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Multi-region segmentation by a single level set generalization applied to stroke CT images

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Abstract

This article proposes a level set approach to segment images with N regions by using a single level set function. Many works use several level set fronts or hierarchical approach to solve this problem. We propose a general formulation of the level set propagation function based on the knowledge for two regions. A modified likelihood function is proposed, and each background probability is maximized. A single propagation function is achieved from N logarithmic components. Experiments are performed on synthetic images with normal probability and computed tomography images of patients with hemorrhagic stroke. Our approach is compared with other ones known in the literature, and the level set was superior in 10 metrics out of 13 evaluated, with an accuracy of 99.67% and FSIM 93.96%. These results confirm the effectiveness of the proposed method.

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Authors and Affiliations

  1. DTEL/PPGET, IFCE - Federal Institute of Education, Science and Technology, Fortaleza, CE, Brazil

    C. Monte & R. C. P. Marques

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  1. C. Monte
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Monte, C., Marques, R.C.P. Multi-region segmentation by a single level set generalization applied to stroke CT images. SIViP 15, 1203–1210 (2021). https://doi.org/10.1007/s11760-020-01850-w

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  • DOI: https://doi.org/10.1007/s11760-020-01850-w

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