Lossless Compression of Angiogram Foreground with Visual Quality Preservation of Background

TL;DR

This paper introduces a neural network-based method for lossless compression of angiogram images that effectively reduces background data while maintaining high visual quality of the vascular foreground crucial for diagnosis.

Contribution

It presents a novel hierarchical block processing algorithm combined with CNN-based segmentation to improve angiogram compression without quality loss.

Findings

Effective background redundancy elimination

Preserves visual quality of vascular structures

Achieves high compression ratios

Abstract

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.