Metric hashing forests

Medical Image Analysis

Volumn 34, Issue , 2016, Pages 13-29

  Conjeti, Sailesh ^a   Katouzian, Amin ^b   Kazi, Anees ^a,c   Mesbah, Sepideh ^a   Beymer, David ^b   Syeda Mahmood, Tanveer F ^b   Navab, Nassir ^a,d  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b IBM ALMADEN RESEARCH CENTER   (United States)

^c INDIAN INSTITUTE OF TECHNOLOGY   (India)

^d Johns Hopkins University   (United States)

Author keywords

Hashing; Image retrieval; Metric learning; Neurons; Neuroscience; Random forests

Indexed keywords

BINARY TREES; DECISION TREES; IMAGE RETRIEVAL; INVERSE PROBLEMS;

EXPERIMENTAL VALIDATIONS; HASHING; LOCALITY PRESERVING PROJECTIONS; METRIC LEARNING; NEUROSCIENCE; RANDOM FORESTS; RETRIEVAL PERFORMANCE; SIMILARITY PRESERVING;

NEURONS;

ACCURACY; ARTICLE; BRAIN REGION; CELL STRUCTURE; CHILD; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; DATA BASE; DECISION SUPPORT SYSTEM; DECISION TREE; HUMAN; HUMAN CELL; INFORMATION RETRIEVAL; LEARNING ALGORITHM; MACHINE LEARNING; METRIC HASHING FOREST; NEIGHBORHOOD; NERVE CELL; NEUROSCIENCE; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; RANDOM FOREST; VALIDATION STUDY; CLASSIFICATION; FACTUAL DATABASE; INFORMATION CENTER; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ARCHIVES; DATABASES, FACTUAL; HUMANS; MACHINE LEARNING; NEURONS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84989166317     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2016.05.010     Document Type: Article

References (48)

