Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies

BMC Bioinformatics

Volumn 16, Issue 1, 2015, Pages

  Kurc, Tahsin ^a   Qi, Xin ^b,h   Wang, Daihou ^c   Wang, Fusheng ^a,d   Teodoro, George ^a,e   Cooper, Lee ^f   Nalisnik, Michael ^f   Yang, Lin ^g   Saltz, Joel ^a   Foran, David J ^b,h  

^a Stony Brook University   (United States)

^b ROBERT WOOD JOHNSON MEDICAL SCHOOL   (United States)

^c RUTGERS UNIVERSITY   (United States)

^d Stony Brook University   (United States)

^e UNIVERSITY OF BRASILIA   (Brazil)

^f EMORY UNIVERSITY SCHOOL OF MEDICINE   (United States)

^g J Crayton Pruitt Family Department of Biomedical Engineering   (United States)

^h RUTGERS CANCER INSTITUTE OF NEW JERSEY   (United States)

Author keywords

Databases; gPUs; High performance computing

Indexed keywords

CLUSTER ANALYSIS; CONTENT BASED RETRIEVAL; DATABASE SYSTEMS; PATHOLOGY; QUERY PROCESSING;

COMPUTATIONAL DEMANDS; CONSENSUS CLUSTERING; CONTENT BASED IMAGES; CONTENT-BASED IMAGE RETRIEVAL; GPUS; HIERARCHICAL ANALYSIS; HIGH PERFORMANCE COMPUTING; SHARED MEMORY SYSTEM;

IMAGE ANALYSIS;

CONSENSUS; HUMAN; HUMAN TISSUE; IMAGE RETRIEVAL; MACHINE; MEMORY; MICROSCOPY; PATHOLOGY; SCIENTIST; ALGORITHM; AUTOMATED PATTERN RECOGNITION; CANCER GRADING; CLUSTER ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; DEVICES; DIAGNOSTIC IMAGING; INFORMATION RETRIEVAL; MALE; PROCEDURES; PROSTATE TUMOR; TISSUE MICROARRAY;

ALGORITHMS; CLUSTER ANALYSIS; DIAGNOSTIC IMAGING; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MALE; NEOPLASM GRADING; PATTERN RECOGNITION, AUTOMATED; PROSTATIC NEOPLASMS; TISSUE ARRAY ANALYSIS;

EID: 84949254575     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0831-6     Document Type: Article

References (103)

