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Simultaneous storage of medical images in the spatial and frequency domain: a comparative study.

Nayak J, Bhat PS, Acharya U R, Uc N - Biomed Eng Online (2004)

Bottom Line: It can be seen from results, the process does not affect the picture quality.Spatial and DFT domain interleaving gave very less %NRMSE as compared to DCT and DWT domain.Among the frequency domain interleaving methods, DFT was found to be very efficient.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of E & C Engg, Manipal Institute Of Technology, Manipal, India 576104. jag.nayak@mit.manapal.edu

ABSTRACT

Background: Digital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images, to reduce storage and transmission overheads.

Methods: The patient information is encrypted before interleaving with images to ensure greater security. The bio-signals are compressed and subsequently interleaved with the image. This interleaving is carried out in the spatial domain and Frequency domain. The performance of interleaving in the spatial, Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) coefficients is studied. Differential pulse code modulation (DPCM) is employed for data compression as well as encryption and results are tabulated for a specific example.

Results: It can be seen from results, the process does not affect the picture quality. This is attributed to the fact that the change in LSB of a pixel changes its brightness by 1 part in 256. Spatial and DFT domain interleaving gave very less %NRMSE as compared to DCT and DWT domain.

Conclusion: The Results show that spatial domain the interleaving, the %NRMSE was less than 0.25% for 8-bit encoded pixel intensity. Among the frequency domain interleaving methods, DFT was found to be very efficient.

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Scheme for Interleaving in DWT Domain
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Figure 6: Scheme for Interleaving in DWT Domain

Mentions: Wavelets are the functions defined over a finite interval and having average value zero. The basic idea of the Wavelet transform is to represent any arbitrary function f(t) as a superposition of a set of such wavelets or basis functions. These basis functions are obtained from a single prototype Wavelet called the mother Wavelet, by dilations or contractions (scaling) and translations (shifts). Besides the usage of DCT in JPEG, a new compression technique called JPEG2000 uses Discrete Wavelet Transform. The blocking artifacts of DCT in JPEG are noticeable and annoying. In Wavelet based compression we get higher compression and blocking artifacts are avoided. The Wavelet transform can be implemented using filter banks [Mallat, 1998]. The signal is decomposed into various subbands octave-band decomposition is most widely used. Figure 6 shows the scheme used for DWT domain interleaving process. The Figure 7. shows three level octave band decomposition. The DWT gives three parts of multiresolution representation and one part of multiresolution approximation [Mallat, 1998]. It is similar to hierarchical subband system, where subbands are logarithmically spaced in frequency. The subbands labeled LH1, HL1, HH1 of multiresolution representation represent the finest scale Wavelet coefficients. To obtain next coarser scale of the Wavelet coefficients, the subband LL1 i.e. multiresolution approximation is further decomposed and critically subsampled. We perform three level decomposition of the image and embed the text/Graphic file information into High frequency region band respectively (Starting from the 32nd coefficient to 64th coefficient). The text and graphic file can be extracted from the DWT coefficients before inverse quantization, inverse zigzag coding and taking inverse discrete Wavelet transform and to recover the original image.


Simultaneous storage of medical images in the spatial and frequency domain: a comparative study.

Nayak J, Bhat PS, Acharya U R, Uc N - Biomed Eng Online (2004)

Scheme for Interleaving in DWT Domain
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC425593&req=5

Figure 6: Scheme for Interleaving in DWT Domain
Mentions: Wavelets are the functions defined over a finite interval and having average value zero. The basic idea of the Wavelet transform is to represent any arbitrary function f(t) as a superposition of a set of such wavelets or basis functions. These basis functions are obtained from a single prototype Wavelet called the mother Wavelet, by dilations or contractions (scaling) and translations (shifts). Besides the usage of DCT in JPEG, a new compression technique called JPEG2000 uses Discrete Wavelet Transform. The blocking artifacts of DCT in JPEG are noticeable and annoying. In Wavelet based compression we get higher compression and blocking artifacts are avoided. The Wavelet transform can be implemented using filter banks [Mallat, 1998]. The signal is decomposed into various subbands octave-band decomposition is most widely used. Figure 6 shows the scheme used for DWT domain interleaving process. The Figure 7. shows three level octave band decomposition. The DWT gives three parts of multiresolution representation and one part of multiresolution approximation [Mallat, 1998]. It is similar to hierarchical subband system, where subbands are logarithmically spaced in frequency. The subbands labeled LH1, HL1, HH1 of multiresolution representation represent the finest scale Wavelet coefficients. To obtain next coarser scale of the Wavelet coefficients, the subband LL1 i.e. multiresolution approximation is further decomposed and critically subsampled. We perform three level decomposition of the image and embed the text/Graphic file information into High frequency region band respectively (Starting from the 32nd coefficient to 64th coefficient). The text and graphic file can be extracted from the DWT coefficients before inverse quantization, inverse zigzag coding and taking inverse discrete Wavelet transform and to recover the original image.

Bottom Line: It can be seen from results, the process does not affect the picture quality.Spatial and DFT domain interleaving gave very less %NRMSE as compared to DCT and DWT domain.Among the frequency domain interleaving methods, DFT was found to be very efficient.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of E & C Engg, Manipal Institute Of Technology, Manipal, India 576104. jag.nayak@mit.manapal.edu

ABSTRACT

Background: Digital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images, to reduce storage and transmission overheads.

Methods: The patient information is encrypted before interleaving with images to ensure greater security. The bio-signals are compressed and subsequently interleaved with the image. This interleaving is carried out in the spatial domain and Frequency domain. The performance of interleaving in the spatial, Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) coefficients is studied. Differential pulse code modulation (DPCM) is employed for data compression as well as encryption and results are tabulated for a specific example.

Results: It can be seen from results, the process does not affect the picture quality. This is attributed to the fact that the change in LSB of a pixel changes its brightness by 1 part in 256. Spatial and DFT domain interleaving gave very less %NRMSE as compared to DCT and DWT domain.

Conclusion: The Results show that spatial domain the interleaving, the %NRMSE was less than 0.25% for 8-bit encoded pixel intensity. Among the frequency domain interleaving methods, DFT was found to be very efficient.

Show MeSH
Related in: MedlinePlus