Literature Cited

  • 1
    Dow A, Shafer S, Kirkwood J, Mascari R, Waggoner A. Automatic multiparameter fluorescence imaging for determining lymphocyte phenotype and activation status in melanoma tissue sections. Cytometry 1996; 25: 7181.
  • 2
    Malpica N, Ortiz de Solorzano C, Vaquero J, Santos A, Vallcorba I, Garcia-Sagredo J, del Pozo F. Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry 1997; 28: 289297.
  • 3
    Lin G, Adiga U, Olson K, Guzowski J, Barnes C, Roysam B. A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks. Cytometry A 2003; 56A: 2336.
  • 4
    Bengtsson E, Wahlby C, Lindblad J. Robust cell image segmentation methods. Pattern Recogn Image Anal 2004; 14: 157167.
  • 5
    Chen X, Zhou X, Wong S. Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Trans Biomed Eng 2006; 53: 762766.
  • 6
    Yang X, Li H, Zhou X. Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy. IEEE Trans Circuits Syst I 2006; 53: 24052414.
  • 7
    Adiga U, Malladi R, Fernandez-Gonzalez R, de Solorzano C. High-throughput analysis of multispectral images of breast cancer tissue. IEEE Trans Image Process 2006; 15: 22592268.
  • 8
    Jung C, Kim C. Segmenting clustered nuclei using h-minima transform-based marker extraction and contour parameterization. IEEE Trans Biomed Eng 2010; 57: 26002604.
  • 9
    Nielsen B, Albregtsen F, Danielsen H. Automatic segmentation of cell nuclei in Feulgen-stained histological sections of prostate cancer and quantitative evaluation of segmentation results. Cytometry A 2012; 81A: 588601.
  • 10
    Lockett S, Sudar D, Thompson C, Pinkel D, Gray J. Efficient, interactive, and three-dimensional segmentation of cell nuclei in thick tissue sections. Cytometry 1998; 31: 275286.
  • 11
    Ortiz de Solorzano C, Malladi R, Lelievre S, Lockett S. Segmentation of nuclei and cells using membrane related protein markers. J Microsc 2001; 201: 404415.
  • 12
    Dufour A, Shinin V, Tajbakhsh S, Guillén-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 2005; 14: 13961410.
  • 13
    Lin G, Chawla M, Olson K, Barnes C, Guzowski J, Bjornsson C, Shain W, Roysam B. A multi-model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3d confocal microscope images. Cytometry A 2007; 71A: 724736.
  • 14
    Hodneland E, Bukoreshtliev N, Eichler T, Tai X, Gurke S, Lundervold A, Gerdes H. A unified framework for automated 3-d segmentation of surface-stained living cells and a comprehensive segmentation evaluation. IEEE Trans Med Imaging 2009; 28: 720738.
  • 15
    Ridler T, Calvard S. Picture thresholding using an iterative selection method. IEEE Trans Syst Man Cybern 1978; 8: 630632.
  • 16
    Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979; 9: 6266.
  • 17
    Li G, Liu T, Tarokh A, Nie J, Guo L, Mara A, Holley S, Wong S. 3D cell nuclei segmentation based on gradient flow tracking. BMC Cell Biol 2007; 8: 4040.
  • 18
    Gonzalez R, Woods R. Digital Image Processing, 2nd ed. Upper Saddle River, NJ: Prentice Hall; 2002.
  • 19
    Wählby C, Sintorn I, Erlandsson F, Borgefors G, Bengtsson E. Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. J Microsc 2004; 215: 6776.
  • 20
    Li F, Zhou X, Ma J, Wong S. Multiple nuclei tracking using integer programming for quantitative cancer cell cycle analysis. IEEE Trans Med Imaging 2010; 29: 96105.
  • 21
    Plissiti M, Nikou C, Charchanti A. Combining shape, texture and intensity features for cell nuclei extraction in pap smear images. Pattern Recogn Lett 2011; 32: 838853.
  • 22
    Nandy K, Gudla P, Amundsen R, Meaburn K, Misteli T, Lockett S. Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images. Cytometry A 2012; 81A: 743754.
