Skip to main content
Log in

Ridge–based curvilinear structure detection for identifying road in remote sensing image and backbone in neuron dendrite image

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The curvilinear structure detection is widely applied in many real tasks, such as the fiber classification, river finding, blood vessel detection, and so on. In this paper, we proposed to use the ridge-based curvilinear structure detection (RCSD) for the road extraction from the remote sensing images. First, we employed the morphology trivial opening operation to filter out almost all the small clusters of noise and the small paths. Then RCSD was used to find the road from the remote sensing images. The experiments showed that our proposed method is efficient and give better results than the current existing road-detection methods. Considering the similar structure between backbone in the neuron dendrite images and the road in remote sensing images, we extended the application of RCSD to the backbone detection in neuron dendrite images. The results on backbone detection also proved the efficiency of RCSD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Álvarez JM et al (2014) Combining priors, appearance, and context for road detection. IEEE Transaction on intelligent transportation systems 15(3):1168–1178

    Article  Google Scholar 

  2. Atangana A (2018) Application of stationary wavelet entropy in pathological brain detection. Multimedia Tools and Applications 77(3):3701–3714

    Article  Google Scholar 

  3. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence 39(12):2481–2495

    Article  Google Scholar 

  4. Barsi A, Heipke C (2008) Artifical neural networks for the detection of road junctions in aerial images. Geol Mag 70(2):180–182

    Google Scholar 

  5. Byun J, Seo B-S, Lee J (2015) Toward accurate road detection in challenging environments using 3D point clouds. ETRI J 37(3):606–616

    Article  Google Scholar 

  6. Chen M (2016) Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine. PeerJ, 4, Article ID. e2207

  7. Chen Y, Chen X-Q (2016) Sensorineural hearing loss detection via discrete wavelet transform and principal component analysis combined with generalized eigenvalue proximal support vector machine and Tikhonov regularization. Multimedia Tools and Applications 77(3):3775–3793

    Article  Google Scholar 

  8. Chen Y, Lu H (2018) Wavelet energy entropy and linear regression classifier for detecting abnormal breasts. Multimedia Tools and Applications 77(3):3813–3832

    Article  Google Scholar 

  9. Chen M, Li Y, Han L (2015) Detection of dendritic spines using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks. Computational and Mathematical Methods in Medicine: Article ID. 454076

  10. Cheng J, Zhou X, Miller E, Witt RM, Zhu J, Sabatini BL, Wong STC (2007) A novel computational approach for automatic dendrite spines detection in two-photon laser scan microscopy. J Neurosci Methods 165(1):122–134

    Article  Google Scholar 

  11. Dong D, Mcavoy TJ (1994) Nonlinear principal components analysis--based on principal curves and neural networks. Computers & Chemical Engineering 20(1):65–78

    Article  Google Scholar 

  12. Du S (2016) Multi-objective path finding in stochastic networks using a biogeography-based optimization method. SIMULATION 92(7):637–647

    Article  Google Scholar 

  13. Einbeck J, Dwyer J (2011) Using principal curves to analyse traffic patterns on freeways. Transportmetrica 7(3):229–246

    Article  Google Scholar 

  14. Fan J, Zhou X, Dy JG, Zhang Y, Wong STC (2009) An automated pipeline for dendrite spine detection and tracking of 3D optical microscopy neuron images of in vivo mouse models. Neuroinformatics 7(2):113–130

    Article  Google Scholar 

  15. Fan J et al. (2017) An automatic method for spine detection and spine tracking in in vivo images. in IEEE/Nih Life Science Systems and Applications Workshop. Bethesda: IEEE. p. 233−+

  16. Guo C, Mita S, McAllester D (2012) Robust road detection and tracking in challenging scenarios based on Markov random fields with unsupervised learning. IEEE Transaction on intelligent transportation systems 13(3):1338–1354

    Article  Google Scholar 

  17. Jia W (2017) Three-Category Classification of Magnetic Resonance Hearing Loss Images Based on Deep Autoencoder. Journal of Medical Systems 41(10):Article ID. 165

    Article  Google Scholar 

  18. Jiang YY (2017) Cerebral micro-bleed detection based on the convolution neural network with rank based average pooling. IEEE Access 5:16576–16583

    Article  Google Scholar 

  19. Koh IY et al (2002) An image analysis algorithm for dendritic spines. Neural Comput 14(6):1283–1310

    Article  MATH  Google Scholar 

  20. Kong H, Audibert J-Y, Ponce J (2010) General road detection from a single image. IEEE Transaction on intelligent transportation systems 19(8):2211–2220

    MathSciNet  MATH  Google Scholar 

  21. Li Y, Cattani C (2017) Detection of dendritic spines using wavelet packet entropy and fuzzy support vector machine. CNS Neurol Disord Drug Targets 16(2):116–121

    Article  Google Scholar 

  22. Li X, Zhang S, Pan X, Dale P, Cropp R (2010) Straight road edge detection from high-resolution remote sensing images based on the ridgelet transform with the revised parallel-beam radon transform. Int J Remote Sens 31(19):5041–5059

