Open Access

ARTICLE

A Novel Reversible Data Hiding Scheme Based on Lesion Extraction and with Contrast Enhancement for Medical Images

Xingxing Xiao¹, Yang^1,*, Rui Li², Weiming Zhang³

1 School of electronics and information engineering, Anhui university, Hefei, 230601, China.
2 School of Engineering, University of Southern California, Los Angeles, CA 90007, USA.
3 School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, China.

* Corresponding Author: Yang Yang. Email: .

Computers, Materials & Continua 2019, 60(1), 101-115. https://doi.org/10.32604/cmc.2019.05293

Abstract

The medical industry develops rapidly as science and technology advance. People benefit from medical resource sharing, but suffer from privacy leaks at the same time. In order to protect patients’ privacy and improve quality of medical images, a novel reversible data hiding (RDH) scheme based on lesion extraction and with contrast enhancement is proposed. Furthermore, the proposed scheme can enhance the contrast of medial image's lesion area directly and embed high-capacity privacy data reversibly. Different from previous segmentation methods, this scheme first adopts distance regularized level set evolution (DRLSE) to extract lesion and targets at the lesion area accurately for medical images. Secondly, the data is embedded into the lesion area by improved histogram shifting method to enhance the contrast of medial image’s lesion area. Lastly, the rest of data is embedded into the non-lesion area by the high-capacity embedding method to achieve the higher payload. At the receiving end, data can be extracted completely and images can be recovered losslessly by the third party with right. Experimental results have shown that the method of lesion extraction has an advantage over the existing segmentation methods in medical images. The image quality is improved well and the performance of contrast enhancement in the lesion area is better than other RDH methods with contrast enhancement.

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Citations