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Kadir Jumaat, Abdul
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Partitioning hazy images using interactive active contour models Ahmad Khairul Anuar, Firhan Azri; Jone, Jenevy; Aiesya Raja Azhar, Raja Farhatul; Kadir Jumaat, Abdul
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1317-1324

Abstract

Image partitioning, also known as image segmentation, is a process that involves dividing an image into distinct and meaningful segments. Recently, an interactive active contour model (ACM) namely the Gaussian regularization selective segmentation (GRSS) was designed to handle images with intensity inhomogeneity effectively. However, the GRSS model shows limited performance when applied to hazy images, which often results in incomplete detection and inaccurate extraction of the target object. This study reformulates the GRSS model by integrating the simple dark channel prior (SimpleDCP) dehazing technique, producing a modified model referred to as GRSS with SimpleDCP (GRSSD). The model is derived and implemented in MATLAB software. Experimental results show that the GRSSD model achieves improved segmentation accuracy (ACU) compared with the GRSS model. On average, the ACU improved by 1.8%, while the error metric (EM) decreased from 0.053 to 0.036, representing a reduction of about 32%. The Dice and Jaccard indices improved by approximately 2.6% and 4.9%, respectively. Although the computation time increased, the enhancement in segmentation ACU demonstrates the benefit of incorporating a dehazing process into the variational formulation. The proposed GRSSD model can be extended to color and three-dimensional image segmentation in future work.
Variational selective segmentation model for intensity inhomogeneous image Christy Saibin, Tammie; Kadir Jumaat, Abdul
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.
Co-Authors Ahmad Khairul Anuar, Firhan Azri Aiesya Raja Azhar, Raja Farhatul Christy Saibin, Tammie Jone, Jenevy