Abstract
Clustering is an important issue in brain medical image segmentation. Original medical images used for clinical diagnosis are often insufficient for clustering in the current domain. As there are sufficient medical images in the related domains, transfer clustering can improve the clustering performance of the current domain by transferring knowledge across the related domains. In this article, we propose a novel shared hidden space transfer fuzzy c-means (FCM) clustering called SHST-FCM for cross-domain brain computed tomography (CT) image segmentation. SHST-FCM projects both the data samples of the source domain and target domain into the shared hidden space, such that the distributions of the two domains are as close as possible. In the learned shared subspace, the data samples of the source domain serve as the auxiliary knowledge to aid the clustering process in the target domain. Extensive experiments on brain CT medical image datasets indicate the effectiveness of the proposed method.
- S. M. Smith. 2002. Fast robust automated brain extraction. Human Brain Mapping 17 (2002), 143--155. DOI:doi.org/10.1002/hbmGoogle Scholar
Cross Ref
- L. Babcock, T. Byczkowski, S. Mookerjee, and J. J. Bazarian. 2012. Ability of S100B to predict severity and cranial CT results in children with TBI, Brain Injury 26, 11 (2012), 1372--1380. DOI:10.3109/02699052.2012.694565Google Scholar
Cross Ref
- H. S. Nguyen, L. Li, M. Patel, and W. Mueller. 2016. Density measurements with computed tomography in patients with extra-axial hematoma can quantitatively estimate a degree of brain compression. The Neuroradiology Journal 29, 5 (2016), 372--376. DOI:10.1177/1971400916658795Google Scholar
Cross Ref
- N. Otsu. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9, 1 (1979), 62--66. DOI:10.1109/TSMC.1979.4310076Google Scholar
Cross Ref
- L. S. Davis. 1975. A survey of edge detection techniques. Computer Graphics and Image Processing 4, 3 (1975), 248--270. DOI:10.1016/0146-664X(75)90012-XGoogle Scholar
Cross Ref
- X. Zhao, Y. Wu, G. Song, Z. Li, Y. Zhang, and Y. Fan. 2017. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Medical Image Analysis 43 (2017), 98--111. DOI:10.1016/j.media.2017.10.002Google Scholar
Cross Ref
- R. Agrawal, M. Sharma, and B. K. Singh. 2018. Segmentation of brain lesions in MRI and CT scan images: A hybrid approach using k-means clustering and image morphology. Journal of the Institution of Engineers 99, 2 (2018), 1--8. DOI:10.1007/s40031-018-0314-zGoogle Scholar
- P. F. Felzenszwalb and D. P. Huttenlocher. 2004. Efficient graph based image segmentation. International Journal of Computer Vision 59, 2 (2004), 167--181. DOI:10.1023/b:visi.0000022288.19776.77Google Scholar
Digital Library
- Y. Boykov and G. Funka-Lea. 2006. Graph cuts and efficient ND image segmentation. International Journal of Computer Vision 70, 2 (2006), 109--131. DOI:10.1109/ICIP.2008.4711858Google Scholar
Cross Ref
- Y. A. Sheikh, E. A. Khan, and T. Kanade. 2007. Mode-seeking by medoidshifts. In IEEE International Conference on Computer Vision (2007). 1--8. DOI:10.1109/ICCV.2007.4408978Google Scholar
- R. Achanta, A. Shaji, and K. Smith. 2012. Slic superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 11 (2012), 2274--2282. DOI:10.1109/TPAMI.2012.120Google Scholar
Digital Library
- X. Y. Wang, Z. F. Wu, and L. Chen. 2016. Pixel classification based color image segmentation using quaternion exponent moments. Neural Networks 74, C (2016), 1--13. DOI:10.1016/j.neunet.2015.10.012Google Scholar
Digital Library
- J. Long, E. Shelhamer, and T. Darrell. 2015. Fully convolutional networks for semantic segmentation. In BIEEE Conference on Computer Vision and Pattern Recognition (2015). 3431--3440. DOI:10.1109/CVPR.2015.7298965Google Scholar
