The performance of some implicit region-based active contours in segmenting and restoring welding radiographic images

Boutiche, Yamina; Halimi, M.

doi:10.1134/s1061830917100035

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…The above discussed shortcomings in the efficient use of EBM have limited their applicability in the field of image segmentation and lead to the use of RBMs. Owing to the less sensitivity towards noise in the images [5] and no data required about image gradient, such models successfully allow the segmentation of objects with weak or no edges [15]. The significant features of RBMs can also automatically detect the interior contours and shows less dependency on the location of initial contour [15].…”

Section: Introductionmentioning

confidence: 99%

New Local Region Based Model for the Segmentation of Medical Images

et al. 2020

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Segmentation of images having inhomogeneous intensities is always challenging. In this paper, we propose a model based on new local data statistics using local means and variances for detection of region of interest in medical images suffered from intensity inhomogeneity. This is done by introducing a new probability density function based on coefficient of variation, which is a best measure for inhomogeneous data. The new energy functional in the proposed model is then expressed in terms of level set function and is minimized for optimal energy. Minimization of the energy will lead to a partial differential equation, which is solved by using well known explicit method. Results of the proposed model are compared with other state of the art models and found that the proposed model outperform other existing models. Comparison is given in both qualitative and quantitative way. Furthermore, the proposed model is tested on different type of medical images like MRI, CT, Mammogram and skin lesion etc. INDEX TERMS Gaussian processes, image segmentation, intensity inhomogeneity, level set method, variational techniques.

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Section: Introductionmentioning

confidence: 99%