Accurate segmentation of fluorescence images has become increasingly important for recognizing cell nucleus that have the phenotype of interest in biomedical applications. In this study an ensemble based method is proposed for the segmentation of cell cancer microscopy images. The ensemble is constructed and compared using Bayes graph-cut algorithm, binary graph-cut algorithm, spatial fuzzy C-means, and fuzzy level set algorithm, which were chosen for their accuracy and efficiency in the segmentation area. We investigate the performance of each method separately and finally compare the results with the ensemble method. Experiments are conducted over two datasets with different cell types. At 95% confidence level, the ensemble based method represents the best among all the implemented algorithms. Also ensemble method depicts better results in comparison with other state-of-the-art segmentation methods.
MohajeraniP. and NtziachristosV., An inversion scheme for hybrid fluorescence molecular tomography using a fuzzy inference system, IEEE Trans Med Imag35(2) (2016), 381–390.
2.
ZhuB., RasmussenJ., MaritoniL. and Sevick-MuracaE., Determining the performance of fluorescence molecular imaging devices using traceable working standards with SI units of radiance, IEEE Trans Med Imag35(3) (2016), 802–811.
3.
KovačevićJ. and RohdeK., Overview of image analysis tools and tasks for microscopy, in Microscopic Imag, 2007, pp. 1–24, Pittsburgh.
4.
BeheshtiM., ParkS., ChoiJ., GengX. and Podlaha-MurphyE., Reduction of Nanowire Agglomeration via an Intermediate Membrane in Nanowires Preparation for Nanosensors Application, in ASME 2015 International Mechanical Engineering Congress and Exposition, 2015, pp. V010T13A017–V010T13A017.
5.
BeheshtiM., FaichneyJ. and GharipourA., Bio-Cell Image Segmentation Using Bayes Graph-Cut Model, in Digital Image Computing: Techniques and Applications (DICTA), 2015 International Conference on, 2015, pp. 1–5.
6.
ZhangX., SuH., YangL. and ZhangS., Fine-grained histopathological image analysis via robust segmentation and large-scale retrieval, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5361–5368.
7.
PengB., ZhangL. and ZhangD., A survey of graph theoretical approaches to image segmentation, Pattern Recognition46 (2013), 1020–1038.
8.
ZhouH., ZhengJ. and WeiL., Texture aware image segmentation using graph cuts and active contours, Pattern Recognition46 (2013), 1719–1733.
9.
SalahM.B., MiticheA. and AyedI.B., Multiregion image segmentation by parametric kernel graph cuts, IEEE Trans Image Process20 (2011), 545–557.
10.
CorsoJ.J., SharonE., DubeS., El-SadenS., SinhaU. and YuilleA., Efficient multilevel brain tumor segmentation with integrated bayesian model classification, IEEE Trans Med Imag27 (2008), 629–640.
11.
LiB.N., ChuiC.K., ChangS. and OngS.H., Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation, Computers in Biology and Medicine41 (2011), 1–10.
12.
YuZ., XuM. and GaoZ., Biomedical image segmentation via constrained graph cuts and pre-segmentation, in Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, 2011, pp. 5714–5717.
13.
PécotT., BouthemyP., BoulangerJ., ChesselA., BardinS., SalameroJ., et al. Background fluorescence estimation and vesicle segmentation in live cell imaging with conditional random fields, IEEE Trans Med Imag24 (2014), 667–680.
14.
ErsoyI., BunyakF., ChaginV., CardosoM.C. and PalaniappanK., Segmentation and classification of cell cycle phases in fluorescence imaging, in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2009, ed: Springer, 2009, pp. 617–624.
15.
GharipourA. and LiewA.W.-C., A Multi-Kernel Local Level Set Image Segmentation Algorithm for Fluorescence Microscopy Images, in Digital Image Computing: Techniques and Applications (DICTA), 2015 International Conference on, 2015, pp. 1–5.
16.
MalladiR., SethianJ.A. and VemuriB.C., Shape modeling with front propagation: A level set approach, IEEE Trans Pattern Anal17 (1995), 158–175.
17.
CasellesV., CattéF., CollT. and DibosF., A geometric model for active contours in image processing, Numerische Mathematik66 (1993), 1–31.
18.
GharipourA. and LiewA.W.-C., Fuzzy clustering using local and global region information for cell image segmentation, in Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on, 2014, pp. 216–222.
19.
ChanT.F. and VeseL.A., Active contours without edges, IEEE Trans Image Process10 (2001), 266–277.
20.
DelongA. and BoykovY., Globally optimal segmentation of multi-region objects, in Computer Vision, 2009 IEEE 12th International Conference on, 2009, pp. 285–292.
21.
DelongA. and BoykovY., A scalable graph-cut algorithm for ND grids, in Computer Vision and Pattern Recognition, 2008 CVPR 2008 IEEE Conference on, 2008, pp. 1–8.
22.
DelongA., OsokinA., IsackH.N. and BoykovY., Fast approximate energy minimization with label costs, International Journal of Computer Vision96 (2012), 1–27.
23.
HunderiA.H. and KarunakaranN., Segmentation of Medical Image Data using Level Set Methods, Norwegian Uni. NTNU, 2013.
24.
