Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Cognitive Computation
Published in: Cognitive Computation 2/2016

01-04-2016

Novel Approach Using Echo State Networks for Microscopic Cellular Image Segmentation

Authors: Boudjelal Meftah, Olivier Lézoray, Abdelkader Benyettou

Published in: Cognitive Computation | Issue 2/2016

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper concentrates on the use of Echo State Networks (ESNs), an effective form of reservoir computing, to improve microscopic cellular image segmentation. An ESN is a sparsely connected recurrent neural network in which most of the weights are fixed a priori to randomly chosen values. The only trainable weights are those of links connected to the outputs. The process of segmentation is conducted via two approaches: the basic form, which uses one reservoir, and our approach, which corresponds to using multiple reservoirs. Experimental results confirm the benefits of the second approach, which outperforms all state-of-the-art methods considered in this paper for the problem of microscopic image segmentation.
previous article A Hybrid Vigilance Monitoring Study for Mental Fatigue and Its Neural Activities
next article Cognitive Computation of Compressed Sensing for Watermark Signal Measurement

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
Footnotes
Literature
1.
2.
Khalbuss WE, Pantanowitz L, Parwani AV. Digital imaging in cytopathology. Pathology Res Int. 2011; 264683
3.
Wu P, Yi J, Zhao G, Huang Z, Qiu B, Gao D. Active contour-based cell segmentation during freezing and its application in cryopreservation. Trans IEEE Biomed Eng. 2015;62(1):284.CrossRef
4.
Zeng Z, Strange H, Han C, Zwiggelaar R. In: Image analysis and recognition—10th international conference, ICIAR 2013, Póvoa do Varzim, Portugal, June 26–28, 2013. Proceedings 2013; p. 605–612
5.
Su CYCM-C, Wang PC. A neural-network-based approach to white blood cell classification. Sci World J. 2014; 796371
6.
Meftah B, Lézoray O, Chaturvedi S, Khurshid A, Benyettou A. Artificial intelligence, evolutionary computing and metaheuristics, studies. In: Yang XS, editor. Computational intelligence, vol. 427. Berlin: Springer; 2013. p. 525–44.
7.
Chourasiya S, Rani G. Automatic red blood cell counting using watershed segmentation. Int J Comput Sci Inf Technol. 2014;5(4):4834.
8.
Takemoto S, Yoshizawa S, Tsujimura Y, Yokota H. In: Computing and networking (CANDAR), 2013 first international symposium on 2013; p. 294–299
9.
Lukoševičius M, Jaeger H. Reservoir computing approaches to recurrent neural network training. Comput Sci Rev. 2009;3(3):1574.
10.
Strauß T, Wustlich W, Labahn R. Design strategies for weight matrices of echo state networks. Neural Comput. 2012;24(12):3246.CrossRefPubMed
11.
Koprinkova-Hristova P, Alexiev K. In: Artificial neural networks and machine learning - ICANN 2013. Lecture notes in computer science, vol. 8131. Berlin: Springer; 2013.
12.
Woodward A, Ikegami T. In: 26th international conferences on image and vision computing 2011; p. 543–548
13.
Koprinkova-Hristova P, Angelova D, Borisova D, Jelev G. In: Innovations in intelligent systems and applications (INISTA), 2013 IEEE international symposium on 2013; p. 1–5
14.
Suganthi D, Purushothaman S. FMRI segmentation using echo state neural network. Int J Image Process. 2008;2(1):1.CrossRef
15.
Kainz P, Mayrhofer-Reinhartshuber M, Burgsteiner H, Asslaber M, Ahammer H. In: 48th annual conference of the German society for biomedical engineering; 2014
16.
Malik Z, Hussain A, Wu J. Novel biologically inspired approaches to extracting online information from temporal data. Cogn Comput. 2014;6(3):595.CrossRef
17.
Mitul A, Rabin M, Rakeeb M, Khan AAM, Rana G, Mollah A, Rahman M. In: Informatics, electronics vision (ICIEV), 2013 international conference on 2013; p. 1–6
18.
