Active Contour Model for Vector-Valued Intensity Inhomogeneity Images Corrupted by a Mixture of Additive Gaussian and Multiplicative Gamma Noise
DOI:
https://doi.org/10.32890/jict2026.25.3.4Keywords:
Active contour model, additive Gaussian multiplicative Gamma noise, image segmentation, intensity inhomogeneityAbstract
Most of the image processing research has demonstrated the exceptional suitability of Active Contour Models (ACMs) concerning image segmentation models in image analysis as well as computer vision tasks. However, noise and intensity inhomogeneity in the vector-valued or colour images may compromise image quality and impact the results of segmentation. Most often, images are disrupted by mixed noise. To date, no existing research has addressed image segmentation under a combination of two noise types: Additive Gaussian and Multiplicative Gamma (AGMG). Thus, to fill this gap, this research proposes a variational ACM for segmenting vector-valued intensity inhomogeneity images distracted by mixed AGMG noise. Correspondingly, we developed our model by integrating dual denoising terms to reduce mixed noise and the term of optimized Laplacian of Gaussian (LoG) to recover homogeneous regions. As such, the solution to the suggested model involves minimization techniques, which are the Calculus of Variations as well as the finite difference approach. Concurrently, the models’ performance, including the proposed as well as comparative models, will be assessed under low, moderate, severe, and very severe noise. As a result, the average values of the suggested framework, namely the Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (JSC), as well as accuracy metric, were 3.29%, 1.796%, and 1.374% higher, respectively, than those of the compared models. Overall, this highlights the accuracy of our suggested model in addressing colour intensity inhomogeneity images with mixed AGMG noise for segmentation.
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