Biomedical Engineering Reference
In-Depth Information
Introduction
The increasing adoption of information systems in health care has led to a scenario
where patient information security is more and more being regarded as a critical
issue. Allowing patient information to be in jeopardy may lead to irreparable
damage, physically, morally, and socially to the patient, potentially shaking the
credibility of the health care institution. This demands adoption of security
mechanisms to assure information integrity and authenticity. Structured descrip-
tions attached to medical image series conforming to the digital imaging and
communications in medicine (DICOM) standard make possible to fit the collec-
tions of existing digitized images into an educational and research framework.
Our aim is to design an application of Wavelet transform in Edge detection,
segmentation, image registration, denoising, providing lossless compression of
DICOM images by applying Daubechie's wavelet, and run length encoding. This
helps in better bandwidth utilization of the networks and at the same time, also
aims at providing the security mechanism for DICOM images by removing the
texual elements of the medical image.
Wavelet Edge Detection and Segmentation
Edge detection plays an important role in image segmentation. In many cases,
boundary delineation is the ultimate goal for an image segmentation and a good
edge detector itself can then fulfill the requirement of segmentation [
1
]. On the
other hand, many segmentation techniques require an estimation of object edges
for their initialization.
Most multiscale edge detectors smooth the input signal at various scales and
detect sharp variation locations (edges) from their first or second derivatives. Edge
locations are related to the extrema of the first derivative of the signal and the zero
crossings of the second derivative of the signal.
It was also pointed out that first-derivative wavelet functions are more appro-
priate for edge detection since the magnitude of wavelet modulus represents the
relative ''strength'' of the edges, and therefore enables to differentiate meaningful
edges from small fluctuations caused by noise (Fig.
1
).
Wavelet Image Registration
Another very important application of wavelets in image processing: image reg-
istration. Image registration is required for many image processing applications. In
medical imaging, co-registration problems are important for many clinical tasks:
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