Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Optical and Quantum Electronics
Erschienen in: Optical and Quantum Electronics 6/2016

01.06.2016

Perspectives on combining Nonlinear Laser Scanning Microscopy and Bag-of-Features data classification strategies for automated disease diagnostics

verfasst von: Stefan G. Stanciu, Denis E. Tranca, George A. Stanciu, Radu Hristu, Juan M. Bueno

Erschienen in: Optical and Quantum Electronics | Ausgabe 6/2016

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Nonlinear Laser Scanning Microscopy (NLSM) techniques have been demonstrated in the past two decades as powerful imaging tools for disease diagnostics (DD). Currently, in most DD related experiments the interpretation of NLSM data sets is performed by trained specialists. Such approaches are both time consuming and prone to errors due to inter- and intra-observer discrepancies. The Bag-of-Features (BoF) paradigm has demonstrated its potential usefulness with respect to automated data classification in the frame of multiple experiments, but its intersections with the field of NLSM are at this moment scarce, to say the least. In this paper we review recent progress on DD using NLSM, and discuss necessary steps and potential future perspectives for merging NLSM and BoF to achieve complex frameworks for automated DD with high sensitivity and specificity.
Vorheriger Artikel Optical properties of ZnO-based step quantum wells
Nächster Artikel Combined terahertz imaging system for enhanced imaging quality

