Segmentation of Ordinary Images and Medical Images with an Adaptive Hidden Markov Model and Viterbi Algorithm

Author(s): Yinglei Song*, Benjamin Adobah, Junfeng Qu, Chunmei Liu

Journal Name: Current Signal Transduction Therapy

Volume 15 , Issue 2 , 2020


Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Abstract:

Background: Image segmentation is an important problem in both image processing and computer vision. Given an image, the goal of image segmentation is to label each pixel in the image such that the pixels with the same label collectively represent an object.

Materials and Methods: Due to the inherent complexity and noise that may exist in images, developing an algorithm that can generate excellent segmentation results for an arbitrary image is still a challenging problem. In this paper, a new adaptive Hidden Markov Model is developed to describe the spatial and semantic relationships among pixels in an image. Based on this statistical model, image segmentation can be efficiently performed with an adaptive Viterbi algorithm in linear time.

Results: The algorithm is unsupervised and does not require being used along with any other approach in image segmentation. Testing results on synthetic and real images show that this algorithm is able to achieve excellent segmentation results in both ordinary images and medical images.

Conclusion: An implementation of this algorithm in MATLAB is freely available upon request.

Keywords: Image segmentation, adaptive Hidden Markov Models, adaptive Viterbi Algorithm, stochastic process.