    Article  Google Scholar 

  23. Li Y, Ding W, Zhang XG, Ju Z (2016) Road detection algorithm for autonomous navigation systems based on dark channel prior and vanishing point in complex road scenes. Robot Auton Syst 85(Supplement C):1–11

    Google Scholar 

  24. Liu AJ (2017) Tea category identification using computer vision and generalized eigenvalue proximal SVM. Fundamenta Informaticae 151(1–4):325–339

    MathSciNet  Google Scholar 

  25. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation, in IEEE Conference on Computer Vision and Pattern Recognition. p. 3431–3440

  26. Lu S, Lu Z (2018) A pathological brain detection system based on kernel based ELM. Multimedia Tools and Applications 77(3):3715–3728

    Article  Google Scholar 

  27. Martínez Z, Ludeña C (2011) An algorithm for automatic curve detection. Computational Statistics & Data Analysis 55(6):2158–2171

    Article  MathSciNet  MATH  Google Scholar 

  28. Meijering E, Jacob M, Sarria JCF, Steiner P, Hirling H, Unser M (2004) Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images. Cytometry Part A 58A(2):167–176

    Article  Google Scholar 

  29. Ozertem U, Erdogmus D (2011) Locally defined principal curves and surfaces. J Mach Learn Res 12(4):1249–1286

    MathSciNet  MATH  Google Scholar 

  30. Pan H, Zhang C, Tian Y (2014) RGB-D image-based detection of stairs, pedestrian crosswalks and traffic signs. J Vis Commun Image Represent 25(2):263–272

    Article  Google Scholar 

  31. Pulkkinen S (2015) Ridge-based method for finding curvilinear structures from noisy data. Computational Statistics & Data Analysis 82:89–109

    Article  MathSciNet  Google Scholar 

  32. Shi Q, Liu X, Li X (2017) Road detection from remote sensing images by generative adversarial networks. IEEE access, 2017. PP, DOI: 10.1109/ACCESS.2017.2773142

  33. Shih FY, Kowalski AJ (2003) Automatic extraction of filaments in Hα solar images. Sol Phys 218(1–2):99–122

    Article  Google Scholar 

  34. Silva G, Martins C, Moreira da Silva N, Vieira D, Costa D, Rego R, Fonseca J, Silva Cunha JP (2017) Automated volumetry of hippocampus is useful to confirm unilateral mesial temporal sclerosis in patients with radiologically positive findings. Neuroradiol J 30(4):318–323

    Article  Google Scholar 

  35. Stanford DC, Raftery AE (2000) Finding curvilinear features in spatial point patterns: principal curve clustering with noise. IEEE Transactions on Pattern Analysis & Machine Intelligence 22(6):601–609

    Article  Google Scholar 

  36. Su J, Srivastava A, Huffer FW (2013) Detection, classification and estimation of individual shapes in 2D and 3D point clouds: Elsevier Science Publishers B V. 227–241

  37. Trevor Hastie WS (1989) Principal Curves. J Am Stat Assoc 84(406):502–516

    Article  MathSciNet  MATH  Google Scholar 

  38. Wei L, Yang J (2016) Fitness-scaling adaptive genetic algorithm with local search for solving the multiple depot vehicle routing problem. SIMULATION 92(7):601–616

    Article  Google Scholar 

  39. Wu X (2018) Tea category identification based on optimal wavelet entropy and weighted k-nearest neighbors algorithm. Multimedia Tools and Applications 77(3):3745–3759

    Article  Google Scholar 

  40. Xu XY et al. (2006) A shape analysis method to detect dendritic spine in 3D optical microscopy image. in 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano. Arlington: IEEE. p. 554–559

  41. Zhan TM, Chen Y (2016) Multiple sclerosis detection based on biorthogonal wavelet transform, RBF kernel principal component analysis, and logistic regression. IEEE Access 4:7567–7576

    Article  MathSciNet  Google Scholar 

  42. Zhang Y, Zhou X, Witt RM, Sabatini BL, Adjeroh D, Wong STC (2007) Dendritic spine detection using curvilinear structure detector and LDA classifier. NeuroImage 36(2):346–360

    Article  Google Scholar 

  43. Zhou H, Kong H, Wei L, Creighton D, Nahavandi S (2015) Efficient road detection and tracking for unmanned aerial vehicle. IEEE Trans Intell Transp Syst 16(1):297–309

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (61401200 & 61602250), Open Fund of Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology (17-259-05-011 K), Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL1601, HGAMTL-1703), National key research and development plan (2017YFB1103202) and Henan Key Research and Development Project (182102310629).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Fanqiang Kong, Vishnu Varthanan Govindaraj or Yu-Dong Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kong, F., Govindaraj, V.V. & Zhang, YD. Ridge–based curvilinear structure detection for identifying road in remote sensing image and backbone in neuron dendrite image. Multimed Tools Appl 77, 22857–22873 (2018). https://doi.org/10.1007/s11042-018-5976-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-5976-7

Keywords

Navigation