Detecting curvilinear structure using ridge distribution feature and layer growth method

Ge Gao; Guoyou Wang; Yu Shi

doi:10.1117/12.2032748

26 October 2013 Detecting curvilinear structure using ridge distribution feature and layer growth method

Ge Gao, Guoyou Wang, Yu Shi

Proceedings Volume 8918, MIPPR 2013: Automatic Target Recognition and Navigation; 891812 (2013) https://doi.org/10.1117/12.2032748
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

Abstract

Wide line detection plays an important role in image analysis and computer vision. However, most of the existing algorithms focus on the extraction of the line positions and length, ignoring line thickness and direction which can deepen our understanding of images. This paper presents a novel wide line detector using the ridge distribution feature and layer growth method. Unlike most existing edge and line detectors which use directional derivatives, our proposed method extracts the ridge target point and use the layer growth to find the line completely based on the isotropic nonlinear filter. Ridge points are detected by its distribution symmetry based on the isotropic responses via circular masks and orientation of the ridge is determined roughly. The ridge point is selected as a seed point, then growth layer by layer, to determine the width and orientation of the curvilinear structure accurately. Instead of point by point scanning, we label points in the growth region and adjust the scanning step adaptively which improve the method efficiently. The proposed method can detect the accurate width and direction of lines dynamically. This can provide great convenience for post-processing or for application requirements. A sequence of tests on a variety of image samples demonstrates that the proposed method outperforms state-of-the-art methods.

Citation Download Citation

Ge Gao, Guoyou Wang, and Yu Shi "Detecting curvilinear structure using ridge distribution feature and layer growth method", Proc. SPIE 8918, MIPPR 2013: Automatic Target Recognition and Navigation, 891812 (26 October 2013); https://doi.org/10.1117/12.2032748

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KEYWORDS

Target detection

Ridge detection

Detection and tracking algorithms

Nonlinear filtering

Sensors

Computer vision technology

Image analysis

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