Abstract
Radiologist diagnose the brain disease through shape and boundary regions of brain in medical image such as CT, MRI, and PET. Automatic medical image segmentation and enhancement method perform less in boundary regions due to artefacts such as dense objects and slice overlap. Manual enhancement and segmentation method never differentiates the shape and location of regions in brain CT/MRI images. Dyadic cat optimization (DCO) algorithm is proposed for segmenting brain regions in medical images such as CT and MRI through Nonlinear perspective Foreground and Background projection. DCO algorithm eliminates the artefacts in the boundary regions of brain and enhances the boundaries and shape such as pterygomaxillary fissure, occipital lobe, vaginal process, zygomatic arch, maxilla and piriform aperture for more visibility. Proposed DCO algorithm enhances the occipital lobe and zygomatic arch regions in CT/MRI image. The occipital lobe and zygomatic arch regions are better enhanced and segmented with DCO algorithm than traditional algorithm and achieve an accuracy of 90% through structural similarity index and visual interpretation.
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The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
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Partheepan, R., Perinbam, J.R.P., Krishnamurthy, M. et al. Visualization of occipital lobe and zygomatic arch of brain region through non-linear perspective projection using DCO algorithm. Soft Comput 26, 11599–11610 (2022). https://doi.org/10.1007/s00500-022-07427-8
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DOI: https://doi.org/10.1007/s00500-022-07427-8