2023
DOI: 10.1142/s021951942340002x
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Segmentation of Mri Images Using a Combination of Active Contour Modeling and Morphological Processing

Abstract: Image segmentation in brain magnetic resonance imaging (MRI) largely relates to dividing brain tissue into components like white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Using the segmentation outputs, medical images can be 3D reconstructed and visualized efficiently. It is common for MRI pictures to have issues such as partial volume effects, asymmetrical grayscale, and noise. As a result, high accuracy in brain MRI picture segmentation is challenging to achieve in practical applications.… Show more

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Cited by 3 publications
(3 citation statements)
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“…where the external energy function, E external (τ(s)) can be defined as -γ|∇I(τ (s)) |.ds and ∇I represents the gradient of the image I at the location [x(s),y(s)] of the contour, τ (12) . c) Contour Refinement process using Boltzmann Monte Carlo method:…”
Section: B) Active Contour Evolution (Snake Model To Delineate the Tumormentioning
confidence: 99%
See 2 more Smart Citations
“…where the external energy function, E external (τ(s)) can be defined as -γ|∇I(τ (s)) |.ds and ∇I represents the gradient of the image I at the location [x(s),y(s)] of the contour, τ (12) . c) Contour Refinement process using Boltzmann Monte Carlo method:…”
Section: B) Active Contour Evolution (Snake Model To Delineate the Tumormentioning
confidence: 99%
“…Also, an overall accuracy of 91% is achieved for 3064 MRI images comprising 1426 Glioma, 708 Meningioma and 930 Pituitary type tumors. Santhosh Kumar et al (12) have proposed a unique approach for segmenting brain MRI images by combining nonlinear filtering, k-means clustering, active contour modelling, thresholding, and morphological post-processing. It has achieved an Accuracy of 97.38%, Sensitivity of 82.01%, Precision of 97.20%, Dice Co-efficient of 85.82%.…”
Section: Introductionmentioning
confidence: 99%
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