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Content-based medical image retrieval using fractional Hartley transform with hybrid features

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Abstract

Content-Based Medical Image Retrieval is used to extract meaningful information from a large number of medical images. To extract image texture characteristics, a novel medical image retrieval technique is presented. Images from Computed Tomography scans are used as input images. At first, the respective image is affected by some noise disturbance. To mitigate this and to improve the contrast of the pixels in the source image, and then a fractional Hartley transform is applied to eradicate the noise variation as well as reduce the image distortion. About this, a better-filtered output is determined. Then the hybrid feature extraction technique is utilized to extract the desirable features. After this Modified Weight-Brownian Motion Monarch Butterfly Optimization approach is exploited to alleviate the unwanted features from the huge amount of features and select certain desirable features. Finally, the similarity between the selected features is measured to detect and classify the medical images. The proposed technique has been more advantageous to increase the accuracy level as well as gradually minimizing the error rate. The proposed algorithms are implemented using the image processing toolbox in MATLAB 2019 platform. The performances are analyzed by the proposed method in terms of precision (99%), recall (83%), F-measure (90%), and accuracy (98.85%).

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Acknowledgements

First of all, the writers are thankful for the continuing encouragement and support from the management of the Udaya School of Engineering for their study. The writers are also noting the vital role of the National Cancer Institute Kanyakumari and the Foundation for the National Institutes of Health for their critical role in the creation of the In-house clinical database & free, public available database used in this study. Finally, we want to thank the anonymous reviewers for their help with this article's improvement.

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Authors and Affiliations

  1. Department of ECE, Udaya School of Engineering, Nagercoil, Tamil Nadu, India

    K. Vijila Rani

  2. Department of Physics, S.T. Hindu College, Nagercoil, Tamil Nadu, India

    M. Eugine Prince

  3. Department of EEE, Noorul Islam Center for Higher Education, Kumaracoil, Tamil Nadu, India

    P. Sujatha Therese

  4. Department of Artificial Intelligence and Data Science, R.M.K College of Engineering and Technology, Chennai, Tamil Nadu, India

    P. Josephin Shermila

  5. Department of ECE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

    E. Anna Devi

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Contributions

K.VIJILA RANI1: Roles: Conceptualization, Methodology, Validation, Visualization, Writing – original draft, Writing-Reviewer Comments Correction.

M.EUGINE PRINCE2: Roles: Visualization, Data Correction, Resources, and Validation

P.SUJATHA THERESE 3: Roles: Software Selection, Validation, and Visualization

P.JOSEPHIN SHERMILA4: Roles: Writing – original draft & Reviewer Comments Correction and Editing

E. ANNA DEVI5: Roles: Proof reading and Visualization

Corresponding author

Correspondence to K. Vijila Rani.

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This study has not been supported by any industrial company and does not serve to promote any commercial product. Anonymized publicly available databases were used in the conducted experiments.

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Rani, K.V., Prince, M.E., Therese, P.S. et al. Content-based medical image retrieval using fractional Hartley transform with hybrid features. Multimed Tools Appl 83, 27217–27242 (2024). https://doi.org/10.1007/s11042-023-16462-y

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