  By increasing the volume of telemedicine information, the need for medical
image compression has become more important. In angiographic images, a small
ratio of the entire image usually belongs to the vasculature that provides
crucial information for diagnosis. Other parts of the image are diagnostically
less important and can be compressed with higher compression ratio. However,
the quality of those parts affect the visual perception of the image as well.
Existing methods compress foreground and background of angiographic images
using different techniques. In this paper we first utilize convolutional neural
network to segment vessels and then represent a hierarchical block processing
algorithm capable of both eliminating the background redundancies and
preserving the overall visual quality of angiograms.
