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Multimedia Systems

29.06.2019 | Special Issue Paper

Oriented grouping-constrained spectral clustering for medical imaging segmentation

verfasst von: Kaijian Xia, Xiaoqing Gu, Yudong Zhang

Erschienen in: Multimedia Systems

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Abstract

Original medical images are often inadequate for clinical diagnosis. Certain prior information can be used as an important basis for disease diagnosis and prevention. In this study, an oriented grouping-constrained spectral clustering method, OGCSC, is proposed to deal with medical image segmentation problems. OGCSC propagates the group information from the affinity matrix and subdivides the group information into two constraints. By adopting the normalized framework, OGCSC can be transformed into normalized spectral clustering. The solution of OGSCSC can be viewed as a generalized eigenvalue problem that can be solved using eigenvalue decomposition techniques. The significance of our work is that the use of group information and constraints information to analyse image data can greatly enhance the results achieved using the clustering segmentation method. The empirical experimental results reveal that the proposed method achieves robust and effective performance for medical image segmentation.

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Literatur
1.
Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32(7), 1153 (2013)CrossRef
2.
Du, J., Li, W., Xiao, B.: Anatomical-functional image fusion by information of interest in local Laplacian filtering domain. IEEE Trans. Image Process. 26(12), 5855–5866 (2017)MathSciNetCrossRef
3.
Bagci, U., Udupa, J.K., Yao, J., Mollura, D.J.: Co-segmentation of functional and anatomical images. In: Medical Image Computing & Computer-Assisted Intervention: Miccai International Conference on Medical Image Computing & Computer-Assisted Intervention. Lecture Notes in Computer Science, vol 7512. Springer, Berlin, Heidelberg, pp. 459–467 (2012)
4.
Healy, S., Mcmahon, J., Owens, P., Dockery, P., Fitzgerald, U.: Threshold-based segmentation of fluorescent and chromogenic images of microglia, astrocytes and oligodendrocytes in fiji. J. Neurosci. Methods 295, 87–103 (2017)CrossRef
5.
Han, W., Yang, Z., Lu, J.: Supervised threshold-based heart sound classification algorithm. Physiol. Meas. 39(11), 115011–115021 (2018)CrossRef
6.
Khan, Faizal: Segmentation of lung images using region based neural networks. Biomed. Pharmacol. J. 11, 2037–2042 (2018)CrossRef
7.
Pratondo, A., Chui, C.K., Ong, S.H.: Integrating machine learning with region-based active contour models in medical image segmentation. J. Vis. Commun. Image Represent. 43, 1–9 (2016)CrossRef
8.
Yogamangalam, R., Karthikeyan, B.: Segmentation techniques comparison in image processing. Int. J. Eng. Technol. (IJET) 5(1), 307–313 (2013)
9.
Pratondo, A., Chui, C.K., Ong, S.H.: Robust edge-stop functions for edge-based active contour models in medical image segmentation. IEEE Signal Process. Lett. 23(2), 222–226 (2016)CrossRef
10.
Zou, J., Chen, L., Chen, C.P.: Ensemble fuzzy c-means clustering algorithms based on KL-divergence for medical image segmentation. In: IEEE International Conference on Bioinformatics & Biomedicine, Shanghai, China, pp.18–21 (2013)
11.
Ajala, F.A., Oke, O.A., Adedeji, T.O., Alade, O.M., Adewusi, E.A.: Fuzzy k-c-means clustering algorithm for medical image segmentation. J. Inf. Eng. Appl. 2(6), 21–32 (2012)
12.
Wang, Y., Wu, L., Lin, X., Gao, J.: Multiview spectral clustering via structured low-rank matrix factorization. IEEE Trans. Neural Netw. Learn. Syst. PP(99), 1–11 (2018)
13.
Zhu, X., Zhang, S., Hu, R., He, W., Lei, C., Zhu, P.: One-step multi-view spectral clustering. IEEE Trans. Knowl. Data Eng. 12(99), 1 (2018)CrossRef
14.
15.
Archip, N., Rohling, R., Cooperberg, P., Tahmasebpour, H., Warfield, S. K.: Spectral clustering algorithms for ultrasound image segmentation. In: Proceedings of the 8th International Conference on Medical Image Computing And Computer-Assisted Intervention, vol. Part II. Springer, Berlin (2005)
16.
Sourati, J., Brooks, D.H., Dy, J.G., Erdogmus, D.: Constrained spectral clustering for image segmentation. In: IEEE International Workshop on Machine Learning for Signal Processing. IEEE, Santander, Spain, pp 23–26 (2012)
17.
Wu, J., Mahfouz, M.R.: Robust X-ray image segmentation by spectral clustering and active shape model. J. Med. Imaging 3(3), 034005 (2016)CrossRef
18.
