Biomedical Engineering Reference
In-Depth Information
info
:
SeriesInstanceUID
ans
¼
1
:
2
:
840
:
113619
:
2
:
1
:
2411
:
1031152382
:
365
:
736169244
Step 3: Remove all the text from the image (Enhancing Security)—Finds the
maximum and minimum values of all pixels in the image by using max
and min functions. The pixels that form the white text characters are set to
the maximum pixel value. The output screen appears as: (Fig.
10
).
Step 4: Generate a new DICOM UID using the dicomuid function—Create
UID as variable to store the value of new DICOM UID
uid
¼
1
:
3
:
6
:
1
:
4
:
1
:
9590
:
100
:
1
:
1
:
39331690911270240817931391323566307755
Step 5: Set the value of the SeriesInstanceUID field in the metadata associ-
ated with the original DICOM file to the generated value
Step 6: Write the modified image to a new DICOM file, specifying the mod-
ified metadata structure, info, as an argument
Step 7: Apply wavelet-based lossless compression technique—Apply wavelet
transform and run length encoding. Output appears as: (Fig.
11
)
Discussion:
On the basis of the parameters of image, the following calculations and results
are shown: (Table
1
) (Fig.
12
).
Results
In this chapter, various techniques for wavelet transforms are developed with run
length coding algorithm; wavelet transform makes it attractive both in terms of
speed and memory needs and also enhances security features. It is found that the
proposed method gives more than 34 % average improvement in the PSNR value
in the bpp range of 0.0625-1.00 and high reduction in mean square error with a
better quality of the reconstructed medical image judged on the basis of the human
visual system (HVS).
Hence, finally, we can conclude that the proposed wavelet-based method is very
suitable for low bit rate compression, high compression ratios, can perform loss-
less coding, high PSNR, low MSEs, as well as good visual quality of the recon-
structed medical image at low bit rates. It can also maintain the high diagnostic
quality of the compressed image and hence can reduce heavily the transmission
and storage costs of the huge medical data generated everyday. Wavelet can also
be used in Image registration, edge detection and segmentation, and denoising.
Acknowledgments I sincerely express indebtedness to my esteemed and revered guide
Prof. Mukta Bhatele for her invaluable guidance, supervision, and encouragement throughout the
work. I also thank MP, CT scan, and MRI Center, Jabalpur, for its contribution to the collection
of images used. This study would not have been possible if compression researchers did not
routinely place their code and papers on the Internet for public access.
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