Bishop C. Pattern recognition and machine learning. Information science and statistics (Springer, 2007)
19.
Cai Q., He H., Man H. In: The 2011 international joint conference on neural networks, IJCNN 2011, San Jose, California, USA, 2011; p. 2313–2320
20.
Goudarzi A, Banda P, Lakin M, Teuscher C, Stefanovic D. CoRR abs/1401.2224 (2014)
21.
22.
Lovlid R. A novel method for training an echo state network with feedback-error learning. Adv Artif Intell. 2013;2013(2514027):9:9.
23.
Goudarzi A, Stefanovic D. Towards a calculus of echo state networks. Procedia Comput Sci. 2014;41:176.CrossRef
24.
Jaeger H. A tutorial on training recurrent neural networks, covering bppt, rtrl, ekf and the echo state network approach. Technical Report 159, German National Research Center for Information Technology (2002)
25.
Meurie C, Lezoray O, Charrier C, Elmoataz A. Combination of multiple pixel classifiers for microscopic image segmentation. Int J Robot Autom. 2005;20(2):63.
26.
Fuchs T, Buhmann J. Computational pathology: challenges and promises for tissue analysis. Comput Med Imaging Graph. 2011;35(7–8):515.CrossRefPubMed
27.
Dumont M, Marée R, Wehenkel L, Geurts P. In VISAPP 2009—proceedings of the fourth international conference on computer vision theory and applications, Lisboa, Portugal, February 5–8, 2009—vol 2, 2009; p. 196–203
28.
Song Y, Cai W, Feng D. In: Digital image computing techniques and applications (DICTA), 2012 international conference on 2012; p. 1–6
29.
Huang GB. An insight into extreme learning machines: random neurons, random features and kernels. Cogn Comput. 2014;6(3):376.CrossRef
30.
Lezoray O, Cardot H. Cooperation of color pixel classification schemes and color watershed: a study for microscopic images. IEEE Trans Image Process. 2002;11(7):783.CrossRefPubMed
31.
Jaeger H. In: Neural networks, 2005. IJCNN ’05. Proceedings. 2005 IEEE international joint conference on, 2005; vol 3, p. 1460–1462
32.
Alexandre L, Embrechts M. In: Artificial neural networks—ICANN 2009, lecture notes in computer science, vol. 5768. Berlin: Springer; 2009. p. 1015–1024
33.
Yuanbiao W, Ni J, Zhiping X. In: Internet computing for science and engineering (ICICSE), 2009 fourth international conference on 2009; p. 102–108
34.
Venayagamoorthy GK, Shishir B. Effects of spectral radius and settling time in the performance of echo state networks. Neural Networks. 2009;22(7):861.CrossRefPubMed
35.
Koryakin D, Butz M. In: Artificial neural networks and machine learning—ICANN 2012, lecture notes in computer science, vol. 7552. Berlin: Springer; 2012. p. 499–506.
36.
Rodan A, Tino P. Minimum complexity echo state network. Neural Netw IEEE Trans. 2011;22(1):131.CrossRef
37.
Triefenbach F, Jalalvand A, Schrauwen B, Martens JP. In: Advances in neural information processing systems, 2010; vol. 23, p. 9
Metadata
Title
Novel Approach Using Echo State Networks for Microscopic Cellular Image Segmentation
Authors
Boudjelal Meftah
Olivier Lézoray
Abdelkader Benyettou
Publication date
01-04-2016
Publisher
Springer US
Published in
Cognitive Computation / Issue 2/2016
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-015-9354-8

Other articles of this Issue 2/2016

Cognitive Computation 2/2016 Go to the issue

OriginalPaper

Evolutionary Multitasking: A Computer Science View of Cognitive Multitasking

OriginalPaper

An Interactive Fuzzy Inference System for Teletherapy of Older People

OriginalPaper

Software Architecture for Smart Emotion Recognition and Regulation of the Ageing Adult

OriginalPaper

A Novel Hybrid Text Summarization System for Punjabi Text

OriginalPaper

Combining Expression Data and Knowledge Ontology for Gene Clustering and Network Reconstruction

OriginalPaper

A Novel Switching Delayed PSO Algorithm for Estimating Unknown Parameters of Lateral Flow Immunoassay

Premium Partner

    Image Credits