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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Abdul Kadir Jumaat
Universiti Teknologi MARA
Electrical & Electronics Engineering

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Variational selective segmentation model for intensity inhomogeneous image Tammie Christy Saibin; Abdul Kadir Jumaat
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp277-285

Abstract

Variational selective image segmentation models aim to extract a particular object in an image depending on a set of user-defined prior points. The current model suffers from high computational costs due to the traditional total variation function that results in a slow segmenting process. In addition, it is not designed to segment images with intensity inhomogeneities. In this research, we formulate a new variational selective image segmentation model based on the Gaussian function. A Gaussian function is proposed to replace the traditional total variation function to regularize the variational level set function. To segment images with intensity inhomogeneities, the local image fitting idea was in corporate into the formulation. The efficiency of the proposed model was then assessed by recording the computation time while the accuracy was measured using Jaccard and Dice similarity values. Numerical experiments using synthetic, natural, and medical images demonstrate that the proposed model is about 6 times faster than the existing model, while the Jaccard and Dice values are about 11% and 7% higher, respectively, compared to the existing model. In the future, this research can be extended further into a 3-dimensional modeling and vector-valued image framework.
ACM for segmenting intensity inhomogeneity images corrupted by a mixture of AGMG noise Nurhuda Ismail; Abdul Kadir Jumaat; Rizauddin Saian; Akmal Shafiq Badarul Azam
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.12549

Abstract

In practice, active contour models (ACMs) are effective in segmenting images, which are mostly applied in computer vision and medical imaging. However, the result may be challenged when the images contain noise and intensity inhomogeneity. Images are commonly deteriorated by a mixture of noise involving additive Gaussian and multiplicative Gamma (AGMG). To the best of our knowledge, this is the first study to formulate an ACM for segmenting intensity inhomogeneity images corrupted by AGMG noise. Our proposed mathematical model was developed using a combination of dual denoising terms to recover the noisy image and Laplacian of Gaussian (LoG) terms to smooth the targeted object. The model is solved using calculus of variations and finite difference methods. The performance of the suggested model for segmenting images was evaluated in MATLAB software and compared with current models using low and severe AGMG noise levels. Quantitative evaluations demonstrated that the proposed model outperformed existing models, achieving an average Jaccard similarity coefficient (JSC) improvement of roughly 2.5% and a 16.00% reduction in average relative error. Consequently, the proposed model proves significantly more effective for the segmentation of images degraded by intensity inhomogeneity and mixed AGMG noise.
Co-Authors Akmal Shafiq Badarul Azam Nurhuda Ismail Rizauddin Saian Tammie Christy Saibin