journal of Combinatorial Theory Series B, 47 (3), 274-291.

[59]Kolmogorov, V., & Zabin, R. (2004). What energy functions can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence, 26 (2), 147-159.

[60]Ishikawa, H., & Geiger, D. (1998). Segmentation by grouping junctions. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 125-131.

[61]Braquelaire, J. P., & Brun, L. (1998). Image segmentation with topological maps and inter-pixel representation. Journal of Visual Communication and Image Representation, 9(1), 62-79.

[62]Boykov, Y., & Funka-Lea, G. (2006). Graph cuts and efficient n-d image segmentation. International Journal of Computer Vision, 70 (2), 109-131.

[63]Freedman, D., & Zhang, T. (2005). Interactive graph cut based segmentation with shape priors, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, pp.755-762.

[64]Slabaugh, G., & Unal, G. (2005). Graph cuts segmentation using an elliptical shape prior, IEEE International Conference on Image Processing, 2, pp. 1222-1225.

[65]Zhang, J., Wang, Y., & Shi, X. (2009). An improved graph cut segmentation method for cervical lymph nodes on sonograms and its relationship with node's shape assessment. Computerized Medical Imaging and Graphics, 33, 602-607.

[66]Zhu-Jacquot, J., & Zabih, R. (2007). Graph cuts segmentation with statistical shape priors for medical images, Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, pp.631-635.

[67]Funka-Lea, G., Boykov, Y., Florin, C., Jolly, M. P., Moreau-gobard, R., Ramaraj, R., & Rinck, D. (2006). Automatic heart isolation for CT coronary visualization using graph-cuts. IEEE International Symposium on Biomedical Imaging, pp. 614-617. 

[68]Das, P., & Veksler, O. (2006). Semiautomatic segmentation with compact shape prior. 3rd Canadian Conference on Computer and Robot Vision, pp. 28-36.

[69]Vicente, S., Kolmogorov, V., & Rother, C. (2008). Graph cut based image segmentation with connectivity priors. IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8. 

[70]Veksler, O. (2008). Star shape prior for graph-cut image segmentation, 10th European Conference on Computer Vision, pp. 454-467.

[71]Liu, C., Li, F., Zhang, Y., & Gu, H. (2009). Interactive image segmentation based on hierarchical graph-cut optimization with generic shape prior. Lecture Notes in Computer Science, pp. 201-210.

[72]Kumar, M. P., Torr, P. H. S., & Zisserman, A. (2005). OBJ CUT. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, pp. 18-25.

[73]Lombaert, H., Sun, Y., Grady, L., and Xu, C. (2005). A multilevel banded graph cuts method for fast image segmentation, Tenth IEEE International Conference on Computer Vision, 1, pp. 259-265.

[74]Kohli, P., & Torr, P. H. S. (2007). Dynamic graph cuts for efficient inference in Markov random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (12), 2079-2088.

[75]Juan, O., & Boykov,Y. (2007). Capacity scaling for graph cuts in vision. IEEE 11th International Conference on Computer Vision, pp.1-8.

[76]Delong, A., & Boykov, Y. (2008). A scalable graph-cut algorithm for N-D grids. IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8.

[77]Schmidt, F. R., Toppe, E., & Cremers, D. (2009). Efficient planar graph cuts with applications in computer vision. IEEE Conference on Computer Vision and Pattern Recognition, pp. 351-356.

[78]Weldeselassie, Y. T., & Hamarneh, G. (2007). DT-MRI segmentation using graph cuts. Proceedings of SPIE, pp. 1-9.

[79]Malcolm, J., Rathi, Y., and Tannenbaum, A. 2007. A graph cut approach to image segmentation in tensor space, Proceedings of Workshop Component Analysis Methods, pp. 18-25.

[80]Rother, C., Kolmogorov, V., & Blake, A. (2004). Grabcut: interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics, 23, 309-314.

[81]Xu, N., Bansal, R., & Ahuja, N. (2003). Object segmentation using graph cuts based active contours. Computer Vision and Pattern Recognition Conference, 2, 46-53.

[82]Xu, N., Ahuja, N., & Bansal, R. (2007). Object segmentation using graph cuts based active contours. Computer Vision and Image Understanding, 107 (3), 210-224.

[83]Yang, Q., Tang, X., Wang, C., Ye, Z., & Chen, M. (2007). Progressive cut: an image cutout algorithm that models user intentions. IEEE Multimedia, 14 (3), 56-66.

[84]Wang, C., Yang, Q., Chen, M., Tang, X., & Ye, Z. (2006). Progressive cut, Proceedings of the 14th Annual ACM international Conference on Multimedia, pp. 251-260.

[85]Zahn, C. T. (1971). Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Transactions on Computers, C-20 (1), 68-86.

[86]Xu, Y., Olman, V., & Uberbacher, E. C. (1996). A segmentation algorithm for noisy images. IEEE International Joint Symposia on Intelligence and Systems, pp. 220-226.

