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Multilevel segmentation of medical images in the framework of quantum and classical techniques

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Abstract

Nowadays, the numerical segmentation is an important step in the processing and interpretation of medical images. The segmentation consists in extracting, from the image, one or more objects forming the regions of interest. Image thresholding is one of the simplest and effective techniques of image segmentation. In this work, we propose and compare multilevel segmentation approaches based on classical and quantum techniques. The Classical Rényi (CR) and the Quantum Rényi (QR) entropies are used to quantify the information contained in the image. Within the quantum framework, the digital image is expressed as a quantum system by means of the Flexible Representation of Quantum Images (FRQI). Generally, the multilevel thresholding formulation leads to a complex optimization problem. The Classical Genetic Algorithm (CGA) and the Quantum Genetic Algorithm (QGA) are employed to efficiently determine the optimal thresholding values by maximizing the entropy-based fitness functions. The segmentation performances of the proposed methods are assessed and compared using some prevailing criteria. The achieved results on a sample of medical images demonstrated that the QGA-QR method outperforms significantly the other combinations for this thresholding exercise.

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Acknowledgements

This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant No. (D-276-612-1440). The authors, therefore, gratefully acknowledge the DSR technical and financial support.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Amani Tariq Jamal

  2. Department of Mathematics, Faculty of Sciences, Taif University, Taif, 11099, Saudi Arabia

    Sayed Abdel-Khalek

  3. Department of Mathematics, Faculty of Science, Sohag University, Sohag, Egypt

    Sayed Abdel-Khalek

  4. Department of Quantitative Methods, Higher Institute of Management University of Tunis, Tunis, Tunisia

    Anis Ben Ishak

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  1. Amani Tariq Jamal
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  2. Sayed Abdel-Khalek
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  3. Anis Ben Ishak
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Correspondence to Sayed Abdel-Khalek.

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Tariq Jamal, A., Abdel-Khalek, S. & Ben Ishak, A. Multilevel segmentation of medical images in the framework of quantum and classical techniques. Multimed Tools Appl 82, 13167–13180 (2023). https://doi.org/10.1007/s11042-020-10235-7

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  • DOI: https://doi.org/10.1007/s11042-020-10235-7

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