  • 23
    Leymarie F, Levine M. Tracking deformable objects in the plane using an active contour model. IEEE Trans Pattern Anal Mach Intell 1993; 15: 617634.
  • 24
    Bamford P, Lovell B. Unsupervised cell nucleus segmentation with active contours. Signal Process 1998; 71: 203213.
  • 25
    Garrido A, Pérez de la Blanca N. Applying deformable templates for cell image segmentation. Pattern Recogn 2000; 33: 821832.
  • 26
    Yang L, Meer P, Foran D. Unsupervised segmentation based on robust estimation and color active contour models. IEEE Trans Inf Technol Biomed 2005; 9: 475486.
  • 27
    Cheng J, Rajapakse J. Segmentation of clustered nuclei with shape markers and marking function. IEEE Trans Biomed Eng 2009; 56: 741748.
  • 28
    Mukherjee D, Ray N, Acton S. Level set analysis for leukocyte detection and tracking. IEEE Trans Image Process 2004; 13: 562572.
  • 29
    Chan T, Vese L. Active contours without edges. IEEE Trans Image Process 2001; 10: 266277.
  • 30
    Yan P, Zhou X, Shah M, Wong S. Automatic segmentation of high-throughput RNAi fluorescent cellular images. IEEE Trans Inf Technol Biomed 2008; 12: 109117.
  • 31
    Xu C, Prince J. Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 1998; 7: 359369.
  • 32
    Plissiti M, Nikou C. Overlapping cell nuclei segmentation using a spatially adaptive active physical model. IEEE Trans Image Process 2012; 21: 45684580.
  • 33
    Dzyubachyk O, van Cappellen W, Essers J, Niessen W, Meijering E. Advanced level-set-based cell tracking in time-lapse fluorescence microscopy. IEEE Trans Med Imaging 2010; 29: 852867.
  • 34
    Dufour A, Thibeaux R, Labruyere E, Guillen N, Olivo J. 3D active meshes: Fast discrete deformable models for cell tracking in 3D time-lapse microscopy. IEEE Trans Image Process 2011; 20: 19251937.
  • 35
    Quelhas P, Marcuzzo M, Mendonca AM, Campilho A. Cell nuclei and cytoplasm joint segmentation using the sliding band filter. IEEE Trans Med Imaging 2010; 29: 14631473.
  • 36
    Esteves T, Quelhas P, Mendonça A, Campilho A. Gradient convergence filters and a phase congruency approach for in vivo cell nuclei detection. Mach Vision Appl 2012; 23: 623638.
  • 37
    Gudla P, Nandy K, Collins J, Meaburn K, Misteli T, Lockett S. A high-throughput system for segmenting nuclei using multiscale techniques. Cytometry A 2008; 73A: 451466.
  • 38
    McCullough D, Gudla P, Harris B, Collins J, Meaburn K, Nakaya M, Yamaguchi T, Misteli T, Lockett S. Segmentation of whole cells and cell nuclei from 3-D optical microscope images using dynamic programming. IEEE Trans Med Imaging 2008; 27: 723734.
  • 39
    Luck B, Carlson K, Bovik A, Richards-Kortum R. An image model and segmentation algorithm for reflectance confocal images of in vivo cervical tissue. IEEE Trans Image Process 2005; 14: 12651276.
  • 40
    Chen C, Li H, Zhou X, Wong S. Constraint factor graph cut-based active contour method for automated cellular image segmentation in RNAi screening. J Microsc 2008; 230: 177191.
  • 41
    Ta V, Lézoray O, Elmoataz A, Schüpp S. Graph-based tools for microscopic cellular image segmentation. Pattern Recogn 2009; 42: 11131125.
  • 42
    Al-Kofahi Y, Lassoued W, Lee W, Roysam B. Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans Biomed Eng 2010; 57: 841852.
  • 43
    Lee K, Street W. Model-based detection, segmentation, and classification for image analysis using on-line shape learning. Mach Vision Appl 2003; 13: 222233.
  • 44
    Lee K, Street W. An adaptive resource-allocating network for automated detection, segmentation, and classification of breast cancer nuclei topic area: image processing and recognition. IEEE Trans Neural Netw 2003; 14: 680687.