- J. Carreira and C. Sminchisescu. 2012. CPMC: Automatic object segmentation using constrained parametric min-cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 7 (2012), 1312--1328. DOI:10.1109/TPAMI.2011.231Google Scholar
Digital Library
- J. Carreira, C. Rui, and J. Batista. 2012. Semantic segmentation with second-order pooling. In Computer Vision (ECCV 2012). Springer, Berlin, 430--443. DOI:10.1007/978-3-642-33786-4-32Google Scholar
- J. R. Uijlings, K. E. Sande, and T. Gevers. 2013. Selective search for object recognition. International Journal of Computer Vision 104, 2 (2013), 154--171. DOI:10.1007/s11263-013-0620-5Google Scholar
Digital Library
- S. A. Burney and H. Tariq. 2014. K-means cluster analysis for image segmentation. International Journal of Computer Applications 96, 4 (2014), 1--8. DOI:10.5120/16779-6360Google Scholar
- D. Gómez, J. Yáñez, C. Guada, J. T. Rodríguez, J. Montero, and E. Zarrazola. 2015. Fuzzy image segmentation based upon hierarchical clustering. Knowledge-Based Systems, 87 (2015), 26--37. DOI:10.1016/j.knosys.2015.07.017Google Scholar
Digital Library
- H. S. Le, B. C. Cuong, and P. L. Lanzi. 2012. A novel intuitionistic fuzzy clustering method for geo-demographic analysis. Expert Systems with Applications 39, 12 (2012), 9848--9859. DOI:10.1016/j.eswa.2012.02.167Google Scholar
Digital Library
- J. C. Bezdek. 1981. Pattern Recognition with Fuzzy Objective Functions, Plenum Press, New York, 1981.Google Scholar
Digital Library
- R. P. Nikhil, P. Kuhu, M. K. James, and C. B. James. 2005. A possibilistic fuzzy c-means clustering algorithm, IEEE Transactions on Fuzzy Systems 13, 4 (2005), 517--530. DOI:10.1109/tfuzz.2004.840099Google Scholar
Digital Library
- C. C. Hung, S. Kulkarni, and B. C. Kuo. 2011. A new weighted fuzzy c-means clustering algorithm for remotely sensed image classification. IEEE Journal of Selected Topics in Signal Processing 5, 3 (2011), 543--553. DOI:10.1109/JSTSP.2010.2096797Google Scholar
Cross Ref
- J. H. Richard, C. B. James, and Y. Hu. 2000. Generalized fuzzy c-means clustering strategies using Lp norm distances, IEEE Transactions on Fuzzy Systems 8, 5 (2000), 576--582. DOI:10.1109/91.873580Google Scholar
Digital Library
- S. J. Pan and Q. Yang. 2010. A survey on transfer learning, IEEE Transactions on Knowledge Data Engineering 22, 10 (2010), 1345--1359. DOI:10.1109/TKDE.2009.191Google Scholar
Digital Library
- K. Kummamuru, A. Dhawale, and R. Krishnapuram. 2003. Fuzzy co-clustering of documents and keywords. In 12th IEEE International Conference on Fuzzy Systems (2003), 772--777. DOI:10.1109/FUZZ.2003.1206527Google Scholar
- Y. Wang and Y. Gelan. 2018. A new brain magnetic resonance imaging segmentation algorithm based on subtractive clustering and fuzzy c-means clustering. Journal of Medical Imaging and Health Informatics 8, 3 (2018), 602--608. DOI:10.1166/jmihi.2018.2309Google Scholar
Cross Ref
- M. C. Lee, K. S. Chuang, M. K. Chen, C. K. Liu, and H. H. Lin. 2016. Fuzzy c-means clustering of magnetic resonance imaging on apparent diffusion coefficient maps for predicting nodal metastasis in head and neck cancer. The British Journal of Radiology 89 (2016), 20150059. DOI:10.1259/bjr.20150059Google Scholar
Cross Ref
- A. Elazab, Y. M. Abdulazeem, S. Wu, Q. Hu, and K. Wong. 2016. Robust kernelized local information fuzzy c-means clustering for brain magnetic resonance image segmentation. Journal of X-Ray Science and Technology 24, 3 (2016), 489--507. DOI:10.3233/XST-160563Google Scholar
Cross Ref
