Improved pharma education system in the field of medical images using compression techniques

The pharmaceutical education has been gaining more and more popularity in the past one and half decades. The pharma people are focusing on Remote Education for students. The medical images and reports pertaining to each individual should be stored in the database for any future references. Medical i...

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Bibliographic Details
Published in:Cluster computing 2019-11, Vol.22 (Suppl 6), p.15049-15057
Main Authors: Reddy, M. Rajasekhar, Ravichandran, K. S., Venkatraman, B., Sekar, K. R., Manikandan, R.
Format: Article
Language:English
Subjects:
Algorithms
Computer Communication Networks
Computer Science
Discrete cosine transform
Distance learning
Education
Eigenvalues
Image compression
Image quality
Image reconstruction
Medical imaging
Methods
Operating Systems
Pharmaceuticals
Processor Architectures
Remote observing
Singular value decomposition
Size reduction
Students
Ultrasonic imaging
Wavelet transforms
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Summary:The pharmaceutical education has been gaining more and more popularity in the past one and half decades. The pharma people are focusing on Remote Education for students. The medical images and reports pertaining to each individual should be stored in the database for any future references. Medical images generally occupy substantial amount of space. In this research article, we have emphasized how information can be stored within the stipulated space and how the space occupied can be reduced. The compression techniques applied are discrete cosine transform followed by a singular value decomposition method which is again cascaded by set partitioning in hierarchical trees, which will reduce 23.67% of the size of the original image.The size reduction will help in storing more number of images in the database thereby giving the Remote Education students a chance to observe and learn. The proposed technique when compared with other existing methods gives better quality of image that is reconstructed than the other methods in terms of PSNR value.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-018-2496-1