1. Saltz J, Kurc T, Cooper L, Kong J, Gutman D, Wang F, et al. Multi-Scale, Integrative Study of Brain Tumor: In Silico Brain Tumor Research Center. Proceedings of the Annual Symposium of American Medical Informatics Association 2010 Summit on Translational Bioinformatics (AMIA-TBI 2010), San Francisco, LA 2010
2. Cooper LAD, Kong J, Gutman DA, Wang F, Cholleti SR, Pan TC, et al. An integrative approach for in silico glioma research. IEEE Trans Biomed Eng. 2010;57(10):2617-21
3. Cooper LAD, Kong J, Gutman DA, Wang F, Gao J, Appin C, et al. Integrated morphologic analysis for the identification and characterization of disease subtypes. J Am Med Inform Assoc. 2012;19(2):317-23
4. Beroukhim R, Getz G, Nghiemphu L, Barretina J, Hsueh T, Linhart D, et al. Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc Natl Acad Sci U S A. 2007; 104(50):20007-12
5. Filippi-Chiela EC, Oliveira MM, Jurkovski B, Callegari-Jacques SM, da Silva VD, Lenz G. Nuclear morphometric analysis (NMA): screening of senescence, apoptosis and nuclear irregularities. PLoS ONE. 2012;7(8):e42522
6. Gurcan MN, Pan T, Shimada H, Saltz J. Image Analysis for Neuroblastoma Classification: Segmentation of Cell Nuclei. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2006. p. 4844-7
7. Han J, Chang H, Fontenay GV, Spellman PT, Borowsky A, Parvin B. Molecular bases of morphometric composition in Glioblastoma multiforme. In: 9th IEEE International Symposium on Biomedical Imaging (ISBI '12): 2012. IEEE: 1631-1634
8. Kothari S, Osunkoya AO, Phan JH, Wang MD: Biological interpretation of morphological patterns in histopathological whole-slide images. In: The ACM Conference on Bioinformatics, Computational Biology and Biomedicine: 2012. ACM: 218-225
9. Phan J, Quo C, Cheng C, Wang M. Multi-scale integration of-omic, imaging, and clinical data in biomedical informatics. IEEE Rev Biomed Eng. 2012;5:74-87
10. Cooper L, Kong J, Wang F, Kurc T, Moreno C, Brat D et al. Morphological Signatures and Genomic Correlates in Glioblastoma. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro: 2011; Beijing, China. 1624-1627
11. Kong J, Cooper L, Sharma A, Kurc T, Brat D, Saltz J. Texture Based Image Recognition in Microscopy Images of Diffuse Gliomas With Multi-Class Gentle Boosting Mechanism. Dallas: The 35th International Conference on Acoustics, Speech, and Signal Processing (ICASSP); 2010. p. 457-60
12. Kong J, Sertel O, Boyer KL, Saltz JH, Gurcan MN, Shimada H. Computerassisted grading of neuroblastic differentiation. Arch Pathol Lab Med. 2008; 132(6):903-4
13. Gudivada VN, Raghavan VV: Content-based image retrieval system. Computer 1995:18-21
14. Flickener M, Sawhney H, Niblack W, Ashley J, Huang Q, Dom B, et al. Query by image and video content: the qbic system. Computer. 1995;28(9):23-32
15. Smith JR, Chang SF. Visualseek: A Fully Automated Content-Based Image Query System. In: Proceeding of the Fourth ACM Internation Multimedia Conference and Exhibition. 1996. p. 87-98
16. Tagare HD, Jaffe CC, Duncan J. Medical image databases: a content-based retrieval approach. J Am Med Inform Assoc. 1997;4:184-98
17. Smeulders AWM, Worring M, Santini S, Gupta A, Jainh R. Content-based image retrieval at the end of early years. IEEE Trans Pattern Anal Machine Intel. 2000;22:1349-80
18. Wang J, Li J, Wiederhold G. Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2001;23:947-63
19. Chen Y, Wang J. A region-based fuzzy feature matching approach to content-based image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002;24:1252-67
20. Chang E, Goh K, Sychay G, Wu G. CBSA: content-based soft annotation for multimodal image retreival using bayes point machines. IEEE Transations on Circuits and Systems for Video Technology. 2003;13:26-38
21. Zheng L, Wetzel AW, Gilbertson J, Becich MJ. Design and analysis of a content-based pathology image retrieval system. IEEE Trans Inf Technol Biomed. 2003;7(4):245-55
