Authors:
Adnan Khashman
and
Kamil Dimililer
Affiliation:
Near East University, Cyprus
Keyword(s):
X-ray medical images, Optimum image compression, Neural Network, DCT Image Compression.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Machine Perception: Vision, Speech, Other
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Medical imaging requires storage of large quantities of digitized data Efficient storage and transmission of medical images in telemedicine is of utmost importance however. Due to the constrained bandwidth and storage capacity, a medical image must be compressed before transmission or storage. An ideal image compression system must yield high quality compressed images with high compression ratio; this can be achieved using DCT-based image compression, however the contents of the image affects the choice of an optimum compression ratio. In this paper, a neural network is trained to relate the x-ray image contents to their optimum compression ratio. Once trained, the optimum DCT compression ratio of the x-ray image can be chosen upon presenting the image to the network. Experimental results suggest that out proposed system, can be efficiently used to compress x-rays while maintaining high image quality.