X-RAY IMAGE COMPRESSION USING NEURAL VETWORKS
Kuther Abood, Haydar Aboud, Falih Hassan Awaid · 2012
Neural Networks are based on the parallel architecture and are inspired from human brains. Neural networks are a form of multiprocessor computer system, with simple processing elements, a high degree of interconnection, simple scalar messages and adaptive interaction between elements. One such application is image compression. Image compression is a process which minimizes the size of an image file without degrading the quality of the image to an unacceptable level. It also reduces the time required for images to be sent over the internet or downloaded from web pages. Efficient storage and transmission of medical images in telemedicine is of utmost importance however, this efficiency can be hindered due to storage capacity and constraints on bandwidth. Thus, a medical image may require compression before transmission or storage. Ideal image compression systems must yield high quality compressed images with high compression ratio; this can be achieved using wavelet transform based compression, however, the choice of an optimum compression ratio is difficult as it varies depending on the content of the image. In this paper, a neural network is trained to relate radiograph image contents to their optimum image compression ratio. Once trained, the neural network chooses the best wavelet compression ratio of the x-ray images upon their presentation to the network. Experimental results suggest that our proposed system can be efficiently used to compress radiographs while maintaining high image quality.