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RDH-based dynamic weighted histogram equalization using for secure transmission and cancer prediction

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Abstract

Image contrast enhancement is a prerequisite and plays a very important role in many image processing field like medical imaging, face recognition, computer-vision, and satellite imaging. In this paper we proposed reversible data hiding based Limited Dynamic Weighted Histogram Equalization techniques for Abnormal Tumor regions which improve the contrast, transmit the hidden secret information, preserve its brightness intensity and original appearance of the image. We have implemented Otsu’s method to segment the input image into two sub-histogram regions of interest (ROI) and non-region of interest; furthermore, the sub-histograms ROI region equalized independently without of over-enhancement and any loss of hidden and diagnostic data. Our proposed method is more efficient to precisely preserve the brightness of the image and extract the secret information with contrast image reversibly; besides, different classifiers are used to classify the brain cancer to check the performance of our proposed method.

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Funding

This work was supported by the Sichuan Science and Technology Program under Grant 2018RZ0070.

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Authors and Affiliations

  1. School of Information and Communication Engineering, University of Electronics Science and Technology of China, Chengdu, China

    Rashid Abbasi, Jianwen Chen & Weiwei Cui

  2. Department of Information Systems, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Yasser Al-Otaibi

  3. Artificial Intelligence and Data Analytics Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia

    Amjad Rehman

  4. Faculty of Information Technology, University of Central Punjab Lahore, Lahore, Pakistan

    Asad Abbas

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  1. Rashid Abbasi
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  2. Jianwen Chen
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  3. Yasser Al-Otaibi
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Correspondence to Rashid Abbasi.

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Communicated by Y. Zhang.

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Abbasi, R., Chen, J., Al-Otaibi, Y. et al. RDH-based dynamic weighted histogram equalization using for secure transmission and cancer prediction. Multimedia Systems 27, 177–189 (2021). https://doi.org/10.1007/s00530-020-00718-w

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