AUTOMATED MEDICAL RADIOGRAPH COMPRESSION &DECOMPRESSION

ABSTRACT

Recently, teleradiology, which is one of the most used clinical aspects of telemedicine, has received much attention. Teleradiology attempts to transfer medical images of various modalities, like computerized tomography (CT) scans, magnetic imaging (MRI), ultrasonography (US), and x-rays from one location to another such as in hospitals, imaging centers or a physician’s desk.

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. There is a general preference to use wavelet transforms in image compression because the compressed images and be obtained with higher compression ratios and higher PSNR values.

However, the choice of an optimum compression ratio is difficult as it varies depending on the content of the image. In a Principal Component Analysis based neural network was used for image compression. In a neural network quantizer was used to yield a high compression ratio while maintaining high quality images. The proposed method suggests that a trained neural network can learn the non-linear relationship between the intensity (pixel values) of a radiograph, or x-ray, image and its optimum compression ratio.

Once the highest compression ratio is obtained, while maintaining good image quality, the result reduction in radiograph image size, should make the storage and transmission of radiographs more efficient.