Fast curvilinear structure extraction and delineation using density estimation |
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| Affiliation: | 1. Beijing Institute of Technology, China;2. Department of Electrical and Computer Engineering, University of Alberta, Canada;3. Bioinformatics Institute, A*STAR, Singapore;1. Department of Biomedical Engineering, Hanyang University, Seoul, South Korea;2. USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA 90033, USA;1. Department of Mathematics, Nanjing University of Science and Technology, Nanjing, China;2. Department of Medical Biophysics, University of Toronto, Toronto, Canada;3. Physical Sciences, Sunnybrook Research Institute, Toronto, Canada;4. Department of Mathematics, Nanjing University, Nanjing, China |
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| Abstract: | Detection and delineation of lines is important for many applications. However, most of the existing algorithms have the shortcoming of high computational cost and can not meet the on-board real-time processing requirement. This paper presents a novel method for curvilinear structure extraction and delineation by using kernel-based density estimation. The method is based on efficient calculation of pixel-wise density estimation for an input feature image, which is termed as local weighted features (LWF). For gray and binary images, the LWF can be efficiently calculated by integral image and accumulated image, respectively. Detectors for small objects and centerlines based on LWF are developed and the selection of density estimation kernels is also illustrated. The algorithm is very fast and achieves 50 fps on a PIV2.4G processor. Evaluation results on a number of images and videos are given to demonstrate the satisfactory performances of the proposed method with its high stability and adaptability. |
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