[1]
Chen S, Zhang D. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans Syst Man Cybern B Cybern 2004; 34(4): 1907-16.
[http://dx.doi.org/10.1109/TSMCB.2004.831165] [PMID: 15462455]
[2]
Arslan S, Ersahin T, Cetin-Atalay R, Gunduz-Demir C. Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy images. IEEE Trans Med Imaging 2013; 32(6): 1121-31.
[http://dx.doi.org/10.1109/TMI.2013.2255309] [PMID: 23549886]
[3]
Fu M, Wu W, Hong X, et al. Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images. BMC Syst Biol 2018; 12(Suppl. 4): 56.
[http://dx.doi.org/10.1186/s12918-018-0572-z] [PMID: 29745840]
[4]
Shen P, Li C. Local feature extraction and information bottleneck-based segmentation of brain magnetic resonance (mr) images. Entropy (Basel) 2013; 15: 3205-18.
[http://dx.doi.org/10.3390/e15083295]
[5]
Caponetti L, Castellano G, Corsini V. MR brain image segmentation: a framework to compare different clustering techniques. Information 2017; 8: 138.
[http://dx.doi.org/10.3390/info8040138]
[6]
Wei H, Chen L, Guo LKL. Divergence-based fuzzy cluster ensemble for image segmentation. Entropy (Basel) 2018; 20: 273.
[http://dx.doi.org/10.3390/e20040273]
[7]
Tavallali P, Yazdi M, Khosravi MR. Robust cascaded skin detector based on Adaboost. Multimedia Tools Appl 2018; 78: 2599-620.
[http://dx.doi.org/10.1007/s11042-018-6385-7]
[8]
Akbarzadeh O, Khosravi MR, Khosravi B, Halvaee P. Medical image magnification based on original and estimated pixel selection models. J Biomed Phy & Eng 2020; 10(3): 357-366.
[http://dx.doi.org/10.31661/jbpe.v0i0.797]
[9]
Bogoslavskyi I, Stachniss C. Efficient online segmentation for sparse 3D laser scans. J. Photogramm. Re-mote Sens. Geoinforma Sci 2017; 85(1): 41-52.
[http://dx.doi.org/10.1007/s41064-016-0003-y]
[10]
Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986; 8(6): 679-98.
[http://dx.doi.org/10.1109/TPAMI.1986.4767851] [PMID: 21869365]
[11]
Adams R, Bischof L. Seeded region growing. IEEE Trans Pattern Anal Mach Intell 1994; 16(6): 641-7.
[http://dx.doi.org/10.1109/34.295913]
[12]
Otsu N. A threshold selection method from graylevel-histograms. IEEE Trans Syst Man Cybern 1979; 9(1): 62-6.
[http://dx.doi.org/10.1109/TSMC.1979.4310076]
[13]
Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. Int J Comput Vis 1988; 1(4): 321-31.
[http://dx.doi.org/10.1007/BF00133570]
[14]
Malladi R, Sethian JA, Vemuri BC. Shape modeling with front propagation: A level set approach. IEEE Trans Pattern Anal Mach Intell 1995; 17(2): 158-75.
[http://dx.doi.org/10.1109/34.368173]
[15]
Mumford D, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 1989; 42(5): 577-685.
[http://dx.doi.org/10.1002/cpa.3160420503]
[16]
Comaniciu D, Meer P. Mean shift: A robust approach to-ward feature space analysis. IEEE Trans Pattern Anal Mach Intell 2002; 24(5): 603-19.
[http://dx.doi.org/10.1109/34.1000236]
[17]
Shi J, Malik J. Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 2000; 22(8): 888-905.
[http://dx.doi.org/10.1109/34.868688]
[18]
Chen L, Chen CL, Lu M. A multiple-kernel fuzzy C-means algorithm for image segmentation. IEEE Trans Syst Man Cybern B Cybern 2011; 41(5): 1263-74.
[http://dx.doi.org/10.1109/TSMCB.2011.2124455] [PMID: 21803693]
[19]
Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal 2017; 35: 18-31.
[http://dx.doi.org/10.1016/j.media.2016.05.004] [PMID: 27310171]
[20]
Pereira S, Pinto A, Alves V, Silva CA. Brain tumor seg-mentation using convolutional neural networks in mri images. IEEE Trans Med Imaging 2016; 35(5): 1240-51.
[http://dx.doi.org/10.1109/TMI.2016.2538465] [PMID: 26960222]
[21]
Xue Y, Xu T, Zhang H, Long R, Huang X. SegAN: Adversarial Network with Multi-scale L1 loss for medical image segmentation. Available from: arXiv preprint arXiv: 170601805 1706
[22]
Song Y, Liu C, Wang Z. A machine learning approach for accurate annotation of noncoding RNAs. IEEE/ACM Trans Comput Biol Bioinformatics 2015; 12(3): 551-9.
[http://dx.doi.org/10.1109/TCBB.2014.2366758] [PMID: 26357266]
[23]
Song Y, Liu C, Huang X, Malmberg RL, Xu Y, Cai L. Efficient parameterized algorithms for biopolymer structure-sequence alignment. IEEE/ACM Trans Comput Biol Bioinformatics 2006; 3(4): 423-32.
[http://dx.doi.org/10.1109/TCBB.2006.52] [PMID: 17085850]
[24]
Proakis GJ, Salehi M. Digital Communications. Fifth Edition.. McGraw-Hill Higher Education 2008.
[25]
Henning R, Chakrabarti C. Low-power approach for decoding convolutional codes with adaptive Viterbi algorithm approximations. Proceedings of the 2002 International Symposium on Low Power Electronics and Design 68-71.
[26]
Harvey BA. Adaptive Viterbi algorithm with ARQ for bounded complexity decoding. IEEE Trans Wirel Commun 2005; 3(6): 1948-52.
[http://dx.doi.org/10.1109/TWC.2004.837451]
[27]
Martin D, Fowlkes C, Tal D, Malik J. A database of hu-man segmented natural images and its applications to evaluating segmentation algorithms and measuring ecological statistics. Proceedings of the 8th International Conference on Computer Vision 2001; 416-23.
[28]
Felzenszwalb P, Huttenlocher D. Efficient graph-based im-age segmentation. Int J Comput Vis 2004; 59(2): 167-81.
[http://dx.doi.org/10.1023/B:VISI.0000022288.19776.77]
[29]
Collins DL, Zijdenbos AP, Kollokian V, et al. Design and construction of a realistic digital brain phantom. IEEE Trans Med Imaging 1998; 17(3): 463-8.
[http://dx.doi.org/10.1109/42.712135] [PMID: 9735909]


open access plus

Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 15
ISSUE: 2
Year: 2020
Published on: 01 December, 2020
Page: [109 - 123]
Pages: 15
DOI: 10.2174/1574362413666181109113834

Article Metrics

PDF: 23
HTML: 3
EPUB: 1