Chain code strategy for lossless storage and transfer of segmented binary medical data

Obtaining a 3D medical visualization is a tedious process requiring several processing steps (such as segmen-tation) and assigning various rendering parameters (such as color and opacity). Current systems use video/image exporting or snapshots to save results. Such vendor-dependent tools not only prevent the possibility of further interactions but also creates additional large-size data that is problematic to store in PACS over time and hard to transfer for teleradiology applications. To overcome, alternative strategies propose a representation of the vi-sualizations, which only store segmentation masks that contains the binary form of segmented data. Unfortu-nately, existing compression methods are limited to effectively compress the volumetric data. In this study, lossless storage of binary segmented data is effectively performed by two newly-proposed chain code approaches. Particularly-two novel contributions are presented: 1. The dictionary of normalized angle difference is improved as a new chain symbol coding procedure, namely normalized angle difference, by adding new symbols to the dictionary aiming to generate a low-entropy symbol sequence for medical volumes. 2. A new volumetric approach that utilizes 26 symbols to encode volumetric data is developed. Each slice is visited, and the contour of the segmented object is codified such that eight different vectors for each slice (pointing to one of the four faces of each voxel, plus four towards one of its edges) are obtained. The developed methods are tested on diverse volumetric segmented data and compared to existing standards. It is shown that the proposed methods outperform well-established techniques.