Abstract
An improved local morphology fitting active contour model with weighted data term is proposed in this paper for automated segmentation of the vascular tree on 2-D angiogram. In the original local morphology fitting model, morphological fuzzy minimum and maximum opening are adopted to approach the background and vessel object, separately. The structuring elements used in the morphology operator are linear ones, and their scale and orientation are computed from the image. The energy of the active contour model is minimized through a level set framework. This model is robust against both the inhomogeneous background and the initial contour location. However, the same weight coefficient is adopted for object structure. It leads to that bigger vessel structure that will dominate the contour evolution. In this paper, a normalized weight is added to the data term of the local morphology fitting to balance the data energy and encourage the segmentation of small vessel structure. The results on angiogram compared with the original local morphology fitting method are presented.
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Wang, X., Sun, K. (2020). Improved Local Morphology Fitting Active Contour with Weighted Data Term for Vessel Segmentation. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I. (eds) Recent Trends in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-9406-5_8
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DOI: https://doi.org/10.1007/978-981-13-9406-5_8
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