  • 45
    Fehr J, Ronneberger O, Kurz H, Burkhardt H. Self-learning segmentation and classification of cell-nuclei in 3D volumetric data using voxel-wise gray scale invariants. Pattern Recogn 2005; 3663: 377384.
  • 46
    Mao K, Zhao P, Tan P. Supervised learning-based cell image segmentation for p53 immunohistochemistry. IEEE Trans Biomed Eng 2006; 53: 11531163.
  • 47
    Jung C, Kim C, Chae S, Oh S. Unsupervised segmentation of overlapped nuclei using Bayesian classification. IEEE Trans Biomed Eng 2010; 57: 28252832.
  • 48
    Penny WD, Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE, editors. Statistical Parametric Mapping: The Analysis of Functional Brain Images. Waltham, Massachusetts: Academic Press; 2006.
  • 49
    MATLAB, version 7.12.0 (R2011a). Natick, MA: The MathWorks, Inc.; 2011.
  • 50
    Rohde GK, Ribeiro AJS, Dahl KN, Murphy RF. Deformation-based nuclear morphometry: Capturing nuclear shape variation in hela cells. Cytometry A 2008; 73A: 341350.
  • 51
    Heitz G, Rohlfing T, Maurer CJr. Statistical shape model generation using nonrigid deformation of a template mesh. In: SPIE International Proceedings on Medical Imaging 2005;5747:1411–1421.
  • 52
    Shlens J. A Tutorial on Principal Component Analysis. San Diego: Systems Neurobiology Laboratory, University of California at San Diego; 2005.
  • 53
    Li C, Xu C, Gui C, Fox M. Level set evolution without re-initialization: A new variational formulation. In: IEEE International Proceedings of Computer Vision and Pattern Recognition 2005;1:430–436. Washington, DC: IEEE Computer Society.
  • 54
    Lehmussola A, Ruusuvuori P, Selinummi J, Huttunen H, Yli-Harja O. Computational framework for simulating fluorescence microscope images with cell populations. IEEE Trans Med Imaging 2007; 26: 10101016.
  • 55
    Lehmussola A, Ruusuvuori P, Selinummi J, Rajala T, Yli-Harja O. Synthetic images of high-throughput microscopy for validation of image analysis methods. In: Proceedings of IEEE 2008;96:1348–1360. Washington, DC: IEEE Computer Society.
  • 56
    Coelho L, Shariff A, Murphy R. Nuclear segmentation in microscope cell images: A hand-segmented dataset and comparison of algorithms. In: IEEE International Symposium on Biomedical Imaging 2009;1:518–521. Washington, DC: IEEE Computer Society.
  • 57
    Wang W, Ozolek JA, Rohde GK. Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images. Cytometry A 2010; 77A: 485494.
  • 58
    Dice L. Measures of the amount of ecologic association between species. Ecology 1945; 26: 297302.
  • 59
    Ravichandran K, Ananthi B. Color skin segmentation using k-means cluster. Int J Comput Appl Math 2009; 4: 153157.
  • 60
    Carpenter A, Jones T, Lamprecht M, Clarke C, Kang I, Friman O, Guertin D, Chang J, Lindquist R, Moffat J, et al. Cellprofiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biol 2006; 7: R100.
  • 61
    Pratt W. Digital Image Processing. Hoboken, New Jersey: Wiley; 1991.
  • 62
    Raimondo F, Gavrielides M, Karayannopoulou G, Lyroudia K, Pitas I, Kostopoulos I. Automated evaluation of her-2/neu status in breast tissue from fluorescent in situ hybridization images. IEEE Trans Image Process 2005; 14: 12881299.
  • 63
    Srinivasa G, Fickus M, Guo Y, Linstedt A, Kovacevic J. Active mask segmentation of fluorescence microscope images. IEEE Trans Image Process 2009; 18: 18171829.
  • 64
    Deng J, Hu J, Wu J. A study of color space transformation method using nonuniform segmentation of color space source. J Comput 2011; 6: 288296.