22. Muller H, Michoux N, Bandon D, Geissbuhler A. A review of content-basd image retrieval systems in medical applicaitons-clinical benefits and future directions. Int J Med Inform. 2004;73:1-23
23. Lehmann TM, Guld MO, Deselaeers T, Keysers D, Schubert H, Spitzer K, et al. Automatic categorization of medical images for content-based retrieval and data mining. Comput Med Imaging Graph. 2005;29:143-55
24. Lam M, Disney T, Pham M, Raicu D, Furst J, Susomboon R. Content-based image retrieval for pulmonary computed tomography nodule images. Proc SPIE 6516, Medical Imaging 2007: PACS and Imaging Informatics, 65160 N 2007, 6516
25. Rahman MM, Antani SK, Thoma GR. A learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM classificaiton and relevance feedback. IEEE Trans Inf Technol Biomed. 2011;15(4):640-6
26. Thies C, Malik A, Keysers D, Kohnen M, Fischer B, Lehmann TM. Hierarchical feature clustering for content-based retrieval in medical image databases. Proc SPIE. 2003;5032:598-608
27. El-Naqa I, Yang Y, Galatsanos NP, NIshikawa RM, Wernick MN. A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE Trans Med Imaging. 2004;23:1233-44
28. Akakin HC, Gurcan MN. Content-based microscopic image retrieval system for multi-image queries. IEEE Trans Inf Technol Biomed. 2012;16:758-69
29. Zhang Q, Izquierdo E. Histology image retrieval in optimized multifeature spaces. IEEE Journal of Biomedical and Health Informatics. 2013;17:240-9
30. Tang HL, Hanka R, Ip HH. Histology image retrieval based on semantic content analysis. IEEE Trans Inf Technol Biomed. 2003;7:26-36
31. Schmidt-Saugenon P, Guillod J, Thiran JP. Towards a computer-aided diagnosis system for pigmented skin lesions. Comput Med Imag Graphics. 2003;27:65-78
32. Sbober A, Eccher C, Blanzieri E, Bauer P, Cristifolini M, Zumiani G, et al. A multiple classifier system for early melanoma diagnosis. Artifical Intel Med. 2003;27:29-44
33. Meyer F. Automatic screening of cytological specimens. Comput Vis Graphics Image Proces. 1986;35:356-69
34. Mattie MEL, Staib ES, Tagare HD, Duncan J, Miller PL. Content-based cell image retrieval using automated feature extraction. J Am Med Informatics Assoc. 2000;7:404-15
35. Beretti S, Bimbo AD, Pala P. Content-Based Retrieval of 3D Cellular Structures. In: Proceeding of the 2nd International Conference on Multimedica and Exposition, IEEE Computer Society. 2001. p. 1096-9
36. Pentland A, Picard RW, Sclaroff S. Phtobook: tools for content-based manipulation of image databases. Int J Comput Vis. 1996;18:233-45
37. Lehmann TM, Guld MO, Thies C, Fischer B, Spitzer K, Keysers D, et al. Content-based image retrieval in medical applications. Methods Inf Med. 2004;4:354-60
38. Cox IJ, Miller ML, Omohundro SM, Yianilos PN. Target Testing and the Picchunter Multimedica Retrieval System. Advances in Digital Libraries. Washington: Library of Congress; 1996. p. 66-75
39. Carson C, Belongies S, Greenspan H, Malik J. Region-Based Image Querying. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 1997. p. 42-51
40. Bui AAT, Taira RK, Dionision JDN, Aberle DR, El-Saden S, Kangarloo H. Evidence-based rediology. Acad Radiol. 2002;9:662-9
41. Qi X, Wang D, Rodero I, Diaz-Montes J, Gensure RH, Xing F, et al. Contentbased histopathology image retrieval using Comet Cloud. BMC Bioinformatics. 2014;15:287. doi:10.1186/1471-2105-1115-1287
42. Kong J, Cooper LAD, Wang F, Gutman DA, Gao J, Chisolm C, et al. Integrative, multimodal analysis of glioblastoma using tcga molecular data, pathology images and clinical outcomes. IEEE Trans Biomed Eng. 2011;58:3469-74
43. Cavallaro A, Graf F, Kriegel H, Schubert M, Thoma M. Reion of Interest Queries in CT Scans. In: Proceedings of the 12th Internatinal Conference on Advances in Spatial and Temporal Databases. 2011. p. 65-73