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
Alahi, A., Ortiz, R., Vandergheynst, P.: Freak: fast retina keypoint. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 510–517. IEEE (2012)
Aly, M., Munich, M., Perona, P.: Multiple dictionaries for bag of Words Large Scale Image Search. In: IEEE International Conference on Image Processing (ICIP) (2011)
Aucouturier, J.-J., Defreville, B., Pachet, F.: The bag-of-frames approach to audio pattern recognition: a sufficient model for urban soundscapes but not for polyphonic music. J. Acoust. Soc. Am. 122(2), 881–891 (2007)ADSCrossRef
Baenke, F., Peck, B., Miess, H., Schulze, A.: Hooked on fat: the role of lipid synthesis in cancer metabolism and tumour development. Dis. Models Mech. 6(6), 1353–1363 (2013)CrossRef
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRef
Birk, J.W., Tadros, M., Moezardalan, K., Nadyarnykh, O., Forouhar, F., Anderson, J., Campagnola, P.: Second harmonic generation imaging distinguishes both high-grade dysplasia and cancer from normal colonic mucosa. Dig. Dis. Sci. 59(7), 1529–1534 (2014)CrossRef
Bonnans, C., Chou, J., Werb, Z.: Remodelling the extracellular matrix in development and disease. Nat. Rev. Mol. Cell Biol. 15(12), 786–801 (2014)CrossRef
Brelstaff, G., Bicego, M., Culeddu, N., Chessa, M.: Bag of peaks: interpretation of nmr spectrometry. Bioinformatics 25(2), 258–264 (2009)CrossRef
Breunig, H.G., Weinigel, M., Bückle, R., Kellner-Höfer, M., Lademann, J., Darvin, M.E., Sterry, W., König, K.: Clinical coherent anti-Stokes Raman scattering and multiphoton tomography of human skin with a femtosecond laser and photonic crystal fiber. Laser Phys. Lett. 10(2), 025604 (2013)ADSCrossRef
Caicedo, J.C., Cruz, A., Gonzalez, F.A.: Histopathology image classification using bag of features and kernel functions. In: Artificial Intelligence in Medicine. pp. 126–135. Springer (2009)
Cao, Y., Wang, C., Li, Z., Zhang, L., Zhang, L.: Spatial-bag-of-features. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3352–3359. IEEE (2010)
Chakraborty, S., Nian, F.-S., Tsai, J.-W., Karmenyan, A., Chiou, A.: Quantification of the metabolic state in cell-model of Parkinson’s disease by fluorescence lifetime imaging microscopy. Sci. Rep. 6, 19145 (2016)ADSCrossRef
Chen, X., Nadiarynkh, O., Plotnikov, S., Campagnola, P.J.: Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure. Nat. Protoc. 7(4), 654–669 (2012)CrossRef
Cicchi, R., Sturiale, A., Nesi, G., Kapsokalyvas, D., Alemanno, G., Tonelli, F., Pavone, F.S.: Multiphoton morpho-functional imaging of healthy colon mucosa, adenomatous polyp and adenocarcinoma. Biomed. Opt. Express 4(7), 1204–1213 (2013)CrossRef
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. In: Conference on IEEE Computer Society, pp. 886–893. IEEE (2005)
Evans, C.L., Xu, X., Kesari, S., Xie, X.S., Wong, S.T., Young, G.S.: Chemically-selective imaging of brain structures with CARS microscopy. Opt. Express 15(19), 12076–12087 (2007)ADSCrossRef
Eweiwi, A., Cheema, M.S., Bauckhage, C.: Action recognition in still images by learning spatial interest regions from videos. Pattern Recogn. Lett. 51, 8–15 (2015)CrossRef
Fu, Z., Lu, G., Ting, K.M., Zhang, D.: Music classification via the bag-of-features approach. Pattern Recogn. Lett. 32(14), 1768–1777 (2011)CrossRef
Gauderon, R., Lukins, P., Sheppard, C.: Second-harmonic generation imaging. In: Optics and Lasers in Biomedicine and Culture. pp. 66–69. Springer (2000)
Giacomelli, M.G., Sheikine, Y., Vardeh, H., Connolly, J.L., Fujimoto, J.G.: Rapid imaging of surgical breast excisions using direct temporal sampling two photon fluorescent lifetime imaging. Biomed. Opt. Express 6(11), 4317–4325 (2015)CrossRef
Gulledge, C., Dewhirst, M.: Tumor oxygenation: a matter of supply and demand. Anticancer Res. 16(2), 741–749 (1995)
