Ridge–based curvilinear structure detection for identifying road in remote sensing image and backbone in neuron dendrite image
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.
KeywordsRidge-based curvilinear structure detection Road detection Remote sensing Backbone detection Neuron dendrite
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).
- 4.Barsi A, Heipke C (2008) Artifical neural networks for the detection of road junctions in aerial images. Geol Mag 70(2):180–182Google 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. e2207Google 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. 454076Google 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−+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):1–11Google 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. 3431–3440Google 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.2017.2773142Google 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. 227–241Google 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. 554–559Google Scholar