Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230700124-7.doi: 10.11896/jsjkx.230700124

• Image Processing & Multimedia Technolog • Previous Articles     Next Articles

Medical Image Reversible Contrast Enhancement Based on Adaptive Histogram Equalization

TAN Peng, OU Bo   

  1. College of Computer Science and Electronic Engineering,Hunan University,Changsha 410082,China
  • Published:2024-06-06
  • About author:TAN Peng,born in 1999,postgraduate.Her main research interests include data hiding and its related topics and so on.
    OU Bo,born in 1985,associate professor,Ph.D supervisor.His main research interests include reversible data hiding and its related topics and so on.
  • Supported by:
    National Natural Science Foundation of China(61872128).

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Abstract: At present,some reversible data hiding algorithms usually conduct the histogram-equalization like data hiding to achieve the contrast enhancement effect for the image.The advantage is that the algorithm is easy to design and conduct.How-ever,it lacks of the optimization objective function,and cannot determine the suitable parameters to optimize the reversible contrast enhancement.As a result,it may suffer the problems of insufficient or excessive enhancement,etc.In order to improve the reversible contrast enhancement effect after data embedding,this paper proposes a reversible data hiding algorithm for medical image combined with contrast enhancement based on adaptive histogram equalization.In the proposed method,the reversible data embedding is implemented by using prediction-error expansion.The objective function of adaptive histogram equalization is designed to optimize the prediction-error histogram modification and determine the optimal embedding positions,by considering the low-distortion embedding and the better contrast enhancement.Experimental results show that compared with other methods,the proposed method can further achieve contrast enhancement effect after reversible data embedding,and therefore improve the target identification of medical image.

Key words: Medical image processing, Reversible contrast enhancement, Adaptive histogram equalization, Prediction-error expansion

CLC Number: 

  • TP309.7
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