Combining interpolation and 3D level set method (I+3DLSM) for medical image segmentation

Doshi, Trushali; Caterina, Gaetano Di; Soraghan, John J.; Petropoulakis, Lykourgos; Grose, Derek; MacKenzie, Kenneth; Wilson, Christina

doi:10.1049/el.2015.3959

Cited by 5 publications

(1 citation statement)

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“…Image Segmentation via Level set method apply to improve boundary accuracy. The accuracy of segmentation results hinder by inhomogeneities and weak edges[7]. Segmentation with an active contour along with level set method apply for medical image.…”

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confidence: 99%

Visualization of Hidden Structure and Shape in Ct Image via Non-Linear Perspective Foreground and Back Ground Projection

pan¹,

Perinbam²,

Murthy³

et al. 2021

Preprint

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The neurologist analyse the brain images to diagnose the disease via structure and shape of the part in the scanned Medical images such as CT, MRI, and PET.The Medical image segmentation perform less in the regions where no or little contrast,artefacts over the different boundary regions. The manual process of segmentation show poor boundary differentiation dueto discernibility in shape and location, intra and inter observer reliability. In this paper, we propose a dyadic Cat optimization (DCO) algorithm to segment the regions in the brain from CT and MRI image via Non- linear perspective Foreground and Back Ground projection. The DCO algorithm remove the artefacts in the boundary regions and provide the exact structure and shape of the brain regions. The DCO algorithm show the region boundary such as plerygomaxillary fissure, occipital lobe, vaginal process zygomatic arch, maxilla and piriform aperture with high visibility in the regions of inadequately visible boundary and distinguish the deformable shape. The DCO algorithm show the increased SSIM and 90 percent accuracy.

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confidence: 99%