An Image Segmentation Model Using a Level Set Method Based on Improved Signed Pressure Force Function SPF

Larbi, Messaouda; Messali, Zoubeida; Abdelghani, Rouini; Larbi, Samira

doi:10.1007/978-981-15-6403-1_87

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Messaouda et al [21] present a novel level set method driven by new signed pressure force function (SPF) for image segmentation. In this paper a novel method is proposed to simultaneously solve a two-dimensional partial differential equations (PDE) system, make a compromise between image restorations and keep edges and correct segmentation.…”

Section: Level Set Formulation With Distance Regularizedmentioning

confidence: 99%

Adaptive segmentation algorithm based on level set model in medical imaging

Mansouri¹,

Khobzaoui²,

Damou³

et al. 2023

TELKOMNIKA

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For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems' two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler's equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.

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Section: Level Set Formulation With Distance Regularizedmentioning

confidence: 99%