Semi-automatic integrated segmentation approaches and contour extraction applied to computed tomography scan images
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Updated: Jun 28, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
B Dhalila S Y Khoodoruth1, Harry C S Rughooputh, Wilfrid Lefer
1Department of Computer Science, University of Pau and Pays de l'Adour, 64012 PauCedex, France. dhalila.khoodoruth@univ-pau.fr
Abstract:
We propose to segment two-dimensional CT scans traumatic brain injuries with various methods. These methods are hybrid, feature extraction, level sets, region growing, and watershed which are analysed based upon their parametric and nonparametric arguments. The pixel intensities, gradient magnitude, affinity map, and catchment basins of these methods are validated based upon various constraints evaluations. In this article, we also develop a new methodology for a computational pipeline that uses bilateral filtering, diffusion properties, watershed, and filtering with mathematical morphology operators for the contour extraction of the lesion in the feature available based mainly on the gradient function. The evaluations of the classification of these lesions are very briefly outlined in this context and are being undertaken by pattern recognition in another paper work.
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