Reversible medical image watermarking based on wavelet histogram shifting Source: Authors: Reporter: Date: The Imaging Science Journal, Vol. 59, No. 1, pp.49-59, 2011 H Golpira and H Danyali Wan-Yu Lu, Jiun-Lwen Liang 2012/12/20

2 Outline Introduction The proposed watermarking system – Insertion algorithm – Extraction algorithm Experimental results Conclusions

3 Introduction The protection of the digital images becomes more and more important because they can be easily copied and modified. Digital watermarking techniques are required in the transfer procedure to ensure copyright protection. The image watermarking schemes can be generally classified into two categories: - Visible watermarking - Imperceptible watermarking

4 Introduction Copyright watermark image Patient’s information Private key Embedded

5 Introduction (a) spatial domain(b) transformed domain

6 The proposed watermarking system

7 A typical decomposed medical image for L=1. (a) a medical host image (b) decomposed image by applying one level of IDWT (c) frequency sub-bands of (b)

8 The proposed watermarking system Typical histogram of high frequency sub-bands of a medical grey scale image Step 1: Apply L levels of a 2D IDWT to the host image to obtain the decomposed one.

9 Step 2: Find T 1 and T 2 on the high frequency sub-bands. Assume an M-bit watermark pattern, a horizontal line at M/2 are drawn to find the proper thresholds. The left side (beginning part) of the histogram, called T 1. The right side (end part) of the histogram, called T 2. The proposed watermarking system

10 Step 3: Create the zero-points ( Z 1 and Z 2 ) (a) Shift the left side of T 1 to left by one unit to create Z 1. (b) Shift the right side of T 2 to right by one unit to create Z 2 The proposed watermarking system

11 Step 4: Create lower resolutions of the copyright protection binary logo watermark. Step 5: Encryption of the watermark, using a private key. - total size of the watermark is M+N bits. (a) M bits copyright watermark. (b) N bits patient personal and examination data. The proposed watermarking system

12 Step 6: Insert the watermark (W) - Divide the watermark into two parts (W 1 and W 2 ). - Insert bit 0 and bit 1 of W 1 at T 1 and Z 1 respectively. - Insert bit 0 and bit 1 of W 2 at T 2 and Z 2 respectively. Step 7: Apply L levels of 2D IIDWT to obtain the watermarked image in spatial domain. The proposed watermarking system

13 Insertion algorithm(1/4) Insertion algorithm (1/4) EX: W 1 = T 1 = -1 Z 1 = -2 Shifted to left If C i < T 1

14 Insertion algorithm(2/4) Insertion algorithm (2/4) Embedded W 1 Used C i = T 1 EX: W 1 = T 1 = -1 Z 1 = -2 C i = C i – W 1i -1= =-1-1

15 Insertion algorithm(3/4) Insertion algorithm (3/4) EX: W 2 = T 2 = 4 Z 2 = 5 Shifted to right If C j > T

16 Insertion algorithm(4/4) Insertion algorithm (4/4) Embedded W 2 Used C j = T 2 C j = C j + W 2j 4 = = EX: W 2 = T 2 = 4 Z 2 =

Extraction algorithm(1/2) Extraction algorithm (1/2) Extracted W 1 = → 0 -2 → 1 EX: T 1 = -1 ; Z 1 = -2 Shifted to right If C i < T 1

Extraction algorithm(2/2) Extraction algorithm (2/2) Extracted W 2 = → 0 5 → 1 Shifted to left If C j > T 2 EX: T 2 = 4 ; Z 2 = 5

19 Experimental results (1/8) (a) MR_liver_t1 (b) MR_ped_chest (c) MR_sag_head (a)MR_liver_t1 (b)MR_ped_chest (c)MR_sag_head A slice of the medical images used for test:

20 Experimental results (2/8) Scenario A The scenario A is dedicated to demonstrate the superiority of the method for the low payloads.

21 Experimental results (3/8) Scenario A

22 Experimental results (4/8) Scenario A 結構相似性影像質量指標 (mean structural similarity index measure, MSSIM) 正好察覺失真度 (just-noticeable distortion, JND)

23 Experimental results (5/8) Scenario A

24 Experimental results (6/8) Scenario B In this scenario, superiority of the proposed method for embedding high amount of data in the host image is demonstrated. (a) a medical host image (b) decomposed image by applying two level of IDWT (c) frequency sub-bands of (b)

25 Experimental results (7/8) Scenario B

26 Experimental results (8/8) Scenario B (a)Host image (b)Watermarked image (PSNR=49.54dB)

27 Conclusions Scalability and security. To solve underflow and overflow. The watermarked images have high quality. The watermark extraction process is blind and both watermark and host image is completely reconstruct without any loss.

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