Hamasha, K., Mohaidat, Q.I., Putnam, R.A., Woodman, R.C., Palchaudhuri, S., Rehse, S.J.: Sensitive and specific discrimination of pathogenic and nonpathogenic Escherichia coli using Raman spectroscopy—a comparison of two multivariate analysis techniques. Biomed. Opt. Express 4(4), 481–489 (2013)CrossRef
Huang, X., Irmak, S., Lu, Y., Pipinos, I., Casale, G., Subbiah, J.: Spontaneous and coherent anti-Stokes Raman spectroscopy of human gastrocnemius muscle biopsies in CH-stretching region for discrimination of peripheral artery disease. Biomed. Opt. Express 6(8), 2766–2777 (2015)CrossRef
Kantelhardt, S.R., Kalasauskas, D., König, K., Kim, E., Weinigel, M., Uchugonova, A., Giese, A.: In vivo multiphoton tomography and fluorescence lifetime imaging of human brain tumor tissue. J. Neuro-Oncol. 127(3), 473–482 (2016)CrossRef
Kong, K., Kendall, C., Stone, N., Notingher, I.: Raman spectroscopy for medical diagnostics—From in vitro biofluid assays to in vivo cancer detection. Adv. Drug Deliv. Rev. 89, 121–134 (2015)CrossRef
Krahmer, N., Farese, R.V., Walther, T.C.: Balancing the fat: lipid droplets and human disease. EMBO Mol. Med. 5(7), 973–983 (2013)CrossRef
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2169–2178. IEEE (2006)
Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: Binary robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555. IEEE (2011)
Li, L., Chen, Z., Wang, X., Liu, X., Jiang, W., Zhuo, S., Guan, G., Chen, J.: Visualization of tumor response to neoadjuvant therapy for rectal carcinoma by nonlinear optical imaging. IEEE J. Sel. Top. Quantum Electron. 22(3), 6800206 (2016)CrossRef
Li L, Li H, Chen Z, Zhuo S, Feng C, Yang Y, Guan G, Chen J Layer-resolved colorectal tissues using nonlinear microscopy. Lasers Med. Sci. 1–9 (2015)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRef
Lu, P., Weaver, V.M., Werb, Z.: The extracellular matrix: a dynamic niche in cancer progression. J. Cell Biol. 196(4), 395–406 (2012)CrossRef
Makino, T., Jain, M., Montrose, D.C., Aggarwal, A., Sterling, J., Bosworth, B.P., Milsom, J.W., Robinson, B.D., Shevchuk, M.M., Kawaguchi, K.: Multiphoton tomographic imaging: a potential optical biopsy tool for detecting gastrointestinal inflammation and neoplasia. Cancer Prev. Res. 5(11), 1280–1290 (2012)CrossRef
Nanni, L., Costa, Y.M., Lumini, A., Kim, M.Y., Baek, S.R.: Combining visual and acoustic features for music genre classification. Expert Syst. Appl. 45, 108–117 (2016)CrossRef
Nanni, L., Melucci, M.: Combination of projectors, standard texture descriptors and bag of features for classifying images. Neurocomputing 173, 1602–1614 (2016)CrossRef
Nordestgaard, B.G., Varbo, A.: Triglycerides and cardiovascular disease. Lancet 384(9943), 626–635 (2014)CrossRef
O’Hara, S., Draper, B.A.: Introduction to the bag of features paradigm for image classification and retrieval. arXiv preprint arXiv:​1101.​3354 (2011)
Pezacki, J.P., Blake, J.A., Danielson, D.C., Kennedy, D.C., Lyn, R.K., Singaravelu, R.: Chemical contrast for imaging living systems: molecular vibrations drive CARS microscopy. Nat. Chem. Biol. 7(3), 137–145 (2011)CrossRef
Raykar, V.C., Yu, S., Zhao, L.H., Jerebko, A., Florin, C., Valadez, G.H., Bogoni, L., Moy, L.: Supervised learning from multiple experts: whom to trust when everyone lies a bit. In: Proceedings of the 26th Annual international conference on machine learning, pp. 889–896. ACM (2009)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2564–2571. IEEE (2011)
Sadek, I., Sidibé, D., Meriaudeau, F.: Automatic discrimination of color retinal images using the bag of words approach. In: SPIE Medical Imaging 2015, pp. 94141 J-94141 J-94148. International Society for Optics and Photonics
Schie, I.W., Krafft, C., Popp, J.: Applications of coherent Raman scattering microscopies to clinical and biological studies. Analyst 140(12), 3897–3909 (2015)CrossRef
So, P.T., Dong, C.Y., Masters, B.R., Berland, K.M.: Two-photon excitation fluorescence microscopy. Annu. Rev. Biomed. Eng. 2(1), 399–429 (2000)CrossRef
