Image segmentation using Atanassov's intuitionistic fuzzy sets

Authors:
Pedro Melo-Pinto;Pedro Couto;Humberto Bustince;Edurne Barrenechea;Miguel Pagola;Javier Fernandez
Affiliations:
CITAB - Centre for the Research and Technology of, Agro-Environmental and Biological Sciences, UTAD University, Quinta de Prados, 5001-801 Vila Real, Portugal;CITAB - Centre for the Research and Technology of, Agro-Environmental and Biological Sciences, UTAD University, Quinta de Prados, 5001-801 Vila Real, Portugal;UPNA - Universidad Pública de Navarra, Departamento de Automática y Computación, Campus Arrosadia s/n, 31006 Pamplona, Spain;UPNA - Universidad Pública de Navarra, Departamento de Automática y Computación, Campus Arrosadia s/n, 31006 Pamplona, Spain;UPNA - Universidad Pública de Navarra, Departamento de Automática y Computación, Campus Arrosadia s/n, 31006 Pamplona, Spain;UPNA - Universidad Pública de Navarra, Departamento de Automática y Computación, Campus Arrosadia s/n, 31006 Pamplona, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

The problem of segmentation in spite of all the work over the last decades, is still an important research field and also a critical preprocessing step for image processing, mostly due to the fact that finding a global optimal threshold that works well for all kind of images is indeed a very difficult task that, probably, will never be accomplished. During the past years, fuzzy logic theory has been successfully applied to image thresholding. In this paper we describe a thresholding technique using Atanassov's intuitionistic fuzzy sets (A-IFSs). This approach uses Atanassov's intuitionistic index values for representing the hesitance of the expert in determining whether the pixel belongs to the background or that it belongs to the object. First, we describe the general framework of this approach to bi-level thresholding. Then we present its natural extension to multilevel thresholding. This multilevel threshold methodology segments the image into several distinct regions which correspond to a background and several objects. Segmentation experimental results and comparison with Otsu's multilevel thresholding algorithm for the calculation of two and three thresholds are presented.