44. Naik J, Doyle S, Basavanhally A, Ganesan S, Feldman MD, Tomaszwski JE, et al. A boosted distance metric: application to content based image retrieval and classification of digitized histopathology. Proceedings of SPIE Medical Imaging. 2009;7260:1-4
45. Chen W, Schmidt C, Parashar M, Reiss M, Foran DJ. Decentralized data sharing of tissue microarrays for investigative research in oncology. Cancer Informat. 2006;2:373-88
46. Yang L, Chen W, Meer P, Salaru G, Feldman MD, Foran DJ. High throughput analysis of breast cancer specimens on the grid. Med Image Comput Assist Interv. 2007;10(1):617-25
47. Yang L, Tuzel O, Chen W, Meer P, Salaru G, Goodell LA, et al. PathMiner: a web-based tool for computer-assisted diganostics in pathology. IEEE Trans Inf Technol Biomed. 2009;13(3):291-9
48. Foran DJ, Yang L, Chen W, Hu J, Goodell LA, Reiss M, et al. ImageMiner: a software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology. J Am Med Inform Assoc. 2011;18(4):403-15
49. Qi X, Kim H, Xing F, Parashar M, Foran DJ, Yang L. The analysis of image feature robustness using CometCloud. Journal of Pathology Informatics. 2012;3
50. Vetter JS, Glassbrook R, Dongarra J, Schwan K, Loftis B, McNally S, et al. Keeneland: bringing heterogeneous GPU computing to the computational science community. Computing in Science and Engineering. 2011;13(5):90-5
51. Linderman MD, Collins JD, Wang H, Meng TH. Merge: a programming model for heterogeneous multi-core systems. SIGPLAN Notices. 2008;43(3): 287-96
52. Diamos GF, Yalamanchili S. Harmony: An Execution Model and Runtime for Heterogeneous Many-Core Systems. In: Proceedings of the 17th International Symposium on High Performance Distributed Computing, vol. 1383447. Boston: ACM; 2008. p. 197-200
53. Luk C-K, Hong S, Kim H. Qilin: Exploiting Parallelism on Heterogeneous Multiprocessors With Adaptive Mapping. In: Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture, vol. 1669121. New York: ACM; 2009. p. 45-55
54. Augonnet C, Thibault S, Namyst R, Wacrenier P-A. StarPU: a unified platform for task scheduling on heterogeneous multicore architectures. Concurr Comput: Pract Exper. 2011;23(2):187-98
55. Teodoro G, Oliveira RS, Sertel O, Gurcan MN, Jr. WM, Çatalyürek ÜV, Ferreira R: Coordinating the use of GPU and CPU for improving performance of compute intensive applications. In: CLUSTER: 2009; New Orleans, Louisiana. conf/cluster/TeodoroOSGMCF09: IEEE: 1-10
56. Sundaram N, Raghunathan A, Chakradhar ST: A framework for efficient and scalable execution of domain-specific templates on GPUs. In: Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing: 2009. 1587427: IEEE Computer Society: 1-12
57. Teodoro G, Hartley TDR, Catalyurek U, Ferreira R: Run-time optimizations for replicated dataflows on heterogeneous environments. In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing: 2010; Chicago, Illinois. 1851479: ACM: 13-24
58. Teodoro G, Hartley TD, Catalyurek UV, Ferreira R. Optimizing dataflow applications on heterogeneous environments. Clust Comput. 2012;15(2): 125-44
59. Bosilca G, Bouteiller A, Herault T, Lemarinier P, Saengpatsa NO, Tomov S, Dongarra JJ: Performance Portability of a GPU Enabled Factorization with the DAGuE Framework. In: Proceedings of the 2011 IEEE International Conference on Cluster Computing: 2011. 2065710: IEEE Computer Society: 395-402
60. Ravi VT, Ma W, Chiu D, Agrawal G. Compiler and Runtime Support for Enabling Generalized Reduction Computations on Heterogeneous Parallel Configurations. In: Proceedings of the 24th ACM International Conference on Supercomputing, vol. 1810106. Tsukuba, Ibaraki, Japan: ACM; 2010. p. 137-46
61. Huo X, Ravi VT, Agrawal G. Porting irregular reductions on heterogeneous CPU-GPU configurations. In: Proceedings of the 18th International Conference on High Performance Computing, vol. 2192618. Bangalore: IEEE Computer Society; 2011. p. 1-10
