Modern imaging modalities generate large volumes of medical data that place a heavy
burden on both storage and transmission. Consequently, image data compression is a key …

In compression of medical image using convolutional neural network trained with the back-
propagation algorithm and lefted wavelet transformation is proposed to compress high …

Over the preceding decade, there has been a discernible surge in the prominence of
artificial intelligence, marked by the development of various methodologies, among which …

Data compression forms a central role in handling the bottleneck of data storage,
transmission and processing. Lossless compression requires reducing the file size whilst …

In the field of medicine, the rapid advancement of medical technology has significantly
increased the speed of medical image generation, compelling us to seek efficient methods …

In the advent of a digital health revolution, vast amounts of clinical data are being generated,
stored and processed on a daily basis. This has made the storage and retrieval of large …

When we compress a large amount of data, we face the problem of the time it takes to
compress it. Moreover, we cannot predict how effective the compression performance will …

Image compression gains prominence in picture archiving and communication systems for
storage and transmission of data. The advancements in technology enable the usage of …

A neural network architecture is proposed and shown to be very effective in performing lossy
compression of medical images. A novel ROI-JPEG technique is introduced as the coding …

The annual volume of imaging data in modern paperless hospitals can approach up to 10
terabytes, heavily pressing the storage and transmission requirements (Choongetal., 2007) …