- A. Ortiz, A. A. Palacio, and J. M. Górriz. 2013. Segmentation of brain MRI using SOM-FCM-based method and 3D statistical descriptors. Computational and Mathematical Methods in Medicine (2013), 1--12. DOI:10.1155/2013/638563Google Scholar
- T. Altameem, E. A. Zanaty, and A. Tolba. 2014. A new fuzzy c-means method for magnetic resonance image brain segmentation. Connection Science 27, 4 (2014), 1--17. DOI:10.1080/09540091.2014.970126Google Scholar
Digital Library
- C. R. Ng, J. C. M. Than, N. M. Noor, and O. M. Rijal. 2015. Preliminary brain region segmentation using FCM and graph cut for CT scan images. In IEEE International Conference on BioSignal Analysis, Processing and Systems (ICBAPS’15), 52--56. DOI:10.1109/ICBAPS.2015.7292217Google Scholar
- H. Huang, F. Meng, S. Zhou, and F. Jiang. 2019. Brain image segmentation based on FCM clustering algorithm and rough set. IEEE Access 7 (2019), 12386--12396. DOI:10.1109/ACCESS.2019.2893063Google Scholar
Cross Ref
- D. Han, Q. Liu, and W. Fan. 2018. A new image classification method using CNN transfer learning and web data augmentation. Expert Systems with Applications 95 (2018), 43--56. DOI:10.1016/j.eswa.2017.11.028Google Scholar
Cross Ref
- P. Qian, K. Zhao, Y. Jiang, K. H. Su, and Z. Deng. 2017. Knowledge-leveraged transfer fuzzy c-means for texture image segmentation with self-adaptive cluster prototype matching. Knowledge-Based Systems 130, 15 (2017), 33--50. DOI:10.1016/j.knosys.2017.05.018Google Scholar
Cross Ref
- Z. Deng, Y. Jiang, F. L. Chung, and H. Ishibuchi. 2014. Transfer prototype-based fuzzy clustering. IEEE Transactions on Fuzzy Systems 24, 5 (2014), 1210--1232. DOI:10.1109/TFUZZ.2015.2505330Google Scholar
Cross Ref
- X. Zhang and X. Liu. 2017. Multi-task clustering through instances transfer. Neurocomputing 251 (2017), 145--155. DOI:10.1016/j.neucom.2017.04.029Google Scholar
Digital Library
- A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. J. Smola. 2006. A kernel method for the two-sample-problem. In Proceedings of the 20th Annual Conference on Neural Information Processing Systems (2006), pp. 513--520.Google Scholar
- J. Shi and J. Malik. 2000. Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intellgence 22, 8 (2000), 888--905. DOI:10.1109/34.868688Google Scholar
Digital Library
- W. Pan and Q. Yang. 2013. Transfer learning in heterogeneous collaborative filtering domains. Artificial intelligence 197 (2013), 39--55. DOI:10.1016/j.artint.2013.01.003Google Scholar
Digital Library
- M. T. Hooijmans, O. Dzyubachyk, and K. Nehrke. 2015. Fast multistation water/fat imaging at 3T using DREAM-based RF shimming. Journal of Magnetic Resonance Imaging 42, 1 (2015), 217--223. DOI:10.1002/jmri.24775Google Scholar
Cross Ref
- A. Kalemis, B. M. Delattre, and S. Heinzer. 2013. Sequential whole-body PET/MR scanner: Concept, clinical use, and optimisation after two years in the clinic. The Manufacturer's Perspective 26, 1 (2013), 5--23. DOI:10.1007/s10334-012-0330-yGoogle Scholar
- W. Jiang and F. Chung. 2012. Transfer spectral clustering. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, Vol. 7524 (2012), 789--803. DOI:10.1007/978-3-642-33486-3_50Google Scholar
- P. Qian, S. Sun, Y. Jiang, K. H. Su, and T. Ni. 2016. Cross-domain, soft-partition clustering with diversity measure and knowledge reference. Pattern Recognition 50 (2016), 155--177. DOI:10.1016/j.patcog.2015.08.009Google Scholar
Digital Library
- A. A. Taha and A. Hanbury. 2015. Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Medical Imaging 15, 29 (2015), 1--28. DOI:10.1186/s12880-015-0068-xGoogle Scholar
Cross Ref
Index Terms
Cross-Domain Brain CT Image Smart Segmentation via Shared Hidden Space Transfer FCM Clustering
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