62. Lee S, Min S-J, Eigenmann R: OpenMP to GPGPU: a compiler framework for automatic translation and optimization. In: Proceedings of the 14th ACM SIGPLAN symposium on Principles and practice of parallel programming: 2009; Raleigh, NC, USA. 1504194: ACM: 101-110
63. Bradski G, Kaehler A. Learning OpenCV: Computer vision with the OpenCV library: O'Reilly. 2008
64. Kahn MG, Weng C. Clinical research informatics: a conceptual perspective. J Am Med Inform Assoc. 2012;19:36-42
65. Carriero N, Osier MV, Cheung K-H, Miller PL, Gerstein M, Zhao H, et al. Case Report: A High Productivity/Low Maintenance Approach to Highperformance Computation for Biomedicine: Four Case Studies. J Am Med Inform Assoc. 2005;12(1):90-8
66. Lindberg DAB, Humphrey BL. High-performance computing and communications and the national information infrastructure: New opportunities and challenges. J Am Med Inform Assoc. 1995;2(3):197
67. Huang Y, Lowe HJ, Klein D, Cucina RJ. Improved identification of noun phrases in clinical radiology reports using a high-performance statistical natural language parser augmented with the UMLS specialist lexicon. J Am Med Inform Assoc. 2004;12(3):275-85
68. Kaspar M, Parsad NM, Silverstein JC. An optimized web-based approach for collaborative stereoscopic medical visualization. J Am Med Inform Assoc. 2013;20(3):535-43
69. Yang L, Chen W, Meer P, Salaru G, Goodell LA, Berstis V, et al. Virtual microscopy and grid-enabled decision support for large-scale analysis of imaged pathology specimens. Trans Info Tech Biomed. 2009;13(4):636-44
70. Eliceiri KW, Berthold MR, Goldberg IG, Ibanez L, Manjunath BS, Martone ME, et al. Biological imaging software tools. Nat Meth. 2012;9(7):697-710
71. Fang Z, Lee JH. High-throughput optogenetic functional magnetic resonance imaging with parallel computations. J Neurosci Methods. 2013; 218(2):184-95
72. Wang Y, Du H, Xia M, Ren L, Xu M, Xie T, et al. A hybrid CPU-GPU accelerated framework for fast mapping of high-resolution human brain connectome. PLoS ONE. 2013;8(5):e62789
73. Webb C, Gray A. Large-scale virtual acoustics simulation at audio rates using three dimensional finite difference time domain and multiple graphics processing units. J Acoust Soc Am. 2013;133(5):3613
74. Hernández M, Guerrero GD, Cecilia JM, García JM, Inuggi A, Jbabdi S, et al. Accelerating fibre orientation estimation from diffusion weighted magnetic resonance imaging using GPUs. PLoS ONE. 2013;8(4), e61892
75. Hu X, Liu Q, Zhang Z, Li Z, Wang S, He L, et al. SHEsisEpi, a GPU-enhanced genome-wide SNP-SNP interaction scanning algorithm, efficiently reveals the risk genetic epistasis in bipolar disorder. Cell Res. 2010;20(7):854-7
76. Sertel O, Kong J, Shimada H, Catalyurek UV, Saltz JH, Gurcan MN. Computeraided prognosis of neuroblastoma on whole-slide images: classification of stromal development. Pattern Recogn. 2009;42(6):1093-103
77. Ruiz A, Sertel O, Ujaldon M, Catalyurek U, Saltz JH, Gurcan M. Pathological Image Analysis Using the GPU: Stroma Classification for Neuroblastoma. In: IEEE International Conference on Bioinformatics and Biomedicine: 2007; Fremont, CA. 78-88
78. Hartley TDR, Catalyurek U, Ruiz A, Igual F, Mayo R, Ujaldon M: Biomedical image analysis on a cooperative cluster of GPUs and multicores. In: Proceedings of the 22nd annual international conference on Supercomputing: 2008; Island of Kos, Greece. 1375533: ACM: 15-25
79. Teodoro G, Pan T, Kurc TM, Kong J, Cooper LAD, Saltz JH. Efficient irregular wavefront propagation algorithms on hybrid CPU-GPU machines. Parallel Comput. 2013;39(4-5):189-211
80. Teodoro G, Kurc TM, Pan T, Cooper LAD, Jun K, Widener P et al.: Accelerating Large Scale Image Analyses on Parallel, CPU-GPU Equipped Systems. In: Proceedings of the IEEE 26th International Parallel & Distributed Processing Symposium: 21-25 May 2012 2012; Shanghai, China. 1093-1104
81. Teodoro G, T. Pan, T. M. Kurc, J. Kong, L. A. Cooper, N. Podhorszki, et al. High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms. In: the 27th IEEE International Parallel and Distributed Processing Symposium (IPDPS): May 20-24 2013; Boston, Massachusetts, USA. May 20-24. 24: 103-114
