We propose a color image segmentation approach based on rough set theory elements. Main con- tributions of the proposed approach are twofold. First, by using an adaptive threshold selection, the approach is automatically adjustable according to the image content. Second, a region-merging process, which takes into account both features and spatial relations of the resulting segments, lets us minimize over-segmentation issues. These two proposals allow our method to overcome some performance issues shown by previous rough set theory-based approaches. In addition, a study to determine the best suited color representation for our segmentation approach is carried out, determining that the best results are obtained using a perceptually uniform color space. A set of qualitative and quantitative tests over a comprehensive image database shows that the proposed method produces high-quality segmentation outcomes, better than those obtained using the pre- vious rough set theory-based and standard segmentation approaches. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JEI.23.1.013024)
['Rocio A. Lizarraga-Morales', 'Raúl Enrique Sánchez-Yáñez', 'Victor Ayala-Ramirez', 'Alberto J. Patlan-Rosales']
Improving a rough set theory-based segmentation approach using adaptable threshold selection and perceptual color spaces