Stanciu, S.G., Coltuc, D., Tranca, D.E., Stanciu, G.A.: Matching DSIFT descriptors extracted from CSLM images. Engineering 5(10), 199–202 (2013)CrossRef
Stanciu, S.G., Hristu, R., Boriga, R., Stanciu, G.A.: On the suitability of SIFT technique to deal with image modifications specific to Confocal Scanning Laser Microscopy. Microsc. Microanal. 16(05), 515–530 (2010)ADSCrossRef
Stanciu, S.G., Hristu, R., Stanciu, G.A.: Influence of confocal scanning laser microscopy specific acquisition parameters on the detection and matching of speeded-up robust features. Ultramicroscopy 111(5), 364–374 (2011)CrossRef
Stanciu, S.G., Hristu, R., Tranca, D.E., Stanciu, G.A.: Bags of features for classification of Laser Scanning Microscopy data. In: 2015 17th International Conference on Transparent Optical Networks (ICTON), pp. 1–4. IEEE (2015)
Stanciu, S.G., Xu, S., Peng, Q., Yan, J., Stanciu, G.A., Welsch, R.E., So, P.T., Csucs, G., Yu, H.: Experimenting liver fibrosis diagnostic by two photon excitation microscopy and bag-of-features image classification. Sci. Rep. 4, 4636 (2014)ADSCrossRef
Suhling, K., Hirvonen, L.M., Levitt, J.A., Chung, P.-H., Tregidgo, C., Le Marois, A., Rusakov, D.A., Zheng, K., Ameer-Beg, S., Poland, S.: Fluorescence lifetime imaging (FLIM): basic concepts and some recent developments. Med. Photonics 27, 3–40 (2015)CrossRef
Tamaki, T., Yoshimuta, J., Kawakami, M., Raytchev, B., Kaneda, K., Yoshida, S., Takemura, Y., Onji, K., Miyaki, R., Tanaka, S.: Computer-aided colorectal tumor classification in NBI endoscopy using local features. Med. Image Anal. 17(1), 78–100 (2013)CrossRef
Thomas, G., van Voskuilen, J., Truong, H., Song, J.-Y., Gerritsen, H.C., Sterenborg, H.: In vivo nonlinear spectral imaging as a tool to monitor early spectroscopic and metabolic changes in a murine cutaneous squamous cell carcinoma model. Biomed. Opt. Express 5(12), 4281–4299 (2014)CrossRef
Tokarz, D., Cisek, R., Golaraei, A., Asa, S.L., Barzda, V., Wilson, B.C.: Ultrastructural features of collagen in thyroid carcinoma tissue observed by polarization second harmonic generation microscopy. Biomed. Opt. Express 6(9), 3475–3481 (2015)CrossRef
Uckermann, O., Galli, R., Tamosaityte, S., Leipnitz, E., Geiger, K.D., Schackert, G., Koch, E., Steiner, G., Kirsch, M.: Label-free delineation of brain tumors by coherent anti-stokes Raman scattering microscopy in an orthotopic mouse model and human glioblastoma. PLoS One 9(9), e107115 (2014)ADSCrossRef
Xu, S., Fang, T., Li, D., Wang, S.: Object classification of aerial images with bag-of-visual words. Geosci. Remote Sens. Lett. IEEE 7(2), 366–370 (2010)ADSCrossRef
Zhou, L., Zhou, Z., Hu, D.: Scene classification using a multi-resolution bag-of-features model. Pattern Recogn. 46(1), 424–433 (2013)CrossRef
Metadaten
Titel
Perspectives on combining Nonlinear Laser Scanning Microscopy and Bag-of-Features data classification strategies for automated disease diagnostics
verfasst von
Stefan G. Stanciu
Denis E. Tranca
George A. Stanciu
Radu Hristu
Juan M. Bueno
Publikationsdatum
01.06.2016
Verlag
Springer US
Erschienen in
Optical and Quantum Electronics / Ausgabe 6/2016
Print ISSN: 0306-8919
Elektronische ISSN: 1572-817X
DOI
https://doi.org/10.1007/s11082-016-0589-8

Weitere Artikel der Ausgabe 6/2016

Optical and Quantum Electronics 6/2016 Zur Ausgabe

OriginalPaper

An analysis of W-shaped plastic optical fibers by WKB approximation

OriginalPaper

Counterpropagating optical solitons in -symmetric photonic lattices

OriginalPaper

Stepless tunable four-chip LED lighting control on a black body radiation curve using the generalized reduced gradient method

OriginalPaper

Preparation and characterization of highly transparent and conductive indium-zinc oxide thin films deposited by pyrolysis spray technique

OriginalPaper

Laser irradiation of 10×(Ni/Ti)/Si multilayers at different wavelengths

OriginalPaper

Simulation analysis of energy efficient WDM ethernet passive optical network

Neuer Inhalt

    Bildnachweise