82. Asur S, Ucar D, Parthasarathy S. An ensemble framework for clustering protein-protein interaction networks. Bioinformatics. 2007;23(13):i29-40
83. Forero P, Cano A, Giannakis G. Consensus Based k-Means Algorithm for Distributed Learning Using wireless sensor networks. Signal and Info Process, Sedona, AZ: Proc Workshop on Sensors; 2008
84. Hore P, Hall LO, Goldgof DB. A scalable framework for cluster ensembles. Pattern Recogn. 2009;42(5):676-88
85. Iam-on N, Garrett S. LinkCluE: a MATLAB package for link-based cluster ensembles. J Stat Softw. 2010;36(9):1-36
86. Luo DJ, Ding C, Huang H, Nie FP. Consensus Spectral Clustering in Near-Linear Time. IEEE 27th International Conference on Data Engineering (ICDE 2011). 2011. p. 1079-90
87. Minaei-Bidgoli B, Topchy A, Punch W. A Comparison of Resampling Methods for Clustering Ensembles. International Conference on Machine Learning; Models, Technologies and Application (MLMTA04). 2004. p. 939-45
88. Strehl A, Ghosh J. Cluster Ensembles-A Knowledge Reuse Framework for Combining Partitionings. In: Proceedings of Eighteenth National Conference on Artificial Intelligence (AAAI-02)/Fourteenth Innovative Applications of Artificial Intelligence Conference (IAAI-02). 2002. p. 93-8
89. Zhang J, Yang Y, Wang H, Mahmood A, Huang F. Semi-Supervised Clustering Ensemble Based on Collaborative Training. In: Nguyen L, Wang G, Grzymala-Busse J, Janicki R, Hassanien A, Yu H, editors. Rough Sets and Knowledge Technology, ser Lecture Notes in Computer Science. 7414th ed. Berlin Heidelberg: Springer; 2012. p. 450-5
90. Yang L, Qi X, Xing F, Kurc T, Saltz J, Foran DJ. Parallel content-based sub-image retrieval using hierarchical searching. Bioinformatics. 2014;30(7):996-1002
91. Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resamplingbased method for class discovery and visualization of gene expression microarray data. Mach Learn. 2003;52(1):91-118
92. Vincent L. Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans Image Process. 1993;2(2): 176-201
93. Körbes A, Vitor GB, Lotufo RA, Ferreira JV. Advances on Watershed Processing on GPU Architecture. In: Proceedings of the 10th International Conference on Mathematical Morphology and its Applications to Image and Signal Processing, vol. 2023072. Verbania-Intra: Springer; 2011. p. 260-71
94. Millstein T. Practical predicate dispatch. SIGPLAN Notices. 2004;39:345-464
95. Jablin TB, Prabhu P, Jablin JA, Johnson NP, Beard SR, August DI. Automatic CPU-GPU Communication Management and Optimization. In: Proceedings of the 32nd ACM SIGPLAN Conference on Programming Language Design and Implementation, vol. 1993516. San Jose: ACM; 2011. p. 142-51
96. Wang F, Kong J, Cooper L, Pan T, Kurc T, Chen W, et al. A data model and database for high-resolution pathology analytical image informatics. J Pathol Inform. 2011;2:32
97. Wang F, Kong J, Gao J, Cooper LA, Kurc T, Zhou Z, et al. A highperformance spatial database based approach for pathology imaging algorithm evaluation. J Pathol Inform. 2013;4:5
98. Hartigan J. Clustering Algorithms. Hoboken: Wiley; 1975
99. Forgy EW. Cluster Analysis of Multivariate Data-Efficiency vs Interpretability of Classifications. Biometrics. 1965;21(3):768
100. Lloyd SP. Least-Squares Quantization in Pcm. IEEE Trans Inf Theory. 1982; 28(2):129-37
101. Parallel k-means data clustering package [http://users.eecs.northwestern. edu/~wkliao/Kmeans. Access date: Nov, 2015
102. Volkov V, Demmel JW. Benchmarking GPUs to tune dense linear albebra. International Conference for High Performance Computing, Networking, Storage and Analysis, Supercomputing. 2008;2008:499-509
103. Tomov S, Dongarra J, Baboulin M. Towards dense linear algebra for hybrid GPU accelerated many core systems. Parallel Comput. 2010;36(5-6):232-40