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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 8    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 8,August 2012
Medical Image Compression Using Region Growing Segmenation
Full Text(PDF, )  PP.67-71  
Author(s)
R.Arun & D.Murugan
KEYWORDS
ABSTRACT
The easy, rapid, and reliable digital transmission and storage of medical and biomedical images would be a tremendous boon to the practice of medicine. Patients in rural areas could have convenient access to second opinions. Patients readmitted to hospitals could have earlier imaging studies instantly available. Rather than waiting for others to finish with hardcopy films, medical and surgical teams collaborating on patient care could have simultaneous access to imaging studies on monitors throughout the hospital. This long-term digital archiving or rapid transmission is prohibitive without the use of image compression to reduce the file sizes. As medical/biological imaging facilities move towards complete film-less imaging, compression plays a key role. Although lossy compression techniques yield high compression rates, the medical community has been reluctant to adopt these methods, largely for legal reasons, and has instead relied on lossless compression techniques that yield low compression rates. The true goal is to maximize compression while maintaining clinical relevance and balancing legal risk. Now-a-days in medical field the digitized medical information such as computed tomography (CT), magnetic resonance imaging (MRI), generates increasingly important volumes of data is an important challenge to deal with is the storage, retrieval and transmission requirements of enormous data, from one place to another place for urgent purpose including medical images. Compression is one of the indispensable techniques to solve this problem. In this paper we offer a lossless compression method with the segmentation for compression of medical images. In this method the medical image is segmented and compressed by wavelet method to increase the compression ratio and to store in a less space. Here we use the CT and MRI images and analyzed in detail.
References
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