Lossless Compression of Angiogram Foreground with Visual Quality Preservation of Background
This work addresses the need for efficient medical image compression in telemedicine by focusing on angiograms, where vasculature is critical for diagnosis, but it is incremental as it builds on existing methods that compress foreground and background differently.
The paper tackled the problem of compressing angiographic images by segmenting vessels with a CNN and applying a hierarchical block processing algorithm to compress the background while preserving visual quality, achieving a high compression ratio for the background without degrading diagnostic information.
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.