WhitakerR.T., A level-set approach to 3D reconstruction from range data, International Journal of Computer Vision29 (1998), 203–231.
25.
BoykovY.Y. and JollyM.-P., Interactive graph cuts for optimal boundary & region segmentation of objects in ND images, in Computer Vision, 2001 ICCV 2001 Proceedings. Eighth IEEE International Conference on, 2001, pp. 105–112.
26.
BoykovY., VekslerO. and ZabihR., Fast approximate energy minimization via graph cuts, IEEE Trans. Pattern Anal23 (2001), 1222–1239.
27.
AntalB., RemenyikB. and HajduA., An unsupervised ensemble-based Markov Random Field approach to microscope cell image segmentation, in Signal Processing and Multimedia Applications (SIGMAP), 2013 International Conference on, 2013, pp. 94–99.
28.
MohapatraS., PatraD. and SatpathyS., An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images, Neural Computing and Applications24 (2014), 1887–1904.
29.
LittlestoneN. and WarmuthM.K., The weighted majority algorithm, Information and Computation108 (1994), 212–261.
30.
KittlerJ. and RoliF., Multiple classifier systems, Lecture notes in computer science, 2002.
31.
MuhlbaierM.D., TopalisA. and PolikarR., Learn NC: Combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes, Neural Networks, IEEE Transactions on20 (2009), 152–168.
32.
KunchevaL.I. and RodríguezJ.J., A weighted voting framework for classifiers ensembles, Knowledge and Information Systems38 (2014), 259–275.
33.
LinG., AdigaU., OlsonK., GuzowskiJ.F., BarnesC.A. and RoysamB., A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks, Cytometry Part A56 (2003), 23–36.
34.
WatersJ.C., Accuracy and precision in quantitative fluorescence microscopy, The Journal of Cell Biology185 (2009), 1135–1148.
35.
BezdekJ.C., Pattern recognition with fuzzy objective function algorithms, Kluwer Academic Publishers, 1981.
36.
ChuangK.-S., TzengH.-L., ChenS., WuJ. and ChenT.-J., Fuzzy c-means clustering with spatial information for image segmentation, Computerized Medical Imaging and Graphics30 (2006), 9–15.
37.
ChenT.F., Medical image segmentation using level sets, Technical Report. Canada, University of Waterloo, 2008.
38.
PengD., MerrimanB., OsherS., ZhaoH. and KangM., A PDE-based fast local level set method, Journal of Computational Physics155 (1999), 410–438.
39.
RastgarpourM. and ShanbehzadehJ., A new kernel-based fuzzy level set method for automated Segmentation of medical images in the presence of intensity inhomogeneity, Computational and Mathematical Methods in Medicine2014, 2014.
40.
FrénayB. and VerleysenM., Classification in the presence of label noise: A survey, IEEE Trans Neural Net25 (2014), 845–869.
41.
MoffatJ., GruenebergD.A., YangX., KimS.Y., KloepferA.M., HinkleG., et al. A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen, Cell124 (2006), 1283–1298.
42.
LjosaV., SokolnickiK.L. and CarpenterA.E., Annotated high-throughput microscopy image sets for validation, Nat Methods9 (2012), 637.
43.
LehmussolaA., RuusuvuoriP., SelinummiJ., HuttunenH. and Yli-HarjaO., Computational framework for simulating fluorescence microscope images with cell populations, IEEE Trans Med Imag26 (2007), 1010–1016.
44.
LehmussolaA., RuusuvuoriP., SelinummiJ., RajalaT. and Yli-HarjaO., Synthetic images of high-throughput microscopy for validation of image analysis methods, Proceedings of the IEEE96 (2008), 1348–1360.
45.
CongaltonR.G. and GreenK., Assessing the accuracy of remotely sensed data: Principles and practices: CRC Press, 2008.
46.
VincentL. and SoilleP., Watersheds in digital spaces: An efficient algorithm based on immersion simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence13 (1991), 583–598.
47.
OtsuN., A threshold selection method from gray-level histograms, Automatica11 (1975), 23–27.
48.
RoussonM. and DericheR., A variational framework for active and adaptative segmentation of vector valued images, in Motion and Video Computing, 2002 Proceedings Workshop on, 2002, pp. 56–61.
49.
LiC., KaoC.-Y., GoreJ.C. and DingZ., Minimization of region-scalable fitting energy for image segmentation, IEEE Transactions on Image Processing17 (2008), 1940–1949.
50.
LiC., XuC., GuiC. and FoxM.D., Distance regularized level set evolution and its application to image segmentation, IEEE Transactions on Image Processing19 (2010), 3243–3254.
51.
ChenY.-T., A level set method based on the Bayesian risk for medical image segmentation, Pattern Recognition43 (2010), 3699–3711.
52.
GharipourA. and LiewA.W.-C., Segmentation of cell nuclei in fluorescence microscopy images: An integrated framework using level set segmentation and touching-cell splitting, Pattern Recognition58 (2016), 1–11.
53.
WangX.-F., HuangD.-S. and XuH., An efficient local Chan– Vese model for image segmentation, Pattern Recognition43 (2010), 603–618.
54.
GoldsteinT., BressonX. and OsherS., Geometric applications of the split Bregman method: Segmentation and surface reconstruction, Journal of Scientific Computing45 (2010), 272–293.
