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The use of contextual spatial knowledge for low-quality image segmentation

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Abstract

In this paper, a novel possibilistic approach for representing pixelic spatial knowledge is proposed to be used in classification; more specifically in segmentation of low quality images. This approach uses the spatial contextual information at the pixel level in order to produce a local possibility distribution. The similarity between this local possibility distribution representing the contextual pixelic information and the possibility distribution for each of the predetermined thematic classes is measured. This measure is used to assign one of these thematic classes to the pixel. In order to show the potential of the proposed possibilistic approach, synthetic and real images (Melanoma) are classified using the possibilistic similarity. The performance is compared with four relevant classic methods and one recent theory-like method (fuzzy c means). Our context-based possibilistic representation approach outperforms the other methods, in terms of classification recognition rate as well as in stability or robustness behavior when compared to those methods.

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Acknowledgements

Imène Khanfir Kallel would like to address a deep thank to Professeor Eloi Bossé of McMaster University of Hamilton (Canada), for his scientific advices and support to technical content editing. Authors thank Professor Denis de BRUCQ of the Perception-System-Information (PSI) of the University of Rouen and the dermatological clinic of University Hospital of Rouen (France), for having provided a database of classified skin lesion images used in the present work. Authors acknowledge the technical assistance of Dr. Héla Fourati Mseddi, working in Medical Imagery Service of CHU Hédi Chaker Sfax, (Tunisia), for providing ground truth of the upcited images.

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Authors and Affiliations

  1. CEM Lab, ENIS, Université de Sfax, BP213, Sfax Eljadida, 3027, Sfax, Tunisia

    Imene Khanfir Kallel

  2. (iTi), IMT-Atlantique, Technopôle Brest, Plouzané, France

    Imene Khanfir Kallel, Shaban Almouahed, Bassem Alsahwa & Basel Solaiman

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  1. Imene Khanfir Kallel
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  2. Shaban Almouahed
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  3. Bassem Alsahwa
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  4. Basel Solaiman
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Correspondence to Imene Khanfir Kallel.

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Kallel, I.K., Almouahed, S., Alsahwa, B. et al. The use of contextual spatial knowledge for low-quality image segmentation. Multimed Tools Appl 78, 9645–9665 (2019). https://doi.org/10.1007/s11042-018-6540-1

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  • DOI: https://doi.org/10.1007/s11042-018-6540-1

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