1. Ascoli, G.A., Donohue, D.E., Halavi, M., Neuromorpho. org: a central resource for neuronal morphologies. J. Neurosci. 27:35 (2007), 9247–9251.
2. Ballerini, L., Li, X., Fisher, R.B., Rees, J., A query-by-example content-based image retrieval system of non-melanoma skin lesions. Medical Content-Based Retrieval for Clinical Decision Support, 2010, Springer Berlin Heidelberg, 31–38.
3. Bellet, A., Habrard, A., Sebban, M., 2013. A survey on metric learning for feature vectors and structured data. ArXiv preprint arXiv:1306.6709.
4. Bentley, J.L., Multidimensional binary search trees used for associative searching. Commun. ACM 18:9 (1975), 509–517.
5. Breiman, L., Random forests. Mach. Learn. 45:1 (2001), 5–32.
6. Cai, D., He, X., Zhou, K., Han, J., Bao, H., Locality sensitive discriminant analysis. IJCAI, 2007, 708–713.
7. Chiang, A.S., Lin, C.Y., Chuang, C.C., Chang, H.M., Hsieh, C.H., Yeh, C.W., Shih, C.T., Wu, J.J., Wang, G.T., Chen, Y.C., Wu, C.C., Three-dimensional reconstruction of brain-wide wiring networks in drosophila at single-cell resolution. Current Biol. 21:1 (2011), 1–11.
8. Costa, L.D.F., Zawadzki, K., Miazaki, M., Viana, M.P., Taraskin, S.N., Unveiling the neuromorphological space. Frontiers Comput. Neurosci, 4, 2010.
9. Costa, M., Ostrovsky, A.D., Manton, J.D., Prohaska, S., Jefferis, G.S., NBLAST: rapid, sensitive comparison of neuronal structure and construction of neuron family databases. bioRxiv, 2014, 006346.
10. Criminisi, A., Shotton, J., Konukoglu, E., Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Foundations Trends Comput. Graph. Vision 7:23 (2012), 81–227.
11. Foncubierta-Rodríguez, A., Vargas, A., Platon, A., Poletti, P.A., Mller, H., Depeursinge, A., Retrieval of 4d dual energy CT for pulmonary embolism diagnosis. Medical Content-Based Retrieval for Clinical Decision Support, 2013, Springer Berlin Heidelberg, 45–55.
12. Ganglberger, F., Schulze, F., Tirian, L., Novikov, A., Dickson, B., Bhler, K., Langs, G., Structure-based neuron retrieval across drosophila brains. Neuroinformatics 12:3 (2014), 423–434.
13. Gionis, A., Indyk, P., Motwani, R., Similarity search in high dimensions via hashing. VLDB, 99, 1999, 518–529.
14. Goldberger, J., Hinton, G.E., Roweis, S.T., Salakhutdinov, R., Neighbourhood components analysis. Advances in neural information processing systems, 2004, 513–520.
15. Gong, Y., Lazebnik, S., Iterative quantization: a procrustean approach to learning binary codes. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, 2011, IEEE, 817–824.
16. He, X., Cai, D., Yan, S., Zhang, H.J., Neighborhood preserving embedding. Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, 2, 2005, 1208–1213 IEEE.
17. He, X., Niyogi, P., Locality preserving projections. Advances in Neural Information Processing Systems, 2004, 153–160.
18. Konukoglu, E., Glocker, B., Zikic, D., Criminisi, A., Neighbourhood approximation using randomized forests. Medical image analysis 17:7 (2013), 790–804.
19. Kulis, B., Metric learning: a survey. Foundations Trends Mach. Learn. 5:4 (2012), 287–364.
20. Liu, W., Wang, J., Ji, R., Jiang, Y.G., Chang, S.F., Supervised hashing with kernels. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012, 2074–2081 IEEE.
21. Liu, W., Wang, J., Kumar, S., Chang, S.F., Hashing with graphs. Proceedings of the 28th international conference on machine learning (ICML-11), 2011, 1–8.
22. Lombaert, H., Criminisi, A., Ayache, N., Spectral Forests: Learning of Surface Data, Application to Cortical Parcellation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015, Springer International Publishing, 547–555.
23. Menze, B.H., Kelm, B.M., Splitthoff, D.N., Koethe, U., Hamprecht, F.A., On oblique random forests. Machine Learning and Knowledge Discovery in Databases, 2011, Springer Berlin Heidelberg, 453–469.
24. Mesbah, S., Conjeti, S., Kumaraswamy, A., Rautenberg, P., Navab, N., Katouzian, A., Hashing forests for morphological search and retrieval in neuroscientific image databases. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015, 2015, Springer International Publishing, 135–143.
25. Overview of L-Measure, 2015. Available at: http://cng.gmu.edu:8080/Lm/help/index.htm, (Accessed: 25 August 2015).
26. Parekh, R., Ascoli, G.A., Neuronal morphology goes digital: a research hub for cellular and system neuroscience. Neuron 77:6 (2013), 1017–1038.
27. Peng, H., Roysam, B., Ascoli, G.A., Automated image computing reshapes computational neuroscience. BMC Bioinformatics, 14(1), 2013, 293.
28. Peng, H., Ruan, Z., Long, F., Simpson, J.H., Myers, E.W., V3d enables real-time 3d visualization and quantitative analysis of large-scale biological image data sets. Nature Biotechnol. 28:4 (2010), 348–353.
29. Polavaram, S., Gillette, T.A., Parekh, R., Ascoli, G.A., Statistical analysis and data mining of digital reconstructions of dendritic morphologies. Frontiers Neuroanatomy 8:138 (2014), 1–16, 10.3389/fnana.2014.00138.
30. Rautenberg, P.L., Grothe, B., Felmy, F., Quantification of the three-dimensional morphology of coincidence detector neurons in the medial superior olive of gerbils during late postnatal development. J. Comparative Neurol. 517:3 (2009), 385–396.
31. Rautenberg, P.L., Kumaraswamy, A., Tejero-Cantero, A., Doblander, C., Norouzian, M.R., Kai, K., Jacobsen, H.A., Ai, H., Wachtler, T., Ikeno, H., Neurondepot: keeping your colleagues in sync by combining modern cloud storage services, the local file system, and simple web applications. Frontiers Neuroinform. 8:55 (2014), 1–11.
32. Scorcioni, R., Polavaram, S., Ascoli, G.A., L-measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nature Protocols 3:5 (2008), 866–876.
33. Search by Cell Type, 2015. Neuromorpho.or Available at: http://neuromorpho.org/bycell.jsp.
34. Search by Morphometry, 2015. Neuromorpho.org Available at: http://neuromorpho.org/neuroMorpho/MorphometrySearch.jsp.
35. Slaney, M., Casey, M., Locality-sensitive hashing for finding nearest neighbors [lecture notes]. Signal Process. Mag. IEEE 25:2 (2008), 128–131.
36. Syeda-Mahmood, T., Wang, F., Kumar, R., Beymer, D., Zhang, Y., Lundstrom, R., McNulty, E., Finding similar 2d x-ray coronary angiograms. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2012, 2012, Springer Berlin Heidelberg, 501–508.
37. Wan, Y., Long, F., Qu, L., Xiao, H., Hawrylycz, M., Myers, E.W., Peng, H., Blastneuron for automated comparison, retrieval and clustering of 3d neuron morphologies. Neuroinformatics 13:4 (2015), 487–499.
38. Wang, J., Liu, W., Kumar, S., Chang, S.F., Learning to hash for indexing big data-a survey. Proc. IEEE 104:1 (2016), 34–57.
39. Wang, J., Shen, H. T., Song, J., Ji, J., 2014. Hashing for similarity search: a survey. ArXiv preprint arXiv:1408.2927.
40. Wang, J., Woznica, A., Kalousis, A., Learning neighborhoods for metric learning. Machine Learning and Knowledge Discovery in Databases, 2012, 223–236 Springer Berlin Heidelberg.
41. Weinberger, K.Q., Saul, L.K., Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10 (2009), 207–244.
42. Weiss, Y., Torralba, A., Fergus, R., Spectral hashing. Advances in neural information processing systems, 2009, 1753–1760.
43. Yianilos, P.N., Data structures and algorithms for nearest neighbor search in general metric spaces. SODA 93:194 (1993), 311–321.
44. Yu, C., Han, Z., Zeng, W., Liu, S., Morphology cluster and prediction of growth of human brain pyramidal neurons. Neural Regeneration Res., 7(1), 2012, 36.
45. Yu, G., Yuan, J., Scalable forest hashing for fast similarity search. Multimedia and Expo (ICME), 2014 IEEE International Conference on, 2014, IEEE, 1–6.
46. Zelnik-Manor, L., Perona, P., Self-tuning spectral clustering. Advances in neural information processing systems, 2004, 1601–1608.
47. Zhang, X., Liu, W., Dundar, M., Badve, S., Zhang, S., Towards large-scale histopathological image analysis: Hashing-based image retrieval. IEEE Trans. Medical Imaging 34:2 (2015), 496–506.
48. Zhang, X., Yang, L., Liu, W., Su, H., Zhang, S., Mining histopathological images via composite hashing and online learning. Lecture Notes in Comput. Sci. 8674 (2014), 479–486.