Mouysset, S., Zbib, H., Stute, S., Girault, J.M., Charara, J., Noailles, J., et al.: Segmentation of dynamic pet images with kinetic spectral clustering. Phys. Med. Biol. 58(19), 6931–6944 (2013)CrossRef
19.
Gao, Y., Gu, S.W., Tang, J.: Research on spectral clustering in machine learning. Comput. Sci. 34(2), 201–203 (2007)
20.
Chen, W., Feng, G.: Spectral clustering: a semi-supervised approach. Neurocomputing 77(1), 229–242 (2012)CrossRef
21.
Ding, S., Qi, B., Jia, H., Hong, Z., Zhang, L.: Research of semi-supervised spectral clustering based on constraints expansion. Neural Comput. Appl. 22(1), 405–410 (2013)CrossRef
22.
Klein, D., Kamvar, S.D., Manning, C.D.: From instance-level constraints to space-level constraints: making the most of prior knowledge in data clustering. In: Nineteenth International Conference on Machine Learning. Morgan Kaufmann Publishers Inc, Burlington (2002)
23.
Chrysouli, C., Tefas, A.: Spectral clustering and semi-supervised learning using evolving similarity graphs. Appl. Soft Comput. 34(C), 625–637 (2015)CrossRef
24.
Qian, P., Jiang, Y., Wang, S., Su, K.H., Wang, J., Hu, L., et al.: Affinity and penalty jointly constrained spectral clustering with all-compatibility, flexibility, and robustness. IEEE Trans. Neural Netw. Learn. Syst. 28, 1123–1138 (2016)CrossRef
25.
Wang, L., Bo, L., Li, C.: Density-sensitive semi-supervised spectral clustering. J. Softw. 18(10), 2412 (2007)CrossRef
26.
Ding, S., Jia, H., Zhang, L., Jin, F.: Research of semi-supervised spectral clustering algorithm based on pairwise constraints. Neural Comput. Appl. 24(1), 211–219 (2014)CrossRef
27.
Wang, S.-H.: RGB-D image-based detection of stairs, pedestrian crosswalks and traffic signs. J. Vis. Commun. Image Represent. 25(2), 263–272 (2014)CrossRef
28.
Lua, S.: A pathological brain detection system based on kernel based ELM. Multimedia Tools Appl. 77(3), 3715–3728 (2018)CrossRef
29.
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRef
30.
31.
Lu Z.D., Carreira, M.A.: Constrained spectral clustering through affinity propagation. In: IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK, USA, pp 23–26 (2008).
32.
Qian, P., Chung, F.L., Wang, S., Deng, Z.: Fast graph-based relaxed clustering for large data sets using minimal enclosing ball. IEEE Trans. Syst. Man Cybern. B Cybern. 42(3), 672–687 (2012)CrossRef
33.
Hooijmans, M.T., Dzyubachyk, O., Nehrke, K.: Fast multistation water/fat imaging at 3T using DREAM-based RF shimming. J. Magn. Reson. Imaging 42(1), 217–223 (2015)CrossRef
34.
Zaidi, H., Ojha, N., Morich, M.: Design and performance evaluation of a whole-body Ingenuity TF PET-MRI system. Phys. Med. Biol. 56(10), 3091–3106 (2011)CrossRef
35.
Kalemis, A., Delattre, B.M., Heinzer, S.: Sequential whole-body PET/MR scanner: concept, clinical use, and optimisation after two years in the clinic. The manufacturer’s perspective. Magn. Reson. Mater. Phys. Biol. Med. 26(1), 5–23 (2013)CrossRef
36.
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Dordrecht (1981)CrossRef
37.
Kamvar, S.D., Klein, D., Manning, C.D.: Spectral learning. In: Proceedings of International Joint Conference on Artificial Intelligence, Acapulco, Mexico, pp 561–566(2003)
38.
Wang, X., Davidson, I.: Flexible constrained spectral clustering. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, pp. 563–572 (2010)
39.
Jiang, Y., Zhao, K., Xia, K., Xue, J., Zhou, L., Ding, Y., Qian, P.: A novel distributed multitask fuzzy clustering algorithm for automatic mr brain image segmentation. J. Med. Syst. 43(5), 118:1–118:9 (2019)CrossRef
40.
Jiang, Y., Chung, F.-L., Wang, S., Deng, Z., Wang, J., Qian, P.: Collaborative fuzzy clustering from multiple weighted views. IEEE Trans. Cybernetics 45(4), 688–701 (2015)CrossRef
41.
Qian, P., Zhao, K., Jiang, Y., Su, K.-H., Deng, Z., Wang, S., Muzic, R.F. Jr.: Knowledge-leveraged transfer fuzzy C-means for texture image segmentation with self-adaptive cluster prototype matching. Knowl. Based Syst. 130, 33–50 (2017)CrossRef
42.
Qian, P., Zhou, J., Jiang, Y., Liang, F., Zhao, K., Wang, S., Su, K.-H., Muzic Jr., R.F.: Multi-view maximum entropy clustering by jointly leveraging inter-view collaborations and intra-view-weighted attributes. IEEE Access 6, 28594–28610 (2018)CrossRef
Metadaten
Titel
Oriented grouping-constrained spectral clustering for medical imaging segmentation
verfasst von
Kaijian Xia
Xiaoqing Gu
Yudong Zhang
Publikationsdatum
29.06.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-019-00626-8

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