[87]Xu, Y., & Uberbacher, E. C. (1997). 2D image segmentation using minimum spanning trees. Image and Vision Computing, 15 (1), pp. 47-57.

[88]Wertheimer, M. (1938). Laws of organization in perceptual forms- A source book of Gestalt psychology. London: Routledge & Kegan Paul.

[89]Chen, P.C., & Pavlidis, T. (1980). Image segmentation as an estimation problem, Computer Graphics and Image Processing, 12 (2), 153-172.

[90]Ping, Y., Runsheng, W., & Diannong, L. (1996). A new image segmentation approach based on linked pyramid. Proceedings of International Conference on Signal Processing, pp. 1118–1121.

[91]Burt, P., Hong, T., & Rosenfeld, A. (1981). Segmentation and estimation of image region properties through cooperative hierarchical computation. IEEE Transaction on Systems, Man and Cybernetics, 11 (12), 802–809.

[92]Ziliani, F., & Jensen, B. (1998). Unsupervised image segmentation using the modified pyramidal linking approach. International Conference on Image Processing, 3, pp. 303-307.

[93]Bister, M., Cornelis, J., Rosenfeld, A. (1990). A critical view of pyramid segmentation algorithms. Pattern Recognition Letters, 11(9), 605- 617.

[94]Montanvert, A., Meer, P., & Rosenfeld, A. (1991). Hierarchical image analysis using irregular tessellations, IEEE Transaction on Pattern Analysis Machine Intelligence, 13(4), 307-316.

[95]Jolion, J. M., & Montanvert, A. (1992). The adaptive pyramid, a framework for 2D image analysis, Computer Vision, Graphics, and Image Processing, 55(3), 339-348.

[96]Huart, J., & Bertolino, P. (2005). Similarity-based and perception-based image segmentation. IEEE International Conference on Image Processing, pp. 1148-1151.

[97]Marfil, R., Rodriguez, J. A., Bandera, A., & Sandoval, F. (2004). Bounded irregular pyramid: a new structure for colour image segmentation. Pattern Recognition, 37 (3), 623-626.

[98]Brun, L., & Kropatsch, W. G. (2003). Construction of combinatorial pyramids. Lecture Notes in Computer Science, pp. 1-12.

[99]Montanari, U. (1971). On the optimal detection of curves in noisy pictures, Communications of ACM, 14(5), pp. 335-345.

[100]Martelli, A. (1972). Edge detection using heuristic search methods. Computer Graphics and Image Processing, 1(2), pp. 169-182.

[101]Martelli, A. (1976). An application of heuristic search methods to edge and contour detection, Communications of the ACM, 19(2), 73-83.

[102]Pope, D. L., Parker, D. L., Clayton, P. D., & Gustafson, D. E. (1985). Left ventricular border recognition using a dynamic search algorithm. Radiology, 155 (2), 513-518.

[103]Sonka, M., Winniford, M. D., & Collins, S. M. (1995). Robust simultaneous detection of coronary borders in complex images. IEEE Transactions on Medical Imaging, 14 (1), pp. 151-161.

[104]Udupa, J. K., Saha, P. K., & Lotufo, R. A. (2002). Relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. IEEE Transactions on Pattern Analysis Machine Intelligence, 24 (11), 1485-1500.

[105]Couprie, C., Grady, L., Najman, L., & Talbot, H. (2009). Power watersheds: a new image segmentation framework extending graph cuts, random walker and optimal spanning forest. International Conference on Computer Vision, pp. 731-738.

[106]Gomez, D., Montero, J., Yáñez, J., & Poidomani, C. (2007). A graph coloring approach for image segmentation. Omega, 35 (2), 173-183.

[107]Corso, J. J., Yuille, A., Sicotte, N. L., & Toga, A. (2007). Detection and segmentation of pathological structures by the extended graph-shifts algorithm, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 985-993.

[108]Yuan, X., Situ, N., & Zouridakis, G. A. (2009). Narrow band graph partitioning method for skin lesion segmentation. Pattern Recognition, 42 (6), 1017-1028. 

[109]Ta, V., Lézoray, O., Elmoataz, A., & Schüpp, S. (2009). Graph-based tools for microscopic cellular image segmentation. Pattern Recognition, 42 (6), 1113-1125.

[110]Zhang, Y. (1996) . A survey on evaluation methods for image segmentation. Pattern Recognition, 29 (8), 1335-1346.

[111]Unnikrishnan, R., Pantofaru, C., & Hebert, M. (2007) .Toward objective evaluation of image segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29 (6), 929-944. 

[112]Zhang, H., Fritts, J., & Goldman, S. (2008). Image segmentation evaluation: A survey of unsupervised methods, Computer Vision and Image Understanding, 110 (2), 260-280.

[113]Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. International Conference on Computer Vision, 2, pp. 416-423.

[114]Sanfeliu, A., Alquezar, R., Andrade, J., Climent, J., Serratosa, F. & Verges, J. (2002). Graph-based representations and techniques for image processing and image analysis. Pattern Recognition, 35 (3), 639-650.